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20192025
most citedCvT: Introducing Convolutions to Vision Transformers

167 citations · 282 across the 10 of their papers we have counts for

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12 papers · 1 filter

cs.CV2025

Benchmarking Large and Small MLLMs

Xuelu Feng, Yunsheng Li, Dongdong Chen +4

Large multimodal language models (MLLMs) such as GPT-4V and GPT-4o have achieved remarkable advancements in understanding and generating multimodal content, showcasing superior qua…

cs.CV2022

Self-Supervised Learning based on Heat Equation

Yinpeng Chen, Xiyang Dai, Dongdong Chen +4

This paper presents a new perspective of self-supervised learning based on extending heat equation into high dimensional feature space. In particular, we remove time dependence by…

cs.CV20229 cited

Reduce Information Loss in Transformers for Pluralistic Image Inpainting

Qiankun Liu, Zhentao Tan, Dongdong Chen +6

Transformers have achieved great success in pluralistic image inpainting recently. However, we find existing transformer based solutions regard each pixel as a token, thus suffer f…

cs.CV20228 cited

MiniViT: Compressing Vision Transformers with Weight Multiplexing

Jinnian Zhang, Houwen Peng, Kan Wu +4

Vision Transformer (ViT) models have recently drawn much attention in computer vision due to their high model capability. However, ViT models suffer from huge number of parameters,…

cs.CV20213 cited

MicroNet: Improving Image Recognition with Extremely Low FLOPs

Yunsheng Li, Yinpeng Chen, Xiyang Dai +6

This paper aims at addressing the problem of substantial performance degradation at extremely low computational cost (e.g. 5M FLOPs on ImageNet classification). We found that two f…

cs.CV202164 cited

Dynamic Head: Unifying Object Detection Heads with Attentions

Xiyang Dai, Yinpeng Chen, Bin Xiao +4

The complex nature of combining localization and classification in object detection has resulted in the flourished development of methods. Previous works tried to improve the perfo…