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20202026
most citedVisual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings

10 citations · 45 across the 20 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cs.CV2023★ 1 cited

Learning Concise and Descriptive Attributes for Visual Recognition

An Yan, Yu Wang, Yiwu Zhong +6

Recent advances in foundation models present new opportunities for interpretable visual recognition -- one can first query Large Language Models (LLMs) to obtain a set of attribute…

cs.IR2023

Pretrained Language Model based Web Search Ranking: From Relevance to Satisfaction

Canjia Li, Xiaoyang Wang, Dongdong Li +6

Search engine plays a crucial role in satisfying users' diverse information needs. Recently, Pretrained Language Models (PLMs) based text ranking models have achieved huge success…

cs.CV2023★ 8 cited

LLMScore: Unveiling the Power of Large Language Models in Text-to-Image Synthesis Evaluation

Yujie Lu, Xianjun Yang, Xiujun Li +2

Existing automatic evaluation on text-to-image synthesis can only provide an image-text matching score, without considering the object-level compositionality, which results in poor…

cs.CL2023★ 4 cited

Multimodal Procedural Planning via Dual Text-Image Prompting

Yujie Lu, Pan Lu, Zhiyu Chen +3

Embodied agents have achieved prominent performance in following human instructions to complete tasks. However, the potential of providing instructions informed by texts and images…

cs.CL2023★ 1 cited

Collaborative Generative AI: Integrating GPT-k for Efficient Editing in Text-to-Image Generation

Wanrong Zhu, Xinyi Wang, Yujie Lu +4

The field of text-to-image (T2I) generation has garnered significant attention both within the research community and among everyday users. Despite the advancements of T2I models,…

cs.CL2023★ 10 cited

Visual Chain of Thought: Bridging Logical Gaps with Multimodal Infillings

Daniel Rose, Vaishnavi Himakunthala, Andy Ouyang +7

Recent advances in large language models elicit reasoning in a chain-of-thought that allows models to decompose problems in a human-like fashion. Though this paradigm improves mult…