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From the 1 of 6 linked papers with an AI index.

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6 papers

cs.CV2026

Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making

Ruoyu Chen, Shangquan Sun, Xiaoqing Guo +8

The paper introduces a training approach that encodes human‑provided region priors and uses a subset‑selection attribution method to penalize models when their decision evidence fa…

cs.CV2026

Render-in-the-Loop: Vector Graphics Generation via Visual Self-Feedback

Guotao Liang, Zhangcheng Wang, Juncheng Hu +5

Multimodal Large Language Models (MLLMs) have shown promising capabilities in generating Scalable Vector Graphics (SVG) via direct code synthesis. However, existing paradigms typic…

cs.CV2026

VAnim: Rendering-Aware Sparse State Modeling for Structure-Preserving Vector Animation

Guotao Liang, Zhangcheng Wang, Chuang Wang +6

Scalable Vector Graphics (SVG) animation generation is pivotal for professional design due to their structural editability and resolution independence. However, this task remains c…

cs.CV2026

EventMemAgent: Hierarchical Event-Centric Memory for Online Video Understanding with Adaptive Tool Use

Siwei Wen, Zhangcheng Wang, Xingjian Zhang +2

Online video understanding requires models to perform continuous perception and long-range reasoning within potentially infinite visual streams. Its fundamental challenge lies in t…

cs.CV2026

TwiFF (Think With Future Frames): A Large-Scale Dataset for Dynamic Visual Reasoning

Junhua Liu, Zhangcheng Wang, Zhike Han +3

Visual Chain-of-Thought (VCoT) has emerged as a promising paradigm for enhancing multimodal reasoning by integrating visual perception into intermediate reasoning steps. However, e…

cs.CV2025

TimeLoc: A Unified End-to-End Framework for Precise Timestamp Localization in Long Videos

Chen-Lin Zhang, Lin Sui, Shuming Liu +3

Temporal localization in untrimmed videos, which aims to identify specific timestamps, is crucial for video understanding but remains challenging. This task encompasses several sub…