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Works87 from public data
- Generalizing to Unseen Domains: A Survey on Domain Generalization1,906
This paper provides a formal definition of domain generalization and discusses several related fields, and categorizes recent algorithms into three classes: data manipulation, representation learning, and learning strategy, and presents several popular algorithms in detail for each category.
- An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data1,071
This work proposes an end-to-end spatial and temporal attention model for human action recognition from skeleton data on top of the Recurrent Neural Networks with Long Short-Term Memory (LSTM), which learns to selectively focus on discriminative joints of skeleton within each frame of the inputs and pays different levels of attention to the outputs of different frames.
- Co-Occurrence Feature Learning for Skeleton Based Action Recognition Using Regularized Deep LSTM Networks930
This work takes the skeleton as the input at each time slot and introduces a novel regularization scheme to learn the co-occurrence features of skeleton joints, and proposes a new dropout algorithm which simultaneously operates on the gates, cells, and output responses of the LSTM neurons.
- View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data675
A novel view adaptation scheme to automatically regulate observation viewpoints during the occurrence of an action by design a view adaptive recurrent neural network with LSTM architecture, which enables the network itself to adapt to the most suitable observation viewpoints from end to end.
- Relation-Aware Global Attention for Person Re-Identification652
This work proposes an effective Relation-Aware Global Attention (RGA) module which captures the global structural information for better attention learning and proposes to stack the relations, i.e., its pairwise correlations/affinities with all the feature positions together to learn the attention with a shallow convolutional model.
- Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition647
A simple yet effective semantics-guided neural network (SGN) for skeleton-based action recognition that achieves the state-of-the-art performance on the NTU60, NTU120, and SYSU datasets.
- View Adaptive Neural Networks for High Performance Skeleton-Based Human Action Recognition522
A novel view adaptation scheme, which automatically determines the virtual observation viewpoints over the course of an action in a learning based data driven manner, and a two-stream scheme (referred to as VA-fusion) that fuses the scores of the two networks to provide the final prediction, obtaining enhanced performance.
- Style Normalization and Restitution for Generalizable Person Re-Identification426
The aim of this paper is to design a generalizable person ReID framework which trains a model on source domains yet is able to generalize/perform well on target domains, and to enforce a dual causal loss constraint in SNR to encourage the separation of identity-relevant features and identity-irrelevant features.
- Densely Semantically Aligned Person Re-Identification302
This work is the first to make use of fine grained semantics for addressing misalignment problems for re-ID and construct a set of densely semantically aligned part images (DSAP-images), where the same spatial positions have the same semantics across different person images.
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- Online Human Action Detection Using Joint Classification-Regression Recurrent Neural Networks226
This paper proposes a multi-task end-to-end Joint Classification-Regression Recurrent Neural Network to better explore the action type and temporal localization information and demonstrates very promising performance of this scheme.
- Diffusion Models for Image Restoration and Enhancement: A Comprehensive Survey224
This first to present a comprehensive review of recent diffusion model-based methods on image restoration, encompassing the learning paradigm, conditional strategy, framework design, modeling strategy, and evaluation, and proposes nine potential and challenging directions for the future research of diffusion model-based IR.
- ReSTR: Convolution-free Referring Image Segmentation Using Transformers210
The first convolution-free model for referring image segmentation using transformers, dubbed ReSTR, is presented, which can capture long-range dependencies between entities within each modality and outperforms all existing models.
- Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification194
This paper proposes to estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels, by suppressing the contribution of noisy samples, and introduces uncertainty-guided optimization, which brings significant improvement and achieves the state-of-the-art performance on benchmark datasets.
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- Semantics-Aligned Representation Learning for Person Re-Identification164
A framework that drives the reID network to learn semantics-aligned feature representation through delicate supervision designs is proposed and achieves the state-of-the-art performances on the benchmark datasets CUHK03, Market1501, MSMT17, and the partial person reID dataset Partial REID.
- MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation135
This paper proposes an effective meta-optimization based strategy dubbed MetaAlign, which treats the domain alignment objective and the classification objective as the meta-train and meta-test tasks in a meta-learning scheme, and encourages both tasks to be optimized in a coordinated way.
- Multi-Granularity Reference-Aided Attentive Feature Aggregation for Video-Based Person Re-Identification128
This paper proposes an attentive feature aggregation module, namely Multi-Granularity Reference-aided Attentive Feature Aggregation (MG-RAFA), to delicately aggregate spatio-temporal features into a discriminative video-level feature representation.
- Uncertainty-Aware Multi-Shot Knowledge Distillation for Image-Based Object Re-Identification127
This paper designs an Uncertainty-aware Multi-shot Teacher-Student (UMTS) Network that consists of a teacher network that learns the comprehensive features from multiple images of the same object, and a student network that takes a single image as input.
- Adaptive Frequency Filters As Efficient Global Token Mixers124
This work applies conventional convolution theorem to deep learning and reveals that adaptive frequency filters can serve as efficient global token mixers, and takes AFF token mixers as primary neural operators to build a lightweight neural network, dubbed AFFNet.
- Style Normalization and Restitution for Domain Generalization and Adaptation113
A novel Style Normalization and Restitution module (SNR) is designed to simultaneously ensure high generalization and discrimination capability of the networks and enforce a dual restitution loss constraint to encourage the better separation of task-relevant and task-irrelevant features.
- Adding Attentiveness to the Neurons in Recurrent Neural Networks102
The effectiveness of the proposed EleAtt-RNN is demonstrated by applying it to the action recognition tasks on both 3D human skeleton data and RGB videos.
- Deep Frequency Filtering for Domain Generalization97
The effectiveness of the proposed Deep Frequency Filtering is demonstrated and it is shown that applying the DFF on a plain baseline out-performs the state-of-the-art methods on different domain generalization tasks, including close-set classification and open-set retrieval.
- Pose-Guided Feature Learning with Knowledge Distillation for Occluded Person Re-Identification87
A network named Pose-Guided Feature Learning with Knowledge Distillation (PGFL-KD), where the pose information is exploited to regularize the learning of semantics aligned features but is discarded in testing to achieve high accuracy while preserving low inference complexity.
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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 2026-10-15. 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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