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05/23/2023
TVTSv2: Learning Out-of-the-box Spatiotemporal Visual Representations at Scale
The ultimate goal for foundation models is realizing task-agnostic, i.e....
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04/17/2023
Efficient Video Action Detection with Token Dropout and Context Refinement
Streaming video clips with large-scale video tokens impede vision transf...
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04/07/2023
SparseFormer: Sparse Visual Recognition via Limited Latent Tokens
Human visual recognition is a sparse process, where only a few salient v...
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03/30/2023
Soft Neighbors are Positive Supporters in Contrastive Visual Representation Learning
Contrastive learning methods train visual encoders by comparing views fr...
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03/29/2023
VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking
Scale is the primary factor for building a powerful foundation model tha...
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03/28/2023
CycleACR: Cycle Modeling of Actor-Context Relations for Video Action Detection
The relation modeling between actors and scene context advances video ac...
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05/26/2022
AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition
Although the pre-trained Vision Transformers (ViTs) achieved great succe...
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03/23/2022
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
Pre-training video transformers on extra large-scale datasets is general...
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02/16/2022
Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations
Vision Transformers (ViTs) take all the image patches as tokens and cons...
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04/20/2021
MGSampler: An Explainable Sampling Strategy for Video Action Recognition
Frame sampling is a fundamental problem in video action recognition due ...
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12/18/2020