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- A Bottom-Up Clustering Approach to Unsupervised Person Re-Identification589
The experimental results demonstrate that the bottom-up clustering approach to jointly optimize a convolutional neural network and the relationship among the individual samples is not only superior to state-of-the-art unsupervised re-ID approaches, but also performs favorably than competing transfer learning and semi-supervised learning methods.
- Post-Training Quantization on Diffusion Models376
This work accelerates generation of diffusion model (DM) generation from the perspective of compressing the noise estimation network and devise a DM-specific PTQ method, which especially targets the unique multi-time-step structure of DMs.
- Style Aggregated Network for Facial Landmark Detection339
This work proposes a style-aggregated approach to deal with the large intrinsic variance of image styles for facial landmark detection and demonstrates to perform well when compared with state-of-the-art algorithms on benchmark datasets AFLW and 300-W.
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- Person Re-identification via Recurrent Feature Aggregation253
This work shows that a progressive/sequential fusion framework based on long short term memory (LSTM) network aggregates the frame-wise human region representation at each time stamp and yields a sequence level human feature representation.
- Towards a Unified Middle Modality Learning for Visible-Infrared Person Re-Identification220
A non-linear middle modality generator (MMG) is proposed, which helps to reduce the modality discrepancy in visible-infrared person re-identification (VI-ReID), and amiddle modality network (MMN) is presented to further enhance the discrimination and richness of features in an explicit manner.
- Feature Decomposition and Reconstruction Learning for Effective Facial Expression Recognition213
Experimental results show that the proposed FDRL method consistently achieves higher recognition accuracy than several state-of-the-art methods, which clearly highlights the benefit of feature decomposition and reconstruction for classifying expressions.
- Segmenting Objects in Day and Night: Edge-Conditioned CNN for Thermal Image Semantic Segmentation163
This article elaborately design a gated featurewise transform layer in EC-CNN to adaptively incorporate edge prior knowledge and introduces a new benchmark data set named “Segmenting Objects in Day and night” (SODA) for comprehensive evaluations in thermal image semantic segmentation.
- Graph self-representation method for unsupervised feature selection159
A new unsupervised feature selection by integrating a subspace learning method into a new feature selection method (i.e., Locality Preserving Projection) and adding a graph regularization term into the resulting feature selection model to simultaneously conduct feature selection and subspaceLearning.
- Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenes146
This paper proposes a real-time high-performance DCNN-based method for robust semantic segmentation of urban street scenes, which achieves a good trade-off between accuracy and speed.
- Cross-Modal Attention Network for Temporal Inconsistent Audio-Visual Event Localization119
Inspired by human system which puts different focuses at specific locations, time segments and media while performing multi-modality perception, this work provides an attention-based method to simulate such process and achieves state-of-the-art performance.
- GestureGAN for Hand Gesture-to-Gesture Translation in the Wild99
Extensive experiments demonstrate that the proposed GestureGAN achieves state-of-the-art performance on the unconstrained hand gesture-to-gesture translation task and the generated images are in high-quality and are photo-realistic, allowing them to be used as data augmentation to improve the performance of a hand gesture classifier.
- Few-shot Medical Image Segmentation with Cycle-resemblance Attention96
A novel self-supervised few-shot medical image segmentation network is proposed and a novel Cycle-Resemblance Attention (CRA) module is introduced to fully leverage the pixel-wise relation between query and support medical images.
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- Audio-Visual Event Localization via Recursive Fusion by Joint Co-Attention77
A novel joint attention mechanism with multi-modal fusion methods for audio-visual event localization and a concise yet valid architecture that effectively learns representations from multiple modalities in a joint manner is presented.
- Deep Disturbance-Disentangled Learning for Facial Expression Recognition74
This paper proposes a novel Deep Disturbance-disentangled Learning (DDL) method for FER that is capable of simultaneously and explicitly disentangling multiple disturbing factors by taking advantage of multi-task learning and adversarial transfer learning.
- A fast face detection method via convolutional neural network73
Experimental results on several popular face detection datasets show the efficiency and the effectiveness of the proposed fast face detection method based on discriminative complete features extracted by an elaborately designed convolutional neural network.
