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20242026
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cs.CV2026

VEGAS: Human-Aligned Video Caption Evaluation via Gaze

Shenghui Chen, Po-han Li, Ximeng Sun +5

Vision-language models excel at video captioning, yet typically generate descriptions that fail to capture individual viewers' attention. We propose VEGAS (Video caption Evaluation…

cs.CV2026

Layout-Conditioned Autoregressive Text-to-Image Generation via Structured Masking

Zirui Zheng, Takashi Isobe, Tong Shen +12

Although autoregressive (AR) models have demonstrated remarkable success in image generation, extending these models to layout-conditioned generation remains challenging due to the…

cs.CV2026

DRIFT: Transferring Reasoning Priors for Efficient MLLM Fine-Tuning

Chao Huang, Zeliang Zhang, Jiang Liu +7

Multimodal large language models (MLLMs) have made rapid progress, yet their reasoning ability often lags behind strong text-only LLMs. Bridging this gap typically requires large-s…

cs.CV2026

CaptionQA: Is Your Caption as Useful as the Image Itself?

Shijia Yang, Yunong Liu, Bohan Zhai +5

Image captions serve as efficient surrogates for visual content in multimodal systems such as retrieval, recommendation, and multi-step agentic inference pipelines. Yet current eva…

cs.CV2026

DiffSparse: Accelerating Diffusion Transformers with Learned Token Sparsity

Haowei Zhu, Ji Liu, Ziqiong Liu +4

Diffusion models demonstrate outstanding performance in image generation, but their multi-step inference mechanism requires immense computational cost. Previous works accelerate in…

cs.CV2026

XModBench: Benchmarking Cross-Modal Capabilities and Consistency in Omni-Language Models

Xingrui Wang, Jiang Liu, Chao Huang +7

Omni-modal large language models (OLLMs) aim to unify audio, vision, and text understanding within a single framework. While existing benchmarks primarily evaluate general cross-mo…