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[CVPR 2025] Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention
The main features of lackel/agla are: Hallucination Mitigation, Mitigation Methods.
Projects with overlapping indexed features include: anonymousanoy/fohe — In Stage 1, the Keyword Extraction Prompt instructs ChatGPT to generate verbs, nouns, and adjectives (highlighted in… 1zhou-wang/memvr — [ICML 2025] Official implementation of paper 'Look Twice Before You Answer: Memory-Space Visual Retracing for… billchan226/halc — [ICML 2024] Official implementation for "HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding". bradyfu/woodpecker — ✨✨Woodpecker: Hallucination Correction for Multimodal Large Language Models. damo-nlp-sg/vcd — [CVPR 2024 Highlight] Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive… fuxiaoliu/lrv-instruction — [ICLR'24] Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning.
In Stage 1, the Keyword Extraction Prompt instructs ChatGPT to generate verbs, nouns, and adjectives (highlighted in brown) from the original caption. In Stage 2, the Caption Generation Prompt guides ChatGPT to generate a rewritten caption. By iteratively applying this prompt, multiple rewritten…
ICML 2025 Official implementation of paper 'Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in Multimodal Large Language Models'.
ICML 2024 Official implementation for "HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding"
✨✨Woodpecker: Hallucination Correction for Multimodal Large Language Models