4 accepted papers
Multi-view learning fuses complementary views to improve perception, but real-world deployments often suffer from Test-time Noisy Correspondence (TNC) -- cross-view misalignment caused by asynchronous sampling, transient network congestion, or other disturbances. Such misalignment introduces semanti
Multimodal Large Language Models (MLLMs) frequently produce hallucinations (i.e., assertions that contradict the image or facts), undermining reliability in high-risk applications. Existing detection approaches typically feed images and texts jointly and estimate hallucination scores by measuring th…
Multimodal large language models (MLLMs) extend large language models (LLMs) with visual perception for open-world understanding, but exacerbate LLMs' hallucinations, in which generated text contradicts visual evidence or common sense. To mitigate hallucinations, a dominant strategy is Direct Prefer…
Composed Image Retrieval (CIR) enables editable image search by integrating a query pair--a reference image ref and a textual modification mod--to retrieve a target image tar that reflects the intended change. While existing CIR methods have shown promising performance using well-annotated triplets…