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20232026
most citedShareGPT4V: Improving Large Multi-Modal Models with Better Captions

12 citations · 46 across the 37 of their papers we have counts for

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cs.CV2026

HPSD: Hybrid-Policy Self-Distillation for Text-Image-to-Video Diffusion Models

Jiazi Bu, Pengyang Ling, Yujie Zhou +10

Text-Image-to-Video (TI2V) models are an emerging unified architecture, where a single model simultaneously supports text-to-video (T2V) and image-to-video (I2V) generation. Given…

cs.CV2026

Beyond the Current Observation: Evaluating Multimodal Large Language Models in Controllable Non-Markov Games

Shengyuan Ding, Xilin Wei, Xinyu Fang +4

Deploying multimodal foundation models as closed-loop policies increasingly requires conditioning actions on observations that are no longer visible. However, existing benchmarks e…

cs.CV2026

From Sparse to Dense: Multi-View GRPO for Flow Models via Augmented Condition Space

Jiazi Bu, Pengyang Ling, Yujie Zhou +8

Group Relative Policy Optimization (GRPO) has emerged as a powerful framework for preference alignment in text-to-image (T2I) flow models. However, we observe that the standard par…

cs.CV2026

Visual-ERM: Reward Modeling for Visual Equivalence

Ziyu Liu, Shengyuan Ding, Xinyu Fang +7

Vision-to-code tasks require models to reconstruct structured visual inputs, such as charts, tables, and SVGs, into executable or structured representations with high visual fideli…

cs.CV2026

Visual Self-Refine: A Pixel-Guided Paradigm for Accurate Chart Parsing

Jinsong Li, Xiaoyi Dong, Yuhang Zang +3

While Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities for reasoning and self-correction at the textual level, these strengths provide minimal benefit…

cs.CV2026

Demo-ICL: In-Context Learning for Procedural Video Knowledge Acquisition

Yuhao Dong, Shulin Tian, Shuai Liu +6

Despite the growing video understanding capabilities of recent Multimodal Large Language Models (MLLMs), existing video benchmarks primarily assess understanding based on models' s…