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20152023
most citedSpatial-Temporal Transformer Networks for Traffic Flow Forecasting

269 citations · 1.8k across the 57 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

cs.CV2021★ 3 cited

Class-aware Sounding Objects Localization via Audiovisual Correspondence

Di Hu, Yake Wei, Rui Qian +3

Audiovisual scenes are pervasive in our daily life. It is commonplace for humans to discriminatively localize different sounding objects but quite challenging for machines to achie…

cs.CV2021

Exploring the Semi-supervised Video Object Segmentation Problem from a Cyclic Perspective

Yuxi Li, Ning Xu, Wenjie Yang +2

Modern video object segmentation (VOS) algorithms have achieved remarkably high performance in a sequential processing order, while most of currently prevailing pipelines still sho…

cs.CV2021★ 1 cited

LSTC: Boosting Atomic Action Detection with Long-Short-Term Context

Yuxi Li, Boshen Zhang, Jian Li +5

In this paper, we place the atomic action detection problem into a Long-Short Term Context (LSTC) to analyze how the temporal reliance among video signals affect the action detecti…

cs.CV2021

Enhancing Self-supervised Video Representation Learning via Multi-level Feature Optimization

Rui Qian, Yuxi Li, Huabin Liu +5

The crux of self-supervised video representation learning is to build general features from unlabeled videos. However, most recent works have mainly focused on high-level semantics…

cs.CV2021★ 2 cited

SiamRCR: Reciprocal Classification and Regression for Visual Object Tracking

Jinlong Peng, Zhengkai Jiang, Yueyang Gu +5

Recently, most siamese network based trackers locate targets via object classification and bounding-box regression. Generally, they select the bounding-box with maximum classificat…

cs.CV2021★ 1 cited

Variational Pedestrian Detection

Yuang Zhang, Huanyu He, Jianguo Li +3

Pedestrian detection in a crowd is a challenging task due to a high number of mutually-occluding human instances, which brings ambiguity and optimization difficulties to the curren…