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20212024
most citedDelving into the Continuous Domain Adaptation

7 citations · 16 across the 10 of their papers we have counts for

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9 papers · 1 filter

cs.CV20241 cited

Semantic-Aligned Adversarial Evolution Triangle for High-Transferability Vision-Language Attack

Xiaojun Jia, Sensen Gao, Qing Guo +6

Vision-language pre-training (VLP) models excel at interpreting both images and text but remain vulnerable to multimodal adversarial examples (AEs). Advancing the generation of tra…

cs.CV20244 cited

ResVG: Enhancing Relation and Semantic Understanding in Multiple Instances for Visual Grounding

Minghang Zheng, Jiahua Zhang, Qingchao Chen +2

Visual grounding aims to localize the object referred to in an image based on a natural language query. Although progress has been made recently, accurately localizing target objec…

cs.CV2024

Exploring Conditional Multi-Modal Prompts for Zero-shot HOI Detection

Ting Lei, Shaofeng Yin, Yuxin Peng +1

Zero-shot Human-Object Interaction (HOI) detection has emerged as a frontier topic due to its capability to detect HOIs beyond a predefined set of categories. This task entails not…

cs.CV2024

Bridging the Gap between 2D and 3D Visual Question Answering: A Fusion Approach for 3D VQA

Wentao Mo, Yang Liu

In 3D Visual Question Answering (3D VQA), the scarcity of fully annotated data and limited visual content diversity hampers the generalization to novel scenes and 3D concepts (e.g.…

cs.CV2023

Efficient Adaptive Human-Object Interaction Detection with Concept-guided Memory

Ting Lei, Fabian Caba, Qingchao Chen +3

Human Object Interaction (HOI) detection aims to localize and infer the relationships between a human and an object. Arguably, training supervised models for this task from scratch…

cs.CV2023

Weakly Supervised Vision-and-Language Pre-training with Relative Representations

Chi Chen, Peng Li, Maosong Sun +1

Weakly supervised vision-and-language pre-training (WVLP), which learns cross-modal representations with limited cross-modal supervision, has been shown to effectively reduce the d…