Fine-tuning pre-trained vision-language models (VLMs), e.g., CLIP, for the open-world generalizat... more Fine-tuning pre-trained vision-language models (VLMs), e.g., CLIP, for the open-world generalization has gained increasing popularity due to its practical value. However, performance advancements are limited when relying solely on intricate algorithmic designs for a single model, even one exhibiting strong performance, e.g., CLIP-ViT-B/16. This paper, for the first time, explores the collaborative potential of leveraging much weaker VLMs to enhance the generalization of a robust single model. The affirmative findings motivate us to address the generalization problem from a novel perspective, i.e., ensemble of pretrained VLMs. We introduce three customized ensemble strategies, each tailored to one specific scenario. Firstly, we introduce the zero-shot ensemble, automatically adjusting the logits of different models based on their confidence when only pre-trained VLMs are available. Furthermore, for scenarios with extra few-shot samples, we propose the training-free and tuning ensemble, offering flexibility based on the availability of computing resources. The code is available at https://github.com/zhiheLu/Ensemble VLM.git.
Adapter-style efficient transfer learning (ETL) has shown excellent performance in the tuning of ... more Adapter-style efficient transfer learning (ETL) has shown excellent performance in the tuning of vision-language models (VLMs) under the low-data regime, where only a few additional parameters are introduced to excavate the task-specific knowledge based on the general and powerful representation of VLMs. However, most adapter-style works face two limitations: (i) modeling task-specific knowledge with a single modality only; and (ii) overlooking the exploitation of the interclass relationships in downstream tasks, thereby leading to sub-optimal solutions. To mitigate that, we propose an effective adapter-style tuning strategy, dubbed GraphAdapter, which performs the textual adapter by explicitly modeling the dualmodality structure knowledge (i.e., the correlation of different semantics/classes in textual and visual modalities) with a dual knowledge graph. In particular, the dual knowledge graph is established with two sub-graphs, i.e., a textual knowledge sub-graph, and a visual knowledge sub-graph, where the nodes and edges represent the semantics/classes and their correlations in two modalities, respectively. This enables the textual feature of each prompt to leverage the task-specific structure knowledge from both textual and visual modalities, yielding a more effective classifier for downstream tasks. Extensive experimental results on 11 benchmark datasets reveal that our GraphAdapter significantly outperforms previous adapterbased methods. The code will be released at https://github.com/lixinustc/ GraphAdapter
Facial expression synthesis with various intensities is a challenging synthesis task due to large... more Facial expression synthesis with various intensities is a challenging synthesis task due to large identity appearance variations and a paucity of efficient means for intensity measurement. This paper advances the expression synthesis domain by the introduction of a Couple-Agent Face Parsing based Generative Adversarial Network (CAFP-GAN) that unites the knowledge of facial semantic regions and controllable expression signals. Specially, we employ a face parsing map as a controllable condition to guide facial texture generation with a special expression, which can provide a semantic representation of every pixel of facial regions. Our method consists of two sub-networks: face parsing prediction network (FPPN) uses controllable labels (expression and intensity) to generate a face parsing map transformation that corresponds to the labels from the input neutral face, and facial expression synthesis network (FESN) makes the pretrained FPPN as a part of it to provide the face parsing map as a guidance for expression synthesis. To enhance the reality of results, couple-agent discriminators are served to distinguish fake-real pairs in both two sub-nets. Moreover, we only need the neutral face and the labels to synthesize the unknown expression with different intensities. Experimental results on three popular facial expression databases show that our method has the compelling ability on continuous expression synthesis.
Face synthesis has been a fascinating yet challenging problem in computer vision and machine lear... more Face synthesis has been a fascinating yet challenging problem in computer vision and machine learning. Its main research effort is to design algorithms to generate photo-realistic face images via given semantic domain. It has been a crucial prepossessing step of mainstream face recognition approaches and an excellent test of AI ability to use complicated probability distributions. In this paper, we provide a comprehensive review of typical face synthesis works that involve traditional methods as well as advanced deep learning approaches. Particularly, Generative Adversarial Net (GAN) is highlighted to generate photo-realistic and identity preserving results. Furthermore, the public available databases and evaluation metrics are introduced in details. We end the review with discussing unsolved difficulties and promising directions for future research.
