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cs.CV2025
Vision Language Models Map Logos to Text via Semantic Entanglement in the Visual Projector
Sifan Li, Hongkai Chen, Yujun Cai +4
Vision Language Models (VLMs) have achieved impressive progress in multimodal reasoning; yet, they remain vulnerable to hallucinations, where outputs are not grounded in visual evi…
cs.CV2025
MME-Unify: A Comprehensive Benchmark for Unified Multimodal Understanding and Generation Models
Wulin Xie, Yi-Fan Zhang, Chaoyou Fu +6
Existing MLLM benchmarks face significant challenges in evaluating Unified MLLMs (U-MLLMs) due to: 1) lack of standardized benchmarks for traditional tasks, leading to inconsistent…
cs.CV2024
Tri-Ergon: Fine-grained Video-to-Audio Generation with Multi-modal Conditions and LUFS Control
Bingliang Li, Fengyu Yang, Yuxin Mao +3
Video-to-audio (V2A) generation utilizes visual-only video features to produce realistic sounds that correspond to the scene. However, current V2A models often lack fine-grained co…