most citedAgent Attention: On the Integration of Softmax and Linear Attention

22 citations · 29 across the 6 of their papers we have counts for

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cs.CV2024

Training an Open-Vocabulary Monocular 3D Object Detection Model without 3D Data

Rui Huang, Henry Zheng, Yan Wang +3

Open-vocabulary 3D object detection has recently attracted considerable attention due to its broad applications in autonomous driving and robotics, which aims to effectively recogn…

cs.CV2024★ 4 cited

Dynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptation

Wangbo Zhao, Jiasheng Tang, Yizeng Han +5

Existing parameter-efficient fine-tuning (PEFT) methods have achieved significant success on vision transformers (ViTs) adaptation by improving parameter efficiency. However, the e…

cs.CV2023★ 1 cited

Mask Grounding for Referring Image Segmentation

Yong Xien Chng, Henry Zheng, Yizeng Han +2

Referring Image Segmentation (RIS) is a challenging task that requires an algorithm to segment objects referred by free-form language expressions. Despite significant progress in r…

cs.CV2023★ 2 cited

GSVA: Generalized Segmentation via Multimodal Large Language Models

Zhuofan Xia, Dongchen Han, Yizeng Han +3

Generalized Referring Expression Segmentation (GRES) extends the scope of classic RES to refer to multiple objects in one expression or identify the empty targets absent in the ima…

cs.CV2023★ 22 cited

Agent Attention: On the Integration of Softmax and Linear Attention

Dongchen Han, Tianzhu Ye, Yizeng Han +5

The attention module is the key component in Transformers. While the global attention mechanism offers high expressiveness, its excessive computational cost restricts its applicabi…