How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
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
AAAI 2024: Visual Instruction Generation and Correction
The main features of opendatalab/vigc 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.