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20162023
most citedPyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition

139 citations · 1.4k across the 92 of their papers we have counts for

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Showing 2021 · cs.CVShow all

51 papers · 2 filters

cs.CV2021

GuidedMix-Net: Semi-supervised Semantic Segmentation by Using Labeled Images as Reference

Peng Tu, Yawen Huang, Feng Zheng +3

Semi-supervised learning is a challenging problem which aims to construct a model by learning from limited labeled examples. Numerous methods for this task focus on utilizing the p…

cs.CV2021★ 6 cited

TransZero++: Cross Attribute-Guided Transformer for Zero-Shot Learning

Shiming Chen, Ziming Hong, Wenjin Hou +6

Zero-shot learning (ZSL) tackles the novel class recognition problem by transferring semantic knowledge from seen classes to unseen ones. Existing attention-based models have strug…

cs.CV2021★ 84 cited

HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning

Shiming Chen, Guo-Sen Xie, Yang Liu +5

Zero-shot learning (ZSL) tackles the unseen class recognition problem, transferring semantic knowledge from seen classes to unseen ones. Typically, to guarantee desirable knowledge…

cs.CV2021★ 1 cited

Light Field Saliency Detection with Dual Local Graph Learning andReciprocative Guidance

Nian Liu, Wangbo Zhao, Dingwen Zhang +2

The application of light field data in salient object de-tection is becoming increasingly popular recently. The diffi-culty lies in how to effectively fuse the features within the…

cs.CV2021

Summarize and Search: Learning Consensus-aware Dynamic Convolution for Co-Saliency Detection

Ni Zhang, Junwei Han, Nian Liu +1

Humans perform co-saliency detection by first summarizing the consensus knowledge in the whole group and then searching corresponding objects in each image. Previous methods usuall…

cs.CV2021★ 20 cited

Full-Duplex Strategy for Video Object Segmentation

Ge-Peng Ji, Deng-Ping Fan, Keren Fu +3

Previous video object segmentation approaches mainly focus on using simplex solutions between appearance and motion, limiting feature collaboration efficiency among and across thes…