4 citations · 4 across the 19 of their papers we have counts for
20 papers
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…
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…
TriCLIP-3D: A Unified Parameter-Efficient Framework for Tri-Modal 3D Visual Grounding based on CLIP
Fan Li, Zanyi Wang, Zeyi Huang +3
3D visual grounding allows an embodied agent to understand visual information in real-world 3D environments based on human instructions, which is crucial for embodied intelligence.…
No Other Representation Component Is Needed: Diffusion Transformers Can Provide Representation Guidance by Themselves
Dengyang Jiang, Mengmeng Wang, Liuzhuozheng Li +6
Recent studies have demonstrated that learning a meaningful internal representation can accelerate generative training. However, existing approaches necessitate to either introduce…
Visual Object Tracking across Diverse Data Modalities: A Review
Mengmeng Wang, Teli Ma, Shuo Xin +5
Visual Object Tracking (VOT) is an attractive and significant research area in computer vision, which aims to recognize and track specific targets in video sequences where the targ…
Low-Biased General Annotated Dataset Generation
Dengyang Jiang, Haoyu Wang, Lei Zhang +5
Pre-training backbone networks on a general annotated dataset (e.g., ImageNet) that comprises numerous manually collected images with category annotations has proven to be indispen…
Schedule Your Edit: A Simple yet Effective Diffusion Noise Schedule for Image Editing
Haonan Lin, Mengmeng Wang, Jiahao Wang +7
Text-guided diffusion models have significantly advanced image editing, enabling high-quality and diverse modifications driven by text prompts. However, effective editing requires…
Flipped Classroom: Aligning Teacher Attention with Student in Generalized Category Discovery
Haonan Lin, Wenbin An, Jiahao Wang +6
Recent advancements have shown promise in applying traditional Semi-Supervised Learning strategies to the task of Generalized Category Discovery (GCD). Typically, this involves a t…
SpotActor: Training-Free Layout-Controlled Consistent Image Generation
Jiahao Wang, Caixia Yan, Weizhan Zhang +6
Text-to-image diffusion models significantly enhance the efficiency of artistic creation with high-fidelity image generation. However, in typical application scenarios like comic b…
OneActor: Consistent Character Generation via Cluster-Conditioned Guidance
Jiahao Wang, Caixia Yan, Haonan Lin +5
Text-to-image diffusion models benefit artists with high-quality image generation. Yet their stochastic nature hinders artists from creating consistent images of the same subject.…
DreamSalon: A Staged Diffusion Framework for Preserving Identity-Context in Editable Face Generation
Haonan Lin, Mengmeng Wang, Yan Chen +6
While large-scale pre-trained text-to-image models can synthesize diverse and high-quality human-centered images, novel challenges arise with a nuanced task of "identity fine editi…
M2-CLIP: A Multimodal, Multi-task Adapting Framework for Video Action Recognition
Mengmeng Wang, Jiazheng Xing, Boyuan Jiang +6
Recently, the rise of large-scale vision-language pretrained models like CLIP, coupled with the technology of Parameter-Efficient FineTuning (PEFT), has captured substantial attrac…
Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition
Chengyou Jia, Minnan Luo, Xiaojun Chang +6
Exploring open-vocabulary video action recognition is a promising venture, which aims to recognize previously unseen actions within any arbitrary set of categories. Existing method…
SSMG: Spatial-Semantic Map Guided Diffusion Model for Free-form Layout-to-Image Generation
Chengyou Jia, Minnan Luo, Zhuohang Dang +4
Despite significant progress in Text-to-Image (T2I) generative models, even lengthy and complex text descriptions still struggle to convey detailed controls. In contrast, Layout-to…