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Works76 from public data
- Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition5,594
A novel model of dynamic skeletons called Spatial-Temporal Graph Convolutional Networks (ST-GCN), which moves beyond the limitations of previous methods by automatically learning both the spatial and temporal patterns from data.
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- Unsupervised Feature Learning via Non-parametric Instance Discrimination4,044
This work forms this intuition as a non-parametric classification problem at the instance-level, and uses noise-contrastive estimation to tackle the computational challenges imposed by the large number of instance classes.
- 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.
- 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.
- Learning Self-Consistency for Deepfake Detection405
A novel representation learning approach, called pair-wise self-consistency learning (PCL), is introduced for training ConvNets to extract source features and detect deepfake images.
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- MeMOT: Multi-Object Tracking with Memory275
An online tracking algorithm that performs the object detection and data association under a common framework, capable of linking objects after a long time span is proposed, by preserving a large spatio-temporal memory to store the identity embeddings of the tracked objects.
- Motion Guided 3D Pose Estimation from Videos256
A new graph convolutional network architecture, U-shaped GCN (UGCN), which captures both short-term and long-term motion information to fully leverage the additional supervision from the motion loss.
- Alpha-CLIP: A CLIP Model Focusing on Wherever you Want223
Alpha-CLIp, an enhanced version of CLIP with an auxiliary alpha channel to suggest attentive regions and fine-tuned with constructed millions of RGBA region-text pairs, is introduced, which not only preserves the visual recognition ability of CLIP but also enables precise control over the emphasis of image contents.
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- Knowledge Guided Disambiguation for Large-Scale Scene Classification With Multi-Resolution CNNs159
A multi-resolution CNN architecture that captures visual content and structure at multiple levels is proposed and two knowledge guided disambiguation techniques to deal with the problem of label ambiguity are designed.
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- TubeR: Tubelet Transformer for Video Action Detection120
TubeR is a simple solution to directly detect an action tubelet in a video by simultaneously performing action localization and recognition from a single representation and outperforms the previous state-of-the-art on commonly used action detection datasets AVA, UCF101-24 and JHMDB51-21.
- Image and Video Tokenization with Binary Spherical Quantization119
By learning an autoregressive prior for adaptive arithmetic coding, BSQ-ViT achieves comparable results on video compression with state-of-the-art video compression standards and enables masked language models to achieve competitive image synthesis quality to GAN- and diffusion-based methods.
- Optimizing Video Object Detection via a Scale-Time Lattice117
This paper presents a unified framework that integrates detection, temporal propagation, and across-scale refinement on a Scale-Time Lattice and develops an adaptive scheme with the detector invoked on demand and thus obtain improved tradeoff.
- Towards Backward-Compatible Representation Learning110
A framework to train embedding models, called backward-compatible training (BCT), is proposed as a first step towards backward compatible representation learning, and models trained with BCT successfully achieve backward compatibility without sacrificing accuracy, thus enabling backfill-free model updates of visual embeddings.
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- Omni-Sourced Webly-Supervised Learning for Video Recognition107
Experiments show that by utilizing data from multiple sources and formats, OmniSource is more data-efficient in training, and several good practices, including data balancing, resampling, and cross-dataset mixup are adopted in joint training.
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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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