most citedDesigning a Better Asymmetric VQGAN for StableDiffusion

5 citations · 8 across the 5 of their papers we have counts for

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cs.CV20241 cited

Learning Discriminative Spatio-temporal Representations for Semi-supervised Action Recognition

Yu Wang, Sanping Zhou, Kun Xia +1

Semi-supervised action recognition aims to improve spatio-temporal reasoning ability with a few labeled data in conjunction with a large amount of unlabeled data. Albeit recent adv…

cs.CV2024

Robust Noisy Label Learning via Two-Stream Sample Distillation

Sihan Bai, Sanping Zhou, Zheng Qin +2

Noisy label learning aims to learn robust networks under the supervision of noisy labels, which plays a critical role in deep learning. Existing work either conducts sample selecti…

cs.CV2023

Single-Shot and Multi-Shot Feature Learning for Multi-Object Tracking

Yizhe Li, Sanping Zhou, Zheng Qin +3

Multi-Object Tracking (MOT) remains a vital component of intelligent video analysis, which aims to locate targets and maintain a consistent identity for each target throughout a vi…

cs.CV20235 cited

Designing a Better Asymmetric VQGAN for StableDiffusion

Zixin Zhu, Xuelu Feng, Dongdong Chen +5

StableDiffusion is a revolutionary text-to-image generator that is causing a stir in the world of image generation and editing. Unlike traditional methods that learn a diffusion mo…

cs.CV20232 cited

MotionTrack: Learning Robust Short-term and Long-term Motions for Multi-Object Tracking

Zheng Qin, Sanping Zhou, Le Wang +3

The main challenge of Multi-Object Tracking~(MOT) lies in maintaining a continuous trajectory for each target. Existing methods often learn reliable motion patterns to match the sa…