66 citations · 132 across the 10 of their papers we have counts for
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Pan-FM: A Pan-Organ Foundation Model with Saliency-Guided Masking for Missing Robustness
Qiangqiang Wu, Grace McIlvain, Zhou Yu +1
Foundation models (FMs) have shown great promise in medical imaging, but most FMs are trained on unimodal data within isolated domains, such as brain MRI alone. Human aging and dis…
M2P: Improving Visual Foundation Models with Mask-to-Point Weakly-Supervised Learning for Dense Point Tracking
Qiangqiang Wu, Tianyu Yang, Bo Fang +4
Tracking Any Point (TAP) has emerged as a fundamental tool for video understanding. Current approaches adapt Vision Foundation Models (VFMs) like DINOv2 via offline finetuning or t…
Scalable Video Object Segmentation with Simplified Framework
Qiangqiang Wu, Tianyu Yang, Wei WU +1
The current popular methods for video object segmentation (VOS) implement feature matching through several hand-crafted modules that separately perform feature extraction and match…
DropMAE: Learning Representations via Masked Autoencoders with Spatial-Attention Dropout for Temporal Matching Tasks
Qiangqiang Wu, Tianyu Yang, Ziquan Liu +3
This paper studies masked autoencoder (MAE) video pre-training for various temporal matching-based downstream tasks, i.e., object-level tracking tasks including video object tracki…
TORE: Token Reduction for Efficient Human Mesh Recovery with Transformer
Zhiyang Dou, Qingxuan Wu, Cheng Lin +5
In this paper, we introduce a set of simple yet effective TOken REduction (TORE) strategies for Transformer-based Human Mesh Recovery from monocular images. Current SOTA performanc…
A Lightweight and Detector-free 3D Single Object Tracker on Point Clouds
Yan Xia, Qiangqiang Wu, Wei Li +2
Recent works on 3D single object tracking treat the task as a target-specific 3D detection task, where an off-the-shelf 3D detector is commonly employed for the tracking. However,…
End-to-end Learning of Object Motion Estimation from Retinal Events for Event-based Object Tracking
Haosheng Chen, David Suter, Qiangqiang Wu +1
Event cameras, which are asynchronous bio-inspired vision sensors, have shown great potential in computer vision and artificial intelligence. However, the application of event came…
Asynchronous Tracking-by-Detection on Adaptive Time Surfaces for Event-based Object Tracking
Haosheng Chen, Qiangqiang Wu, Yanjie Liang +2
Event cameras, which are asynchronous bio-inspired vision sensors, have shown great potential in a variety of situations, such as fast motion and low illumination scenes. However,…
Hallucinated Adversarial Learning for Robust Visual Tracking
Qiangqiang Wu, Zhihui Chen, Lin Cheng +3
Humans can easily learn new concepts from just a single exemplar, mainly due to their remarkable ability to imagine or hallucinate what the unseen exemplar may look like in differe…
DSNet: Deep and Shallow Feature Learning for Efficient Visual Tracking
Qiangqiang Wu, Yan Yan, Yanjie Liang +2
In recent years, Discriminative Correlation Filter (DCF) based tracking methods have achieved great success in visual tracking. However, the multi-resolution convolutional feature…