Generate, but Verify: Reducing Hallucination in Vision-Language Models with Retrospective Resampling
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2504.13169
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David Chan
davidchan
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Vision + Language
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Generate, but Verify: Reducing Hallucination in Vision-Language Models
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TULIP: Towards Unified Language-Image Pretraining
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Multi-Stage Multi-Modal Pre-Training for Automatic Speech Recognition
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ALOHa: A New Measure for Hallucination in Captioning Models
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Virtual Personas for Language Models via an Anthology of Backstories
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Visual Haystacks: Answering Harder Questions About Sets of Images
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🚨 Launching The Visual Haystacks (VHs) Benchmark: the first "visual-centric" Needle-In-A-Haystack (NIAH) benchmark to assess LMMs' capability in long-context visual retrieval and reasoning.
Check it out!
tsunghanwu/visual_haystacks
https://visual-haystacks.github.io/
https://arxiv.org/abs/2407.13766
https://github.com/visual-haystacks/vhs_benchmark
Check it out!
tsunghanwu/visual_haystacks
https://visual-haystacks.github.io/
https://arxiv.org/abs/2407.13766
https://github.com/visual-haystacks/vhs_benchmark
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Are We Ready for Multi-Image Reasoning? Launching VHs: The Visual Haystacks Benchmark!
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