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From the 1 of 16 linked papers with an AI index.

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20242026
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cs.CV2026

From Draft to Draft-Free: One-Step Video Object Removal via Privileged Distillation and Fast Planting

Zizhao Chen, Ping Wei, Guang Dai +2

The paper introduces D2DF, a one‑step video object removal framework that learns to turn coarse removal drafts into high‑quality videos via privileged distillation, and adds a self…

cs.CV2026

From SRA to Self-Flow: Data Augmentation or Self-Supervision?

Dengyang Jiang, Mengmeng Wang, Harry Yang +1

Representation alignment has become an effective way to accelerate diffusion transformer training and improve generation quality. Recent self-alignment methods, such as SRA and Sel…

cs.CV2026

IndusAgent: Reinforcing Open-Vocabulary Industrial Anomaly Detection with Agentic Tools

Rongbin Tan, Fangfang Lin, Zhenlong Yuan +10

Multimodal large language models (MLLMs) have shown remarkable capability in bridging visual perception and textual reasoning, enabling zero-shot understanding across diverse indus…

cs.CV2026

Unlocking the Potential of Grounding DINO in Videos: Parameter-Efficient Adaptation for Limited-Data Spatial-Temporal Localization

Zanyi Wang, Fan Li, Dengyang Jiang +4

Spatio-temporal video grounding (STVG) aims to localize queried objects within dynamic video segments. Prevailing fully-trained approaches are notoriously data-hungry. However, gat…

cs.CV2026

SRA 2: Variational Autoencoder Self-Representation Alignment for Efficient Diffusion Training

Mengmeng Wang, Dengyang Jiang, Liuzhuozheng Li +6

Denoising-based diffusion transformers, despite their strong generation performance, suffer from inefficient training convergence. Existing methods addressing this issue, such as R…

cs.CV2026

Deforming Videos to Masks: Flow Matching for Referring Video Segmentation

Zanyi Wang, Dengyang Jiang, Liuzhuozheng Li +6

Referring Video Object Segmentation (RVOS) requires segmenting specific objects in a video guided by a natural language description. The core challenge of RVOS is to anchor abstrac…