most citedWoodpecker: Hallucination Correction for Multimodal Large Language Models

93 citations · 136 across the 16 of their papers we have counts for

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cs.CV2023

Bi-discriminator Domain Adversarial Neural Networks with Class-Level Gradient Alignment

Chuang Zhao, Hongke Zhao, Hengshu Zhu +4

Unsupervised domain adaptation aims to transfer rich knowledge from the annotated source domain to the unlabeled target domain with the same label space. One prevalent solution is…

cs.CV202393 cited

Woodpecker: Hallucination Correction for Multimodal Large Language Models

Shukang Yin, Chaoyou Fu, Sirui Zhao +7

Hallucination is a big shadow hanging over the rapidly evolving Multimodal Large Language Models (MLLMs), referring to the phenomenon that the generated text is inconsistent with t…

cs.CV2023

A Solution to CVPR'2023 AQTC Challenge: Video Alignment for Multi-Step Inference

Chao Zhang, Shiwei Wu, Sirui Zhao +2

Affordance-centric Question-driven Task Completion (AQTC) for Egocentric Assistant introduces a groundbreaking scenario. In this scenario, through learning instructional videos, AI…

cs.CV20235 cited

Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation

Likang Wu, Zhi Li, Hongke Zhao +5

Zero-Shot Learning (ZSL), which aims at automatically recognizing unseen objects, is a promising learning paradigm to understand new real-world knowledge for machines continuously.…

cs.CV2023736 cited

A Survey on Multimodal Large Language Models

Shukang Yin, Chaoyou Fu, Sirui Zhao +4

Recently, Multimodal Large Language Model (MLLM) represented by GPT-4V has been a new rising research hotspot, which uses powerful Large Language Models (LLMs) as a brain to perfor…

cs.CV20232 cited

AU-aware graph convolutional network for Macro- and Micro-expression spotting

Shukang Yin, Shiwei Wu, Tong Xu +3

Automatic Micro-Expression (ME) spotting in long videos is a crucial step in ME analysis but also a challenging task due to the short duration and low intensity of MEs. When solvin…