Large-scale vision-language models (VLMs) pre-trained on billion-level data have learned general ... more Large-scale vision-language models (VLMs) pre-trained on billion-level data have learned general visual representations and broad visual concepts. In principle, the welllearned knowledge structure of the VLMs should be inherited appropriately when being transferred to downstream tasks with limited data. However, most existing efficient transfer learning (ETL) approaches for VLMs either damage or are excessively biased towards the prior knowledge, e.g., prompt tuning (PT) discards the pre-trained text-based classifier and builds a new one while adapter-style tuning (AT) fully relies on the pre-trained features. To address this, we propose a new efficient tuning approach for VLMs named Task Residual Tuning (TaskRes), which performs directly on the textbased classifier and explicitly decouples the prior knowledge of the pre-trained models and new knowledge regarding a target task. Specifically, TaskRes keeps the original classifier weights from the VLMs frozen and obtains a new classifier for the target task by tuning a set of prior-independent parameters as a residual to the original one, which enables reliable prior knowledge preservation and flexible task-specific knowledge exploration. The proposed TaskRes is simple yet effective, which significantly outperforms previous ETL methods (e.g., PT and AT) on 11 benchmark datasets while requiring minimal effort for the implementation. Our code is available at https://github.com/geekyutao/TaskRes.
Generalized Few-shot Semantic Segmentation (GFSS) aims to segment each image pixel into either ba... more Generalized Few-shot Semantic Segmentation (GFSS) aims to segment each image pixel into either base classes with abundant training examples or novel classes with only a handful of (e.g., 1-5) training images per class. Compared to the widely studied Few-shot Semantic Segmentation (FSS), which is limited to segmenting novel classes only, GFSS is much under-studied despite being more practical. Existing approach to GFSS is based on classifier parameter fusion whereby a newly trained novel class classifier and a pre-trained base class classifier are combined to form a new classifier. As the training data is dominated by base classes, this approach is inevitably biased towards the base classes. In this work, we propose a novel Prediction Calibration Network (PCN) to address this problem. Instead of fusing the classifier parameters, we fuse the scores produced separately by the base and novel classifiers. To ensure that the fused scores are not biased to either the base or novel classes, a new Transformer-based calibration module is introduced. It is known that the lower-level features are useful of detecting edge information in an input image than higherlevel features. Thus, we build a cross-attention module that guides the classifier's final prediction using the fused multi-level features. However, transformers are computationally demanding. Crucially, to make the proposed cross-attention module training tractable at the pixel level, this module is designed based on feature-score cross-covariance and episodically trained to be generalizable at inference time. Extensive experiments on PASCAL-5 i and COCO-20 i show that our PCN outperforms the state-the-the-art alternatives by large margins.
The goal of image restoration (IR), a fundamental issue in computer vision, is to restore a high-... more The goal of image restoration (IR), a fundamental issue in computer vision, is to restore a high-quality (HQ) image from its degraded low-quality (LQ) observation. Multiple HQ solutions may correspond to an LQ input in this poorly posed problem, creating an ambiguous solution space. This motivates the investigation and incorporation of prior knowledge in order to effectively constrain the solution space and enhance the quality of the restored images. In spite of the pervasive use of hand-crafted and learned priors in IR, limited attention has been paid to the incorporation of knowledge from large-scale foundation models. In this paper, we for the first time leverage the prior knowledge of the state-of-the-art segment anything model (SAM) [55] to boost the performance of existing IR networks in an parameter-efficient tuning manner. In particular, the choice of SAM is based on its robustness to image degradations, such that HQ semantic masks can be extracted from it. In order to leverage semantic priors and enhance restoration quality, we propose a lightweight SAM prior tuning (SPT) unit. This plug-and-play component allows us to effectively integrate semantic priors into existing IR networks, resulting in significant improvements in restoration quality. As the only trainable module in our method, the SPT unit has the potential to improve both efficiency and scalability. We demonstrate the effectiveness of the proposed method in enhancing a variety of methods across multiple tasks, such as image super-resolution and color image denoising.
