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- 4,387
- Action recognition with trajectory-pooled deep-convolutional descriptors1,205
This paper presents a new video representation, called trajectory-pooled deep-convolutional descriptor (TDD), which shares the merits of both hand-crafted features and deep-learned features, and achieves superior performance to the state of the art on these datasets.
- A review of convolutional neural networks in computer vision1,170
An elementary understanding of CNN components and their functions, including input layers, convolution layers, pooling layers, activation functions, batch normalization, dropout, fully connected layers, and output layers are presented.
- Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering1,011
This work introduces Scaffold-GS, which uses an-chor points to distribute local 3D Gaussians, and predicts their attributes on-the-fly based on viewing direction and distance within the view frustum, and effectively reduces redun-dant Gaussians while delivering high-quality rendering.
- Temporal Action Detection with Structured Segment Networks992
This paper addresses an important and challenging task, namely detecting the temporal intervals of actions in untrimmed videos by presenting a framework called structured segment network (SSN), built on temporal proposals of actions.
- Temporal Segment Networks for Action Recognition in Videos989
The proposed TSN framework, called temporal segment network (TSN), aims to model long-range temporal structure with a new segment-based sampling and aggregation scheme and won the video classification track at the ActivityNet challenge 2016 among 24 teams.
- MixFormer: End-to-End Tracking with Iterative Mixed Attention911
This paper proposes a compact tracking framework, termed as MixFormer, built upon transformers, to utilize the flexibility of attention operations, and proposes a Mixed Attention Module (MAM) for simultaneous feature extraction and target information integration.
- VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking871
This paper shows that video masked autoencoder (VideoMAE) is a scalable and general self-supervised pre-trainer for building video foundation models, and presents a dual masking strategy for efficient pre-training, which enables the efficientPre-training of billion-level models in video.
- Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice677
A comprehensive study of all steps in BoVW and different fusion methods is provided, and a simple yet effective representation is proposed, called hybrid supervector, by exploring the complementarity of different BoVW frameworks with improved dense trajectories.
- InternVideo: General Video Foundation Models via Generative and Discriminative Learning584
Without bells and whistles, InternVideo achieves state-of-the-art performance on 39 video datasets from extensive tasks including video action recognition/detection, video-language alignment, and open-world video applications.
- TEA: Temporal Excitation and Aggregation for Action Recognition575
This paper proposes a Temporal Excitation and Aggregation block, including a motion excitation module and a multiple temporal aggregation module, specifically designed to capture both short- and long-range temporal evolution, and achieves impressive results at low FLOPs on several action recognition benchmarks.
- InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation557
A scalable approach to autonomously build a high-quality video-text dataset with large language models (LLM), thereby showcasing its efficacy in learning video-language representation at scale and introducing ViCLIP, a video- Text representation learning model based on ViT-L.
- UntrimmedNets for Weakly Supervised Action Recognition and Detection546
This paper presents a new weakly supervised architecture, called UntrimmedNet, which is able to directly learn action recognition models from untrimmed videos without the requirement of temporal annotations of action instances.
- TDN: Temporal Difference Networks for Efficient Action Recognition506
A new video architecture, termed as Temporal Difference Network (TDN), with a focus on capturing multi-scale temporal information for efficient action recognition, which provides a simple and principled temporal modeling framework and could be instantiated with the existing CNNs at a small extra computational cost.
- Real-Time Action Recognition with Enhanced Motion Vector CNNs444
This paper accelerates the deep two-stream architecture by replacing optical flow with motion vector which can be obtained directly from compressed videos without extra calculation, and introduces three strategies for this, initialization transfer, supervision transfer and their combination.
- InternVideo2: Scaling Foundation Models for Multimodal Video Understanding438
This work introduces InternVideo2, a new family of video foundation models (ViFM) that achieve the state-of-the-art results in video recognition, video-text tasks, and video-centric dialogue, highlighting its ability to reason and comprehend longer contexts.
- Appearance-and-Relation Networks for Video Classification403
This paper presents a new architecture, termed as Appearance-and-Relation Network (ARTNet), to learn video representation in an end-to-end manner, constructed by stacking multiple generic building blocks, called SMART, whose goal is to simultaneously model appearance and relation from RGB input in a separate and explicit manner.
- PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop402
A Pyramidal Mesh Alignment Feedback (PyMAF) loop to leverage a feature pyramid and rectify the predicted parameters explicitly based on the mesh-image alignment status in the authors' deep regressor is proposed.
- TAM: Temporal Adaptive Module for Video Recognition388
A new temporal adaptive module (TAM) to generate video-specific temporal kernels based on its own feature map that outperforms other temporal modeling methods consistently, and achieves the state-of-the-art performance under the similar complexity.
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- 311
- Learning Actor Relation Graphs for Group Activity Recognition306
This paper proposes to build a flexible and efficient Actor Relation Graph (ARG) to simultaneously capture the appearance and position relation between actors, and performs extensive experiments on two standard group activity recognition datasets.
- Unmasked Teacher: Towards Training-Efficient Video Foundation Models288
This paper proposes a training-efficient method for temporal-sensitive VFMs that integrates the benefits of existing methods, and masks out most of the low-semantics video tokens, but selectively align the unmasked tokens with IFM, which serves as the UnMasked Teacher (UMT).
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- TEINet: Towards an Efficient Architecture for Video Recognition275
The proposed TEINet can achieve a good recognition accuracy on these datasets but still preserve a high efficiency, and is able to capture temporal structure flexibly and effectively, but also efficient for model inference.
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 2026-10-14. 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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