- Automatic Image Cropping for Visual Aesthetic Enhancement Using Deep Neural Networks and Cascaded Regression73
A novel cascaded cropping regression (CCR) method to perform image cropping by learning the knowledge from professional photographers is proposed, which improves the convergence speed of the cascaded method, which directly uses random-ferns regressors.
- Stacking of doxorubicin on folic acid-targeted multiwalled carbon nanotubes for in vivo chemotherapy of tumors72
The developed FA-modified MWCNTs with an unconventionally high DOX loading boosted in vivo anti-tumor efficacy, and the lower systemic toxicity may be utilized for tumor therapy upon clinical translation.
- Stage-Aware Feature Alignment Network for Real-Time Semantic Segmentation of Street Scenes71
A novel Stage-aware Feature Alignment Network (SFANet) based on the encoder-decoder structure for real-time semantic segmentation of street scenes is presented and an auxiliary training strategy is developed to explicitly alleviate the multi-scale object problem without bringing additional computational costs during the inference phase.
- Deep Multi-Task Multi-Label CNN for Effective Facial Attribute Classification64
A novel deep multi-task multi-label CNN, termed DMM-CNN, is proposed for effective FAC, which jointly optimizes two closely-related tasks to improve the performance of FAC by taking advantage of multi- task learning.
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- Multi-Level Visual-Semantic Alignments with Relation-Wise Dual Attention Network for Image and Text Matching58
A relation-wise dual attention network (RDAN) for image-text matching is proposed, which maintains an over-complete set that contains pairs of regions and words and encodes the local correlations and the global dependencies between regions and Words by training a visual-semantic network.
- Searching for Representative Modes on Hypergraphs for Robust Geometric Model Fitting58
A simple and effective geometric model fitting method to fit and segment multi-structure data even in the presence of severe outliers and achieves significant superiority over several state-of-the-art model fitting methods on both synthetic data and real images.
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- A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network52
The segmentation and classification of tumor images as well as the diagnosis methods based on CNN to help doctors determine tumors are introduced and provided a reference for developing a CNN computer-aided system based on tumor detection research in the future.
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- Multi-subregion based correlation filter bank for robust face recognition51
The proposed Multi-Subregion based Correlation Filter Bank (MS-CFB) combines the benefits of global-based and local-based feature extraction algorithms, where multiple correlation filters corresponding to different face subregions are jointly designed to optimize the overall correlation outputs.
- Joint Deep Learning of Facial Expression Synthesis and Recognition50
A novel joint deep learning of facial expression synthesis and recognition method for effective FER and an intra- class loss with a novel real data-guided back-propagation (RDBP) algorithm to reduce the intra-class variations of images from the same class, which can significantly improve the final performance.
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- Parallel Blockwise Knowledge Distillation for Deep Neural Network Compression48
A novel parallel blockwise distillation algorithm to accelerate the distillation process of sophisticated DNNs is proposed, which leverages local information to conduct independent blockwisedistillation, utilizes depthwise separable layers as the efficient replacement block architecture, and properly addresses limiting factors that affect parallelism.
- Low-resolution facial expression recognition: A filter learning perspective47
Experimental results on several facial expression datasets show the superior performance of the proposed IFSL method for low-resolution facial expression recognition, compared with several state-of-the-art methods.
- Improved spatial pyramid matching for scene recognition46
A new type of spatial partitioning scheme and a modified pyramid matching kernel based on spatial pyramid matching (SPM) are proposed and a dense histogram of oriented gradients is used as a low-level visual descriptor.
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- Skeleton Sequence and RGB Frame Based Multi-Modality Feature Fusion Network for Action Recognition45
This work proposes a multi-modality feature fusion network to combine the modalities of the skeleton sequence and RGB frame instead of the RGB video, as the key information contained by the combination of the skeletons sequence andRGB frame is close to that of the Skeleton Sequence and RGB video.
- Adaptive Deep Disturbance-Disentangled Learning for Facial Expression Recognition45
Extensive experimental results show that the proposed ADDL method consistently outperforms several state-of-the-art FER methods, demonstrating the great potential of disturbance disentanglement for FER.