A rapid, sensitive and high-throughput liquid chromatography-tandem mass spectrometry (LC-MS-MS) ... more A rapid, sensitive and high-throughput liquid chromatography-tandem mass spectrometry (LC-MS-MS) method was established and validated to assay the concentration of nardosinone, a main active compound isolated from Nardostachys chinensis, in rat plasma. Plasma samples were processed by protein precipitation with acetonitrile and separated on a Venusil MP-C 18 column (50 × 2.1 mm, 5 µm) at an isocratic flow rate of 0.6 mL/min using methanol-0.1% formic acid in water (55 : 45, v/v) as mobile phase, and total run time was 2.5 min. MS-MS detection was accomplished in selected reaction monitoring mode with positive electrospray ionization. The calibration curve was linear over the concentration range of 9.60-320 ng/mL with lower limit of quantification of 9.60 ng/mL. The intra-and inter-day precisions were below 12.3% in terms of relative standard deviation, and the accuracy was within ±9.0% in terms of relative error. Extraction recovery, matrix effect and stability were also satisfactory in rat plasma. The developed method was successfully applied to a pharmacokinetic study of nardosinone following an intravenous injection at a dose of 1.04 mg/kg to Sprague-Dawley rats.
Drug Development and Industrial Pharmacy, Sep 6, 2016
Gemcitabine (GEM) and Baicalein (BCL) are reported to have anti-tumor effects including pancreati... more Gemcitabine (GEM) and Baicalein (BCL) are reported to have anti-tumor effects including pancreatic cancer. Hyaluronic acid (HA) can bind to over-expressed receptors in various kinds of cancer cells. The aim of this study is to develop prodrugs containing HA, BCL and GEM, and construct nanomedicine incorporate GEM and BCL in the core and HA on the surface. This system could target the cancer cells and co-deliver the drugs. GEM-stearic acid lipid prodrug (GEM-SA) and hyaluronic acid-amino acid-baicalein prodrug (HA-AA-BCL) were synthesized. Then, GEM and BCL prodrug-based targeted nanostructured lipid carriers (HA-GEM-BCL NLCs) were prepared by the nanoprecipitation technique. The in vitro cytotoxicity studies of the NLCs were evaluated on AsPC1 pancreatic cancer cell line. In vivo anti-tumor effects were observed on the murine-bearing pancreatic cancer model. HA-GEM-BCL NLCs were effective in entering pancreatic cancer cells over-expressing HA receptors, and showed cytotoxicity of tumor cells in vitro. In vivo study revealed significant tumor growth inhibition ability of HA-GEM-BCL NLCs in murine pancreatic cancer model. It could be concluded that HA-GEM-BCL NLCs could be featured as promising co-delivery, tumor-targeted nanomedicine for the treatment of cancers.
The primary challenge in video super-resolution (VSR) is to handle large motions in the input fra... more The primary challenge in video super-resolution (VSR) is to handle large motions in the input frames, which makes it difficult to accurately aggregate information from multiple frames. Existing works either adopt deformable convolutions or estimate optical flow as a prior to establish correspondences between frames for the effective alignment and fusion. However, they fail to take into account the valuable semantic information that can greatly enhance it; and flow-based methods heavily rely on the accuracy of a flow estimate model, which may not provide precise flows given two low-resolution frames. In this paper, we investigate a more robust and semantic-aware prior for enhanced VSR by utilizing the Segment Anything Model (SAM), a powerful foundational model that is less susceptible to image degradation. To use the SAM-based prior, we propose a simple yet effective module-SAM-guidEd refinEment Module (SEEM), which can enhance both alignment and fusion procedures by the utilization of semantic information. This lightweight plug-in module is specifically designed to not only leverage the attention mechanism for the generation of semantic-aware feature but also be easily and seamlessly integrated into existing methods. Concretely, we apply our SEEM to two representative methods, EDVR and BasicVSR, resulting in consistently improved performance with minimal implementation effort, on three widely used VSR datasets: Vimeo-90K, REDS and Vid4. More importantly, we found that the proposed SEEM can advance the existing methods in an efficient tuning manner, providing increased flexibility in adjusting the balance between performance and the number of training parameters. Code will be open-source soon.
