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20242026
most citedAccelerating Non-Maximum Suppression: A Graph Theory Perspective

1 citations · 1 across the 8 of their papers we have counts for

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cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024★ 1 cited

Accelerating Non-Maximum Suppression: A Graph Theory Perspective

King-Siong Si, Lu Sun, Weizhan Zhang +4

Non-maximum suppression (NMS) is an indispensable post-processing step in object detection. With the continuous optimization of network models, NMS has become the ``last mile'' to…

cs.CV2024

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.…