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20212024
most citedCross-Modal Causal Intervention for Medical Report Generation

48 citations · 141 across the 25 of their papers we have counts for

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Showing 2022Show all

10 papers · 1 filter

cs.CV2022★ 29 cited

Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning

Ziyi Zhang, Weikai Chen, Hui Cheng +4

We investigate a practical domain adaptation task, called source-free domain adaptation (SFUDA), where the source-pretrained model is adapted to the target domain without access to…

cs.CV2022★ 5 cited

DreamArtist++: Controllable One-Shot Text-to-Image Generation via Positive-Negative Adapter

Ziyi Dong, Pengxu Wei, Liang Lin

State-of-the-arts text-to-image generation models such as Imagen and Stable Diffusion Model have succeed remarkable progresses in synthesizing high-quality, feature-rich images wit…

cs.CV2022

Being Comes from Not-being: Open-vocabulary Text-to-Motion Generation with Wordless Training

Junfan Lin, Jianlong Chang, Lingbo Liu +4

Text-to-motion generation is an emerging and challenging problem, which aims to synthesize motion with the same semantics as the input text. However, due to the lack of diverse lab…

cs.CV2022★ 12 cited

Prompt-Matched Semantic Segmentation

Lingbo Liu, Jianlong Chang, Bruce X. B. Yu +3

The objective of this work is to explore how to effectively and efficiently adapt pre-trained visual foundation models to various downstream tasks of semantic segmentation. Previou…

cs.CV2022★ 3 cited

Adversarially-Aware Robust Object Detector

Ziyi Dong, Pengxu Wei, Liang Lin

Object detection, as a fundamental computer vision task, has achieved a remarkable progress with the emergence of deep neural networks. Nevertheless, few works explore the adversar…

cs.CV2022★ 8 cited

The Lottery Ticket Hypothesis for Self-attention in Convolutional Neural Network

Zhongzhan Huang, Senwei Liang, Mingfu Liang +3

Recently many plug-and-play self-attention modules (SAMs) are proposed to enhance the model generalization by exploiting the internal information of deep convolutional neural netwo…