The interactive liveness detection for face recognition often requires users to read some digits ... more The interactive liveness detection for face recognition often requires users to read some digits from 0 to 9. The movement and variation of lip texture during reading potentially provide discriminative information for human identification. This paper firstly addressed the issue of whether the lip texture during reading can serve as a soft-biometric for person identification. Different from the traditional lip recognition methods that are based on color statistics and lip shapes, we develop a deep architecture that incorporates both CNN and LSTM to jointly model the appearance and the spatial-temporal information of lip texture. We also build a new lip recognition database that contains 11,123 videos for the number 0∼9 in Chinese from 57 people. Experimental results show that the proposed method can achieve 96.01% on close-set protocols, suggesting the usage of lip texture as soft-biometrics for facilitating face recognition.
Facial expression synthesis has drawn much attention in the field of computer graphics and patter... more Facial expression synthesis has drawn much attention in the field of computer graphics and pattern recognition. It has been widely used in face animation and recognition. However, it is still challenging due to the high-level semantic presence of large and non-linear face geometry variations. This paper proposes a Geometry-Guided Generative Adversarial Network (G2-GAN) for photo-realistic and identity-preserving facial expression synthesis. We employ facial geometry (fiducial points) as a controllable condition to guide facial texture synthesis with specific expression. A pair of generative adversarial subnetworks are jointly trained towards opposite tasks: expression removal and expression synthesis. The paired networks form a mapping cycle between neutral expression and arbitrary expressions, which also facilitate other applications such as face transfer and expression invariant face recognition. Experimental results show that our method can generate compelling perceptual results on various facial expression synthesis databases. An expression invariant face recognition experiment is also performed to further show the advantages of our proposed method.
A common strategy adopted by existing state-of-the-art unsupervised domain adaptation (UDA) metho... more A common strategy adopted by existing state-of-the-art unsupervised domain adaptation (UDA) methods is to employ two classifiers to identify the misaligned local regions between a source and target domain. Following the 'wisdom of the crowd' principle, one has to ask: why stop at two? Indeed, we find that using more classifiers leads to better performance, but also introduces more model parameters, therefore risking overfitting. In this paper, we introduce a novel method called STochastic clAssifieRs (STAR) for addressing this problem. Instead of representing one classifier as a weight vector, STAR models it as a Gaussian distribution with its variance representing the interclassifier discrepancy. With STAR, we can now sample an arbitrary number of classifiers from the distribution, whilst keeping the model size the same as having two classifiers. Extensive experiments demonstrate that a variety of existing UDA methods can greatly benefit from STAR and achieve the state-of-the-art performance on both image classification and semantic segmentation tasks.
Class Incremental Semantic Segmentation (CISS) has been a trend recently due to its great signifi... more Class Incremental Semantic Segmentation (CISS) has been a trend recently due to its great significance in realworld applications. Although the existing CISS methods demonstrate remarkable performance, they either leverage the high-level knowledge (feature) only while neglecting the rich and diverse knowledge in the low-level features, leading to poor old knowledge preservation and weak new knowledge exploration; or use multi-level features for knowledge distillation by retraining a heavy backbone, which is computationally intensive. In this paper, we for the first time investigate the efficient multi-grained knowledge reuse for CISS, and propose a novel method, Evolving kNowleDge minING (ENDING), employing a frozen backbone. ENDING incorporates two key modules: evolving fusion and semantic enhancement, for dynamic and comprehensive exploration of multi-grained knowledge. Evolving fusion is tailored to extract knowledge from individual low-level feature using a personalized lightweight network, which is generated from a meta-net, taking the highlevel feature as input. This design enables the evolution of knowledge mining and fusing when applied to incremental new classes. In contrast, semantic enhancement is specifically crafted to aggregate prototype-based semantics from multi-level features, contributing to an enhanced representation. We evaluate our method on two widely used benchmarks and consistently demonstrate new state-of-the-art performance. The code is available at https://github.com/zhiheLu/ENDING ISS.
