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20152023
most citedVideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training

441 citations · 2.5k across the 54 of their papers we have counts for

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Showing 2022Show all

12 papers · 1 filter

cs.CV2022★ 93 cited

InternVideo: General Video Foundation Models via Generative and Discriminative Learning

Yi Wang, Kunchang Li, Yizhuo Li +14

The foundation models have recently shown excellent performance on a variety of downstream tasks in computer vision. However, most existing vision foundation models simply focus on…

cs.CV2022

VLG: General Video Recognition with Web Textual Knowledge

Jintao Lin, Zhaoyang Liu, Wenhai Wang +2

Video recognition in an open and dynamic world is quite challenging, as we need to handle different settings such as close-set, long-tail, few-shot and open-set. By leveraging sema…

cs.CV2022★ 59 cited

UniFormerV2: Spatiotemporal Learning by Arming Image ViTs with Video UniFormer

Kunchang Li, Yali Wang, Yinan He +4

Learning discriminative spatiotemporal representation is the key problem of video understanding. Recently, Vision Transformers (ViTs) have shown their power in learning long-term v…

cs.CV2022★ 3 cited

Dynamic MDETR: A Dynamic Multimodal Transformer Decoder for Visual Grounding

Fengyuan Shi, Ruopeng Gao, Weilin Huang +1

Multimodal transformer exhibits high capacity and flexibility to align image and text for visual grounding. However, the existing encoder-only grounding framework (e.g., TransVG) s…

cs.CV2022★ 1 cited

BasicTAD: an Astounding RGB-Only Baseline for Temporal Action Detection

Min Yang, Guo Chen, Yin-Dong Zheng +2

Temporal action detection (TAD) is extensively studied in the video understanding community by generally following the object detection pipeline in images. However, complex designs…

cs.CV2022★ 5 cited

AdaMixer: A Fast-Converging Query-Based Object Detector

Ziteng Gao, Limin Wang, Bing Han +1

Traditional object detectors employ the dense paradigm of scanning over locations and scales in an image. The recent query-based object detectors break this convention by decoding…