activity
20192026
most citedSemi-Heterogeneous Three-Way Joint Embedding Network for Sketch-Based Image Retrieval

62 citations · 100 across the 26 of their papers we have counts for

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Showing 2024 · cs.CVShow all

6 papers · 2 filters

cs.CV2024

Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search

Huanjin Yao, Jiaxing Huang, Wenhao Wu +8

In this work, we aim to develop an MLLM that understands and solves questions by learning to create each intermediate step of the reasoning involved till the final answer. To this…

cs.CV2024

Explanatory Instructions: Towards Unified Vision Tasks Understanding and Zero-shot Generalization

Yang Shen, Xiu-Shen Wei, Yifan Sun +6

Computer Vision (CV) has yet to fully achieve the zero-shot task generalization observed in Natural Language Processing (NLP), despite following many of the milestones established…

cs.CV2024

DistinctAD: Distinctive Audio Description Generation in Contexts

Bo Fang, Wenhao Wu, Qiangqiang Wu +2

Audio Descriptions (ADs) aim to provide a narration of a movie in text form, describing non-dialogue-related narratives, such as characters, actions, or scene establishment. Automa…

cs.CV2024

MonoFormer: One Transformer for Both Diffusion and Autoregression

Chuyang Zhao, Yuxing Song, Wenhao Wang +5

Most existing multimodality methods use separate backbones for autoregression-based discrete text generation and diffusion-based continuous visual generation, or the same backbone…

cs.CV2024

Automated Multi-level Preference for MLLMs

Mengxi Zhang, Wenhao Wu, Yu Lu +8

Current multimodal Large Language Models (MLLMs) suffer from ``hallucination'', occasionally generating responses that are not grounded in the input images. To tackle this challeng…

cs.CV2024★ 1 cited

Dense Connector for MLLMs

Huanjin Yao, Wenhao Wu, Taojiannan Yang +7

Do we fully leverage the potential of visual encoder in Multimodal Large Language Models (MLLMs)? The recent outstanding performance of MLLMs in multimodal understanding has garner…