Fine-tuning pre-trained vision-language models (VLMs), e.g., CLIP, for the open-world generalizat... more Fine-tuning pre-trained vision-language models (VLMs), e.g., CLIP, for the open-world generalization has gained increasing popularity due to its practical value. However, performance advancements are limited when relying solely on intricate algorithmic designs for a single model, even one exhibiting strong performance, e.g., CLIP-ViT-B/16. This paper, for the first time, explores the collaborative potential of leveraging much weaker VLMs to enhance the generalization of a robust single model. The affirmative findings motivate us to address the generalization problem from a novel perspective, i.e., ensemble of pretrained VLMs. We introduce three customized ensemble strategies, each tailored to one specific scenario. Firstly, we introduce the zero-shot ensemble, automatically adjusting the logits of different models based on their confidence when only pre-trained VLMs are available. Furthermore, for scenarios with extra few-shot samples, we propose the training-free and tuning ensemble, offering flexibility based on the availability of computing resources. The code is available at https://github.com/zhiheLu/Ensemble VLM.git.
Adapter-style efficient transfer learning (ETL) has shown excellent performance in the tuning of ... more Adapter-style efficient transfer learning (ETL) has shown excellent performance in the tuning of vision-language models (VLMs) under the low-data regime, where only a few additional parameters are introduced to excavate the task-specific knowledge based on the general and powerful representation of VLMs. However, most adapter-style works face two limitations: (i) modeling task-specific knowledge with a single modality only; and (ii) overlooking the exploitation of the interclass relationships in downstream tasks, thereby leading to sub-optimal solutions. To mitigate that, we propose an effective adapter-style tuning strategy, dubbed GraphAdapter, which performs the textual adapter by explicitly modeling the dualmodality structure knowledge (i.e., the correlation of different semantics/classes in textual and visual modalities) with a dual knowledge graph. In particular, the dual knowledge graph is established with two sub-graphs, i.e., a textual knowledge sub-graph, and a visual knowledge sub-graph, where the nodes and edges represent the semantics/classes and their correlations in two modalities, respectively. This enables the textual feature of each prompt to leverage the task-specific structure knowledge from both textual and visual modalities, yielding a more effective classifier for downstream tasks. Extensive experimental results on 11 benchmark datasets reveal that our GraphAdapter significantly outperforms previous adapterbased methods. The code will be released at https://github.com/lixinustc/ GraphAdapter
Facial expression synthesis with various intensities is a challenging synthesis task due to large... more Facial expression synthesis with various intensities is a challenging synthesis task due to large identity appearance variations and a paucity of efficient means for intensity measurement. This paper advances the expression synthesis domain by the introduction of a Couple-Agent Face Parsing based Generative Adversarial Network (CAFP-GAN) that unites the knowledge of facial semantic regions and controllable expression signals. Specially, we employ a face parsing map as a controllable condition to guide facial texture generation with a special expression, which can provide a semantic representation of every pixel of facial regions. Our method consists of two sub-networks: face parsing prediction network (FPPN) uses controllable labels (expression and intensity) to generate a face parsing map transformation that corresponds to the labels from the input neutral face, and facial expression synthesis network (FESN) makes the pretrained FPPN as a part of it to provide the face parsing map as a guidance for expression synthesis. To enhance the reality of results, couple-agent discriminators are served to distinguish fake-real pairs in both two sub-nets. Moreover, we only need the neutral face and the labels to synthesize the unknown expression with different intensities. Experimental results on three popular facial expression databases show that our method has the compelling ability on continuous expression synthesis.
Face synthesis has been a fascinating yet challenging problem in computer vision and machine lear... more Face synthesis has been a fascinating yet challenging problem in computer vision and machine learning. Its main research effort is to design algorithms to generate photo-realistic face images via given semantic domain. It has been a crucial prepossessing step of mainstream face recognition approaches and an excellent test of AI ability to use complicated probability distributions. In this paper, we provide a comprehensive review of typical face synthesis works that involve traditional methods as well as advanced deep learning approaches. Particularly, Generative Adversarial Net (GAN) is highlighted to generate photo-realistic and identity preserving results. Furthermore, the public available databases and evaluation metrics are introduced in details. We end the review with discussing unsolved difficulties and promising directions for future research.
