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20192026
most citedGenerative adversarial networks in time series: A survey and taxonomy

46 citations · 118 across the 21 of their papers we have counts for

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Showing 2021 · cs.CVShow all

9 papers · 2 filters

cs.CV2021★ 3 cited

Background-aware Classification Activation Map for Weakly Supervised Object Localization

Lei Zhu, Qi She, Qian Chen +9

Weakly supervised object localization (WSOL) relaxes the requirement of dense annotations for object localization by using image-level classification masks to supervise its learnin…

cs.CV2021★ 9 cited

Learning from Temporal Gradient for Semi-supervised Action Recognition

Junfei Xiao, Longlong Jing, Lin Zhang +5

Semi-supervised video action recognition tends to enable deep neural networks to achieve remarkable performance even with very limited labeled data. However, existing methods are m…

cs.CV2021★ 4 cited

TEAM-Net: Multi-modal Learning for Video Action Recognition with Partial Decoding

Zhengwei Wang, Qi She, Aljosa Smolic

Most of existing video action recognition models ingest raw RGB frames. However, the raw video stream requires enormous storage and contains significant temporal redundancy. Video…

cs.CV2021★ 4 cited

3rd Place Solution to Google Landmark Recognition Competition 2021

Cheng Xu, Weimin Wang, Shuai Liu +6

In this paper, we show our solution to the Google Landmark Recognition 2021 Competition. Firstly, embeddings of images are extracted via various architectures (i.e. CNN-, Transform…

cs.CV2021

MT-ORL: Multi-Task Occlusion Relationship Learning

Panhe Feng, Qi She, Lei Zhu +7

Retrieving occlusion relation among objects in a single image is challenging due to sparsity of boundaries in image. We observe two key issues in existing works: firstly, lack of a…

cs.CV2021★ 1 cited

Inter-intra Variant Dual Representations forSelf-supervised Video Recognition

Lin Zhang, Qi She, Zhengyang Shen +1

Contrastive learning applied to self-supervised representation learning has seen a resurgence in deep models. In this paper, we find that existing contrastive learning based soluti…