activity
20172023
most citedGCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond

214 citations · 690 across the 33 of their papers we have counts for

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Showing cs.CVShow all

43 papers · 1 filter

cs.CV20232 cited

Exploring Non-additive Randomness on ViT against Query-Based Black-Box Attacks

Jindong Gu, Fangyun Wei, Philip Torr +1

Deep Neural Networks can be easily fooled by small and imperceptible perturbations. The query-based black-box attack (QBBA) is able to create the perturbations using model output p…

cs.CV20235 cited

InstructDiffusion: A Generalist Modeling Interface for Vision Tasks

Zigang Geng, Binxin Yang, Tiankai Hang +8

We present InstructDiffusion, a unifying and generic framework for aligning computer vision tasks with human instructions. Unlike existing approaches that integrate prior knowledge…

cs.CV20234 cited

Mask-Attention-Free Transformer for 3D Instance Segmentation

Xin Lai, Yuhui Yuan, Ruihang Chu +3

Recently, transformer-based methods have dominated 3D instance segmentation, where mask attention is commonly involved. Specifically, object queries are guided by the initial insta…

cs.CV20232 cited

PartSeg: Few-shot Part Segmentation via Part-aware Prompt Learning

Mengya Han, Heliang Zheng, Chaoyue Wang +4

In this work, we address the task of few-shot part segmentation, which aims to segment the different parts of an unseen object using very few labeled examples. It is found that lev…

cs.CV2023

Rethinking the Localization in Weakly Supervised Object Localization

Rui Xu, Yong Luo, Han Hu +3

Weakly supervised object localization (WSOL) is one of the most popular and challenging tasks in computer vision. This task is to localize the objects in the images given only the…

cs.CV20235 cited

V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detection

Yichao Shen, Zigang Geng, Yuhui Yuan +6

We introduce a highly performant 3D object detector for point clouds using the DETR framework. The prior attempts all end up with suboptimal results because they fail to learn accu…