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- TransReID: Transformer-based Object Re-Identification1,342
This work first encode an image as a sequence of patches and build a transformer-based strong baseline with a few critical improvements, which achieves competitive results on several ReID benchmarks with CNN-based methods.
- Hallucination of Multimodal Large Language Models: A Survey466
A comprehensive analysis of the phenomenon of hallucination in multimodal large language models (MLLMs), also known as Large Vision-Language Models (LVLMs), offering a detailed overview of the underlying causes, evaluation benchmarks, metrics, and strategies developed to address this issue.
- MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation452
A Multi-Hypothesis Transformer (MHFormer) that learns spatio-temporal representations of multiple plausible pose hypotheses and achieves state-of-the-art results on two challenging datasets: Human3.6M and MPI-INF-3DHP.
- RGB-D-based human motion recognition with deep learning: A survey403
A detailed overview of recent advances in RGB-D-based motion recognition is presented, and the methods of encoding spatial-temporal-structural information inherent in video sequence are highlighted, and potential directions for future research are discussed.
- Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks403
A compact, effective yet simple method to encode spatio-temporal information carried in 3D skeleton sequences into multiple 2D images, referred to as Joint Trajectory Maps (JTM), and ConvNets are adopted to exploit the discriminative features for real-time human action recognition.
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- Exploiting Temporal Contexts With Strided Transformer for 3D Human Pose Estimation314
An improved Transformer-based architecture, called Strided Transformer, which simply and effectively lifts a long sequence of 2D joint locations to a single 3D pose, and achieves state-of-the-art results with fewer parameters.
- RGB-D-based action recognition datasets: A survey291
A comprehensive review of the most commonly used action recognition related RGB-D video datasets, including 27 single-view datasets, 10 multi- view datasets, and 7 multi-person datasets, resulting in a number of recommendations for collection of new datasets and use of evaluation protocols.
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- PoseFormerV2: Exploring Frequency Domain for Efficient and Robust 3D Human Pose Estimation257
PoseFormerV2 is proposed, which exploits a compact representation of lengthy skeleton sequences in the frequency domain to efficiently scale up the receptive field and boost robustness to noisy 2D joint detection.
- Action recognition based on joint trajectory maps with convolutional neural networks237
An effective yet simple method to represent spatio-temporal information carried in 3 D skeleton sequences into three 2 D images by encoding the joint trajectories and their dynamics into color distribution in the images, referred to as Joint Trajectory Maps (JTM).
- EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation229
EPro-PnP is proposed, a probabilistic PnP layer for general end-to-end pose estimation, which outputs a distribution of pose with differentiable probability density on the SE(3) manifold, and helps to explore new possibilities of network design.
- Selective Structured State-Spaces for Long-Form Video Understanding200
A novel Selective S4 model that employs a lightweight mask generator to adaptively select informative image tokens resulting in more efficient and accurate modeling of long-term spatiotemporal dependencies in videos and a novel long-short masked contrastive learning (LSMCL) approach that enables the model to predict longer temporal context using shorter input videos.
- Skeleton-based action recognition using LSTM and CNN192
The proposed method achieved state-of-the-art results on NTU RGB+D datasets for 3D human action analysis and ranked 1st place in Large Scale 3D Human Activity Analysis Challenge in Depth Videos.
- Zen-NAS: A Zero-Shot NAS for High-Performance Image Recognition186
A novel zero-shot index dubbed Zen- score is proposed to rank the architectures in Neural Architecture Search by maximizing the Zen-Score of the target network under given inference budgets, which is magnitude times faster on multiple server-side and mobile-side GPU platforms with state-of-the-art accuracy on ImageNet.
- Depth Pooling Based Large-Scale 3-D Action Recognition With Convolutional Neural Networks184
The image-based representations enable the existing ConvNet models trained on image data without training a large number of parameters from scratch, and achieve the state-of-art results on three large datasets, namely, the large-scale continuous gesture recognition dataset.
- KVT: k-NN Attention for Boosting Vision Transformers159
A sparse attention scheme, dubbed k-NN attention, which naturally inherits the local bias of CNNs without introducing convolutional operations, and allows for the exploration of long range correlation and filter out irrelevant tokens by choosing the most similar tokens from the entire image.
- Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks157
A new representation, namely, Scene Flow to Action Map (SFAM), that describes several long term spatio-temporal dynamics for action recognition from RGB-D data and takes better advantage of the trained ConvNets models over ImageNet.
- One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos147
VideoLISA is introduced, a video-based multimodal large language model designed to tackle the problem of language-instructed reasoning segmentation in videos, and shows promising generalization to image segmentation, revealing its potential as a unified foundation model for language-instructed object segmentation.
- Unsupervised feature selection via latent representation learning and manifold regularization144
A robust unsupervised feature selection method which embeds the latent representation learning into feature selection and is carried out in the learned latent representation space which is more robust to noises.
- TransRPPG: Remote Photoplethysmography Transformer for 3D Mask Face Presentation Attack Detection142
This letter proposes a pure rPPG transformer (TransRPPG) framework for learning intrinsic liveness representation efficiently and is lightweight and efficient, which is promising for mobile-level applications.
- Robust unsupervised feature selection via dual self-representation and manifold regularization138
Experimental results on ten real-world data sets demonstrate that the proposed method can effectively identify important features, outperforming many state-of-the-art unsupervised feature selection methods in terms of clustering accuracy (ACC) and normalized mutual information (NMI).
- Searching Multi-Rate and Multi-Modal Temporal Enhanced Networks for Gesture Recognition137
The proposed method includes two key components: enhanced temporal representation via the proposed 3D Central Difference Convolution (3D-CDC) family, which is able to capture rich temporal context via aggregating temporal difference information; and optimized backbones for multi-sampling-rate branches and lateral connections among varied modalities.
- ConvNets-Based Action Recognition from Depth Maps through Virtual Cameras and Pseudocoloring112
Through transferring learning the models originally trained over ImageNet for image classification, the three ConvNets are trained independently on the color-coded DMMs constructed in three orthogonal planes, achieving state-of-the-art results on these datasets.
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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