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20222026
most citedAssessing and Understanding Creativity in Large Language Models

35 citations · 84 across the 37 of their papers we have counts for

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5 papers · 1 filter

cs.CV2024

World-Consistent Data Generation for Vision-and-Language Navigation

Yu Zhong, Rui Zhang, Zihao Zhang +9

Vision-and-Language Navigation (VLN) is a challenging task that requires an agent to navigate through photorealistic environments following natural-language instructions. One main…

cs.CV2024

Prompt-based Visual Alignment for Zero-shot Policy Transfer

Haihan Gao, Rui Zhang, Qi Yi +13

Overfitting in RL has become one of the main obstacles to applications in reinforcement learning(RL). Existing methods do not provide explicit semantic constrain for the feature ex…

cs.CV2023★ 5 cited

Learning Domain-Aware Detection Head with Prompt Tuning

Haochen Li, Rui Zhang, Hantao Yao +5

Domain adaptive object detection (DAOD) aims to generalize detectors trained on an annotated source domain to an unlabelled target domain. However, existing methods focus on reduci…

cs.CV2023

Unlearnable Examples Give a False Sense of Data Privacy: Understanding and Relearning

Pucheng Dang, Xing Hu, Kaidi Xu +5

Unlearnable examples are proposed to prevent third parties from exploiting unauthorized data, which generates unlearnable examples by adding imperceptible perturbations to public p…

cs.CV2023★ 8 cited

Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation

Zhengyue Zhao, Jinhao Duan, Xing Hu +6

Diffusion models have demonstrated remarkable performance in image generation tasks, paving the way for powerful AIGC applications. However, these widely-used generative models can…