Large-scale vision-language models (VLMs) pre-trained on billion-level data have learned general ... more Large-scale vision-language models (VLMs) pre-trained on billion-level data have learned general visual representations and broad visual concepts. In principle, the welllearned knowledge structure of the VLMs should be inherited appropriately when being transferred to downstream tasks with limited data. However, most existing efficient transfer learning (ETL) approaches for VLMs either damage or are excessively biased towards the prior knowledge, e.g., prompt tuning (PT) discards the pre-trained text-based classifier and builds a new one while adapter-style tuning (AT) fully relies on the pre-trained features. To address this, we propose a new efficient tuning approach for VLMs named Task Residual Tuning (TaskRes), which performs directly on the textbased classifier and explicitly decouples the prior knowledge of the pre-trained models and new knowledge regarding a target task. Specifically, TaskRes keeps the original classifier weights from the VLMs frozen and obtains a new classifier for the target task by tuning a set of prior-independent parameters as a residual to the original one, which enables reliable prior knowledge preservation and flexible task-specific knowledge exploration. The proposed TaskRes is simple yet effective, which significantly outperforms previous ETL methods (e.g., PT and AT) on 11 benchmark datasets while requiring minimal effort for the implementation. Our code is available at https://github.com/geekyutao/TaskRes.
Generalized Few-shot Semantic Segmentation (GFSS) aims to segment each image pixel into either ba... more Generalized Few-shot Semantic Segmentation (GFSS) aims to segment each image pixel into either base classes with abundant training examples or novel classes with only a handful of (e.g., 1-5) training images per class. Compared to the widely studied Few-shot Semantic Segmentation (FSS), which is limited to segmenting novel classes only, GFSS is much under-studied despite being more practical. Existing approach to GFSS is based on classifier parameter fusion whereby a newly trained novel class classifier and a pre-trained base class classifier are combined to form a new classifier. As the training data is dominated by base classes, this approach is inevitably biased towards the base classes. In this work, we propose a novel Prediction Calibration Network (PCN) to address this problem. Instead of fusing the classifier parameters, we fuse the scores produced separately by the base and novel classifiers. To ensure that the fused scores are not biased to either the base or novel classes, a new Transformer-based calibration module is introduced. It is known that the lower-level features are useful of detecting edge information in an input image than higherlevel features. Thus, we build a cross-attention module that guides the classifier's final prediction using the fused multi-level features. However, transformers are computationally demanding. Crucially, to make the proposed cross-attention module training tractable at the pixel level, this module is designed based on feature-score cross-covariance and episodically trained to be generalizable at inference time. Extensive experiments on PASCAL-5 i and COCO-20 i show that our PCN outperforms the state-the-the-art alternatives by large margins.
The goal of image restoration (IR), a fundamental issue in computer vision, is to restore a high-... more The goal of image restoration (IR), a fundamental issue in computer vision, is to restore a high-quality (HQ) image from its degraded low-quality (LQ) observation. Multiple HQ solutions may correspond to an LQ input in this poorly posed problem, creating an ambiguous solution space. This motivates the investigation and incorporation of prior knowledge in order to effectively constrain the solution space and enhance the quality of the restored images. In spite of the pervasive use of hand-crafted and learned priors in IR, limited attention has been paid to the incorporation of knowledge from large-scale foundation models. In this paper, we for the first time leverage the prior knowledge of the state-of-the-art segment anything model (SAM) [55] to boost the performance of existing IR networks in an parameter-efficient tuning manner. In particular, the choice of SAM is based on its robustness to image degradations, such that HQ semantic masks can be extracted from it. In order to leverage semantic priors and enhance restoration quality, we propose a lightweight SAM prior tuning (SPT) unit. This plug-and-play component allows us to effectively integrate semantic priors into existing IR networks, resulting in significant improvements in restoration quality. As the only trainable module in our method, the SPT unit has the potential to improve both efficiency and scalability. We demonstrate the effectiveness of the proposed method in enhancing a variety of methods across multiple tasks, such as image super-resolution and color image denoising.
