4 citations · 4 across the 19 of their papers we have counts for
7 papers · 1 filter
From Draft to Draft-Free: One-Step Video Object Removal via Privileged Distillation and Fast Planting
Zizhao Chen, Ping Wei, Guang Dai +2
Video object removal is a fundamental yet challenging task in video editing. Despite recent progress, existing methods typically fall into two categories. Traditional approaches ba…
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…
PA-BiCoop: A Primary-Auxiliary Cooperative Framework for General Bimanual Manipulation
Bai Qicheng, Wang Ziru, Ma Teli +3
Bimanual manipulation is essential for advanced robotic systems because it offers higher efficiency and flexibility compared to single-arm configurations. However, existing approac…
IndusAgent: Agentic Reinforcement Learning for Cross-Category Industrial Anomaly Detection
Rongbin Tan*, Rongbin Tan, Fangfang Lin* +16
Small defects in unfamiliar product categories can be difficult to distinguish from benign appearance variations in a global view. Additional reasoning over the same view may leave…
Exploring Time Conditioning in Diffusion Generative Models from Disjoint Noisy Data Manifolds
Liuzhuozheng Li, Zhiyuan Zhan, Shuhong Liu +5
Practically, training diffusion models typically requires explicit time conditioning to guide the network through the denoising sampling process. Especially in deterministic method…
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…
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…