- Progressive Cross-modal Knowledge Distillation for Human Action Recognition44
A novel Progressive Skeleton-to-sensor Knowledge Distillation (PSKD) model which utilizes only time-series data, i.e., accelerometer data, from a smartwatch for solving the wearable sensor-based HAR problem and proposes an effective progressive learning scheme to eliminate the performance gap between teacher and student models.
- Deep Multi-Branch Aggregation Network for Real-Time Semantic Segmentation in Street Scenes42
A novel Deep Multi-branch Aggregation Network based on the encoder-decoder structure to perform real-time semantic segmentation in street scenes is proposed and extensive experimental results demonstrate that DMA-Net provides a good tradeoff between segmentation quality and speed.
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- A tree-based decision method for the configuration design of reconfigurable machine tools41
A tree-based method to determine the configuration design for reconfiguration of an RMT using a tree structure is proposed and the benefit of using the proposed method is its improved functionality.
- iERM: An Interpretable Deep Learning System to Classify Epiretinal Membrane for Different Optical Coherence Tomography Devices: A Multi-Center Analysis40
iERM proved to be a benchmark method to improve the performance and enhance the interpretability of the traditional DL model with the implementation of segmentation based on prior human knowledge and may have the potential to provide precise guidance for ERM diagnosis and treatment.
- Multi-task Learning of Cascaded CNN for Facial Attribute Classification40
A novel multi-task learning of cascaded convolutional neural network method, termed MCFA, for predicting multiple facial attributes simultaneously, which outperforms several state-of-the-art FAC methods on the challenging CelebA and LFWA datasets.
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- Laplacian Salience-Gated Feature Pyramid Network for Accurate Liver Vessel Segmentation38
Observations suggest that the proposed Laplacian salience, together with the elaborated dataset, can be helpful for liver vessel segmentation.
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- Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo Collection36
A novel Learning to Aggregate and Personalize (LAP) framework for unsupervised robust 3D face modeling that recovers superior or competitive face shape and texture compared with state-of-the-art (SOTA) methods with or without prior and supervision.
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- Attention‐based deep learning system for automated diagnoses of age‐related macular degeneration in optical coherence tomography images34
A deep learning system based on OCT images to automatically diagnose age-related macular degeneration and achieved a detection precision and recall equal to those of ophthalmologists is developed.
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- Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting33
A novel geometric model fitting method, called Mode-Seeking on Hypergraphs (MSH), to deal with multi-structure data even in the presence of severe outliers and has significant superiority over the state-of-the-art fitting methods on both synthetic data and real images.
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- When Facial Expression Recognition Meets Few-Shot Learning: A Joint and Alternate Learning Framework29
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- DD-Ranking: Rethinking the Evaluation of Dataset Distillation14
DD-Ranking, a unified evaluation framework, along with new general evaluation metrics to uncover the true performance improvements achieved by different methods are proposed, which provide a more comprehensive and fair evaluation standard for future research advancements.
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- Hierarchical Token-Aware Cross-Modality Reconstruction for Visible-Infrared Person Re-Identification13
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- GLaD: Geometric Latent Distillation for Vision-Language-Action Models11
This work introduces GLaD, a geometry-aware VLA framework that incorporates 3D geometric priors during pretraining through knowledge distillation, validate that geometry-aware pretraining enhances spatial reasoning and policy generalization without requiring explicit depth sensors or 3D annotations.
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- Mitosis event recognition and detection based on evolution of feature in time domain11
This paper presents an approach which utilized the evolution of feature in the time domain to represent the feature of mitosis and constructs the levels of motorists according to the steps of mitotic cells.
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- Augmentation Matters: A Mix-Paste Method for X-Ray Prohibited Item Detection Under Noisy Annotations5
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- Modality Interference Decoupling and Representation Alignment for Caricature-Visual Face Recognition1
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- MLRT-DETR: A Multi-Directional and Multi-Level Small Object Detection Algorithm from UAV Perspective–
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- Neyman’s Smoothness Test: A Trade-Off Between Moment-Based and Distribution-Based Leakage Detections–
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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