Multi-focus image fusion (MFIF) aims to generate an all-in-focus image from multiple images of the same scene focused at different regions. Most existing deep learning-based methods lack explicit interaction between the source images, which limits their performance and interpretability. This paper presents a novel Clar...
Yi-Cheng Zhang, Haoyou Deng, Zhiqiang Li et al.· 0 citations
Video language models process videos as dense visual-token sequences with substantial representational redundancy. Compressing these sequences is therefore essential for reducing the visual-token burden on language-model decoding. The central challenge is to preserve visual information dispersed across frames under suc...
Wen-Ti Yin, Xiao-Tian Han, Jun-Yuan Shang et al.· 0 citations
Video anomaly detection (VAD) aims to identify and temporally localize abnormal events in videos. Supervised methods learn anomaly decision boundaries from target-domain annotations but require substantial in-domain data. Existing training-free methods leverage the rich semantic knowledge and reasoning capabilities of...
Wenti Yin, Xiang Wang, Huaxin Zhang et al.· 0 citations
TetherMem is introduced, a training-free, query-aware spatiotemporal memory router for frozen video generators that separates subject and scene queries and modulates historical access with region- and age-conditioned priors: subject queries retain identity-bearing history, while scene queries reduce reliance on subject...
Chen Li, Peng Zhang, Han-Yu Zhou et al.· 0 citations
FilmEval is introduced, a systematic evaluation framework that couples a difficulty-graded benchmark of 15 representative novels with an automated protocol of nine objective metrics spanning three dimensions: cinematic presentation, film consistency, and novel fidelity.
Jialong Zuo, Haotong Zuo, Shiwei Zhang et al.· 0 citations
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