A rapid, sensitive and high-throughput liquid chromatography-tandem mass spectrometry (LC-MS-MS) ... more A rapid, sensitive and high-throughput liquid chromatography-tandem mass spectrometry (LC-MS-MS) method was established and validated to assay the concentration of nardosinone, a main active compound isolated from Nardostachys chinensis, in rat plasma. Plasma samples were processed by protein precipitation with acetonitrile and separated on a Venusil MP-C 18 column (50 × 2.1 mm, 5 µm) at an isocratic flow rate of 0.6 mL/min using methanol-0.1% formic acid in water (55 : 45, v/v) as mobile phase, and total run time was 2.5 min. MS-MS detection was accomplished in selected reaction monitoring mode with positive electrospray ionization. The calibration curve was linear over the concentration range of 9.60-320 ng/mL with lower limit of quantification of 9.60 ng/mL. The intra-and inter-day precisions were below 12.3% in terms of relative standard deviation, and the accuracy was within ±9.0% in terms of relative error. Extraction recovery, matrix effect and stability were also satisfactory in rat plasma. The developed method was successfully applied to a pharmacokinetic study of nardosinone following an intravenous injection at a dose of 1.04 mg/kg to Sprague-Dawley rats.
Drug Development and Industrial Pharmacy, Sep 6, 2016
Gemcitabine (GEM) and Baicalein (BCL) are reported to have anti-tumor effects including pancreati... more Gemcitabine (GEM) and Baicalein (BCL) are reported to have anti-tumor effects including pancreatic cancer. Hyaluronic acid (HA) can bind to over-expressed receptors in various kinds of cancer cells. The aim of this study is to develop prodrugs containing HA, BCL and GEM, and construct nanomedicine incorporate GEM and BCL in the core and HA on the surface. This system could target the cancer cells and co-deliver the drugs. GEM-stearic acid lipid prodrug (GEM-SA) and hyaluronic acid-amino acid-baicalein prodrug (HA-AA-BCL) were synthesized. Then, GEM and BCL prodrug-based targeted nanostructured lipid carriers (HA-GEM-BCL NLCs) were prepared by the nanoprecipitation technique. The in vitro cytotoxicity studies of the NLCs were evaluated on AsPC1 pancreatic cancer cell line. In vivo anti-tumor effects were observed on the murine-bearing pancreatic cancer model. HA-GEM-BCL NLCs were effective in entering pancreatic cancer cells over-expressing HA receptors, and showed cytotoxicity of tumor cells in vitro. In vivo study revealed significant tumor growth inhibition ability of HA-GEM-BCL NLCs in murine pancreatic cancer model. It could be concluded that HA-GEM-BCL NLCs could be featured as promising co-delivery, tumor-targeted nanomedicine for the treatment of cancers.
The primary challenge in video super-resolution (VSR) is to handle large motions in the input fra... more The primary challenge in video super-resolution (VSR) is to handle large motions in the input frames, which makes it difficult to accurately aggregate information from multiple frames. Existing works either adopt deformable convolutions or estimate optical flow as a prior to establish correspondences between frames for the effective alignment and fusion. However, they fail to take into account the valuable semantic information that can greatly enhance it; and flow-based methods heavily rely on the accuracy of a flow estimate model, which may not provide precise flows given two low-resolution frames. In this paper, we investigate a more robust and semantic-aware prior for enhanced VSR by utilizing the Segment Anything Model (SAM), a powerful foundational model that is less susceptible to image degradation. To use the SAM-based prior, we propose a simple yet effective module-SAM-guidEd refinEment Module (SEEM), which can enhance both alignment and fusion procedures by the utilization of semantic information. This lightweight plug-in module is specifically designed to not only leverage the attention mechanism for the generation of semantic-aware feature but also be easily and seamlessly integrated into existing methods. Concretely, we apply our SEEM to two representative methods, EDVR and BasicVSR, resulting in consistently improved performance with minimal implementation effort, on three widely used VSR datasets: Vimeo-90K, REDS and Vid4. More importantly, we found that the proposed SEEM can advance the existing methods in an efficient tuning manner, providing increased flexibility in adjusting the balance between performance and the number of training parameters. Code will be open-source soon.
