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cs.CV2026

Ground, Cover, and Refine: Evidence-Centric Frame Selection for Long-Video Question Answering

Fan Wei, Siru Zhong, Runmin Dong +3

Long-video question answering requires identifying sparse yet critical evidence from videos containing thousands of frames under a constrained visual-token budget. Existing methods…

cs.CV2026

Attend, Transform, or Silence: Operator-Level Visual Skipping for Efficient Multimodal LLM Inference

Zhaoyang Luo, Runmin Dong, Miao Yang +4

Multimodal large language models (MLLMs) increasingly process long visual-token sequences, increasing the overall inference computation. Existing acceleration methods usually remov…

cs.CV2026

RS-Prune: Training-Free Data Pruning at High Ratios for Efficient Remote Sensing Diffusion Foundation Models

Fan Wei, Runmin Dong, Yushan Lai +8

Diffusion-based remote sensing (RS) generative foundation models are cruial for downstream tasks. However, these models rely on large amounts of globally representative data, which…

cs.CV2025

Alchemist: Unlocking Efficiency in Text-to-Image Model Training via Meta-Gradient Data Selection

Kaixin Ding, Yang Zhou, Xi Chen +5

Recent advances in Text-to-Image (T2I) generative models, such as Imagen, Stable Diffusion, and FLUX, have led to remarkable improvements in visual quality. However, their performa…

cs.CV2025

FilmWeaver: Weaving Consistent Multi-Shot Videos with Cache-Guided Autoregressive Diffusion

Xiangyang Luo, Qingyu Li, Xiaokun Liu +7

Current video generation models perform well at single-shot synthesis but struggle with multi-shot videos, facing critical challenges in maintaining character and background consis…

cs.CV2025

OpusAnimation: Code-Based Dynamic Chart Generation

Bozheng Li, Miao Yang, Zhenhan Chen +9

Dynamic Chart Generation (DCG) involves producing code-rendered animated visualizations as charts. While recent advances in multi-modal large language models (MLLMs) have significa…