3 citations · 3 across the 4 of their papers we have counts for
4 papers · 1 filter
Generate, but Verify: Reducing Hallucination in Vision-Language Models with Retrospective Resampling
Tsung-Han Wu, Heekyung Lee, Jiaxin Ge +3
Vision-Language Models (VLMs) excel at visual understanding but often suffer from visual hallucinations, where they generate descriptions of nonexistent objects, actions, or concep…
Visual Haystacks: A Vision-Centric Needle-In-A-Haystack Benchmark
Tsung-Han Wu, Giscard Biamby, Jerome Quenum +4
Large Multimodal Models (LMMs) have made significant strides in visual question-answering for single images. Recent advancements like long-context LMMs have allowed them to ingest…
See, Say, and Segment: Teaching LMMs to Overcome False Premises
Tsung-Han Wu, Giscard Biamby, David Chan +5
Current open-source Large Multimodal Models (LMMs) excel at tasks such as open-vocabulary language grounding and segmentation but can suffer under false premises when queries imply…
Self-correcting LLM-controlled Diffusion Models
Tsung-Han Wu, Long Lian, Joseph E. Gonzalez +2
Text-to-image generation has witnessed significant progress with the advent of diffusion models. Despite the ability to generate photorealistic images, current text-to-image diffus…