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cs.CV2026

Human-Centric Image Captioning with Subject-Centered Spatial Understanding

Bozhou Li, Jiahang Zhang, Yue Ding +11

While multimodal large language models (MLLMs) achieve remarkable performance on generic image captioning, they frequently suffer from structural hallucinations in human-centric sc…

cs.CV2026

CapRiCorn-1K: A Comprehensive Benchmark for Video Captioning and Subject Referential Consistency Across Temporal Scales

Xinlong Chen, Jiafu Tang, Yue Ding +12

Accurate and comprehensive video captions with consistent subject references are critical for downstream understanding and generation tasks. However, few existing benchmarks can ob…

cs.CV2026

Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos

Yuqi Tang, Yang Shi, Zhuoran Zhang +21

Recent video generative models have greatly improved the realism of AI-generated videos, yet their outputs still exhibit artifacts such as temporal inconsistencies, structural dist…

cs.CV2026

Edit-Compass & EditReward-Compass: A Unified Benchmark for Image Editing and Reward Modeling

Xuehai Bai, Yang Shi, Yi-Fan Zhang +7

Recent image editing models have achieved remarkable progress in instruction following, multimodal understanding, and complex visual editing. However, existing benchmarks often fai…

cs.CV2026

Semantic Routing: Exploring Multi-Layer LLM Feature Weighting for Diffusion Transformers

Bozhou Li, Yushuo Guan, Haolin Li +7

Recent DiT-based text-to-image models increasingly adopt LLMs as text encoders, yet text conditioning remains largely static and often utilizes only a single LLM layer, despite pro…