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- NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis3,115
A large-scale dataset for RGB+D human action recognition with more than 56 thousand video samples and 4 million frames, collected from 40 distinct subjects is introduced and a new recurrent neural network structure is proposed to model the long-term temporal correlation of the features for each body part, and utilize them for better action classification.
- NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding1,846
This work introduces a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames, and investigates a novel one-shot 3D activity recognition problem on this dataset.
- Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition1,414
This paper introduces new gating mechanism within LSTM to learn the reliability of the sequential input data and accordingly adjust its effect on updating the long-term context information stored in the memory cell, and proposes a more powerful tree-structure based traversal method.
- Human Action Recognition From Various Data Modalities: A Review848
This article presents a comprehensive survey of recent progress in deep learning methods for HAR based on the type of input data modality, including the fusion-based and the co-learning-based frameworks.
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- Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates540
A new gating mechanism within LSTM module is introduced, with which the network can learn the reliability of the sequential data and accordingly adjust the effect of the input data on the updating procedure of the long-term context representation stored in the unit's memory cell.
- Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study389
A real-time quality improvement system (ENDOANGEL) to monitor real- time withdrawal speed and colonoscopy withdrawal time and to remind endoscopists of blind spots caused by endoscope slipping significantly improved the adenoma yield during Colonoscopy.
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- UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles282
A fisheye-based action recognition method that mitigates the distortions in fisHEye videos via learning unbounded transformations guided by flat RGB videos is proposed, and experiments show the efficacy of this method on the UAV-Human dataset.
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- A convex formulation for learning shared structures from multiple tasks209
An improved formulation for multi-task learning based on the non-convex alternating structure optimization (ASO) algorithm, in which all tasks are related by a shared feature representation, is presented.
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- Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models194
This survey quantitatively assesses model performance, delves into research challenges and directions, and outlines potential avenues for future exploration, as well as assembling a compilation of research resources, including public datasets, available codebases, programming tools, and pertinent literature.
- DiffPose: Toward More Reliable 3D Pose Estimation194
This work explores a novel pose estimation framework (DiffPose) that formulates 3D pose estimation as a reverse diffusion process and significantly outperforms existing methods on the widely used pose estimation benchmarks Human3.6M and MPI-INF-3DHP.
- Animal Kingdom: A Large and Diverse Dataset for Animal Behavior Understanding191
This work creates a large and diverse dataset, Animal Kingdom, that provides multiple annotated tasks to enable a more thorough understanding of natural animal behaviors, and proposes a Collaborative Action Recognition model that learns general and specific features for action recognition with unseen new animals.
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- Interventional Video Grounding with Dual Contrastive Learning175
A novel paradigm from the perspective of the causal inference, i.e., interventional video grounding (IVG) that leverages backdoor adjustment to deconfound the selection bias based on structured causal model (SCM) and do-calculus P(Y |do(X)).
- Generalizable Person Re-identification with Relevance-aware Mixture of Experts174
This work proposes a novel method called the relevance-aware mixture of experts (RaMoE), using an effective voting-based mixture mechanism to dynamically leverage source domains’ diverse characteristics to improve the model’s generalization.
- Skeleton-Based Online Action Prediction Using Scale Selection Network174
A dilated convolutional network is introduced to model the motion dynamics in temporal dimension via a sliding window over the temporal axis and a novel window scale selection method is proposed to make the network focus on the performed part of the ongoing action and try to suppress the possible incoming interference from the previous actions at each step.
- IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID154
An Intermediate Domain Module (IDM) is proposed to generate intermediate domains’ representations on-the-fly by mixing the source and target domains�’ hidden representations using two domain factors and ensures two properties that these intermediate domains should satisfy.
- A novel artificial intelligence system for the assessment of bowel preparation (with video)146
A novel and more accurate evaluation method for bowel preparation and an objective and stable system-ENDOANGEL-that could be applied reliably and steadily in clinical settings are provided.
- SoftPatch: Unsupervised Anomaly Detection with Noisy Data144
Comprehensive experiments demonstrate that SoftPatch outperforms the state-of-the-art AD methods on the MVTecAD and BTAD benchmarks and is comparable to those methods under the setting without noise.
- SUTD-TrafficQA: A Question Answering Benchmark and an Efficient Network for Video Reasoning over Traffic Events141
A novel dataset, SUTD-TrafficQA (Traffic Question Answering), which takes the form of video QA based on the collected 10,080 in-the-wild videos and annotated 62,535 QA pairs, for benchmarking the cognitive capability of causal inference and event understanding models in complex traffic scenarios.
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 2026-10-12. 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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