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20172023
most citedData Augmentation in Emotion Classification Using Generative Adversarial Networks

78 citations · 100 across the 18 of their papers we have counts for

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32 papers · 1 filter

cs.CV2023

Dynamic Token Pruning in Plain Vision Transformers for Semantic Segmentation

Quan Tang, Bowen Zhang, Jiajun Liu +2

Vision transformers have achieved leading performance on various visual tasks yet still suffer from high computational complexity. The situation deteriorates in dense prediction ta…

cs.CV20236 cited

BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset

Biao Wu, Yutong Xie, Zeyu Zhang +6

Intracranial hemorrhage (ICH) is a pathological condition characterized by bleeding inside the skull or brain, which can be attributed to various factors. Identifying, localizing a…

cs.CV20233 cited

Object Detection Difficulty: Suppressing Over-aggregation for Faster and Better Video Object Detection

Bingqing Zhang, Sen Wang, Yifan Liu +3

Current video object detection (VOD) models often encounter issues with over-aggregation due to redundant aggregation strategies, which perform feature aggregation on every frame.…

cs.CV20231 cited

SegPrompt: Boosting Open-world Segmentation via Category-level Prompt Learning

Muzhi Zhu, Hengtao Li, Hao Chen +5

Current closed-set instance segmentation models rely on pre-defined class labels for each mask during training and evaluation, largely limiting their ability to detect novel object…

cs.CV20231 cited

Category Feature Transformer for Semantic Segmentation

Quan Tang, Chuanjian Liu, Fagui Liu +5

Aggregation of multi-stage features has been revealed to play a significant role in semantic segmentation. Unlike previous methods employing point-wise summation or concatenation f…

cs.CV20231 cited

CTVIS: Consistent Training for Online Video Instance Segmentation

Kaining Ying, Qing Zhong, Weian Mao +7

The discrimination of instance embeddings plays a vital role in associating instances across time for online video instance segmentation (VIS). Instance embedding learning is direc…