The interactive liveness detection for face recognition often requires users to read some digits ... more The interactive liveness detection for face recognition often requires users to read some digits from 0 to 9. The movement and variation of lip texture during reading potentially provide discriminative information for human identification. This paper firstly addressed the issue of whether the lip texture during reading can serve as a soft-biometric for person identification. Different from the traditional lip recognition methods that are based on color statistics and lip shapes, we develop a deep architecture that incorporates both CNN and LSTM to jointly model the appearance and the spatial-temporal information of lip texture. We also build a new lip recognition database that contains 11,123 videos for the number 0∼9 in Chinese from 57 people. Experimental results show that the proposed method can achieve 96.01% on close-set protocols, suggesting the usage of lip texture as soft-biometrics for facilitating face recognition.
Facial expression synthesis has drawn much attention in the field of computer graphics and patter... more Facial expression synthesis has drawn much attention in the field of computer graphics and pattern recognition. It has been widely used in face animation and recognition. However, it is still challenging due to the high-level semantic presence of large and non-linear face geometry variations. This paper proposes a Geometry-Guided Generative Adversarial Network (G2-GAN) for photo-realistic and identity-preserving facial expression synthesis. We employ facial geometry (fiducial points) as a controllable condition to guide facial texture synthesis with specific expression. A pair of generative adversarial subnetworks are jointly trained towards opposite tasks: expression removal and expression synthesis. The paired networks form a mapping cycle between neutral expression and arbitrary expressions, which also facilitate other applications such as face transfer and expression invariant face recognition. Experimental results show that our method can generate compelling perceptual results on various facial expression synthesis databases. An expression invariant face recognition experiment is also performed to further show the advantages of our proposed method.
A common strategy adopted by existing state-of-the-art unsupervised domain adaptation (UDA) metho... more A common strategy adopted by existing state-of-the-art unsupervised domain adaptation (UDA) methods is to employ two classifiers to identify the misaligned local regions between a source and target domain. Following the 'wisdom of the crowd' principle, one has to ask: why stop at two? Indeed, we find that using more classifiers leads to better performance, but also introduces more model parameters, therefore risking overfitting. In this paper, we introduce a novel method called STochastic clAssifieRs (STAR) for addressing this problem. Instead of representing one classifier as a weight vector, STAR models it as a Gaussian distribution with its variance representing the interclassifier discrepancy. With STAR, we can now sample an arbitrary number of classifiers from the distribution, whilst keeping the model size the same as having two classifiers. Extensive experiments demonstrate that a variety of existing UDA methods can greatly benefit from STAR and achieve the state-of-the-art performance on both image classification and semantic segmentation tasks.
Class Incremental Semantic Segmentation (CISS) has been a trend recently due to its great signifi... more Class Incremental Semantic Segmentation (CISS) has been a trend recently due to its great significance in realworld applications. Although the existing CISS methods demonstrate remarkable performance, they either leverage the high-level knowledge (feature) only while neglecting the rich and diverse knowledge in the low-level features, leading to poor old knowledge preservation and weak new knowledge exploration; or use multi-level features for knowledge distillation by retraining a heavy backbone, which is computationally intensive. In this paper, we for the first time investigate the efficient multi-grained knowledge reuse for CISS, and propose a novel method, Evolving kNowleDge minING (ENDING), employing a frozen backbone. ENDING incorporates two key modules: evolving fusion and semantic enhancement, for dynamic and comprehensive exploration of multi-grained knowledge. Evolving fusion is tailored to extract knowledge from individual low-level feature using a personalized lightweight network, which is generated from a meta-net, taking the highlevel feature as input. This design enables the evolution of knowledge mining and fusing when applied to incremental new classes. In contrast, semantic enhancement is specifically crafted to aggregate prototype-based semantics from multi-level features, contributing to an enhanced representation. We evaluate our method on two widely used benchmarks and consistently demonstrate new state-of-the-art performance. The code is available at https://github.com/zhiheLu/ENDING ISS.
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