5 citations · 8 across the 11 of their papers we have counts for
11 papers
Everybody Tracking Every Body
Daeyun Shin, Yunhan Zhao, Shu Kong +2
We address the problem of 3D body pose estimation of multiple interacting people from their egocentric views with centralized coordination. Each individual wears a camera recording…
DSA: Dynamic Step Allocation for Fast Autoregressive Video Generation
Thanh-Tung Le, Yunhan Zhao, Menglei Chai +5
Video diffusion transformers have achieved state-of-the-art visual quality, but their high inference cost remains a major bottleneck for real-time applications. Recent distillation…
Generating a Paracosm for Training-Free Zero-Shot Composed Image Retrieval
Tong Wang, Yunhan Zhao, Shu Kong
Composed Image Retrieval (CIR) is the task of retrieving a target image from a database using a multimodal query, which consists of a reference image and a modification text. The t…
Solving Instance Detection from an Open-World Perspective
Qianqian Shen, Yunhan Zhao, Nahyun Kwon +3
Instance detection (InsDet) aims to localize specific object instances within a novel scene imagery based on given visual references. Technically, it requires proposal detection to…
Instance Tracking in 3D Scenes from Egocentric Videos
Yunhan Zhao, Haoyu Ma, Shu Kong +1
Egocentric sensors such as AR/VR devices capture human-object interactions and offer the potential to provide task-assistance by recalling 3D locations of objects of interest in th…
A High-Resolution Dataset for Instance Detection with Multi-View Instance Capture
Qianqian Shen, Yunhan Zhao, Nahyun Kwon +3
Instance detection (InsDet) is a long-lasting problem in robotics and computer vision, aiming to detect object instances (predefined by some visual examples) in a cluttered scene.…
GeoFill: Reference-Based Image Inpainting with Better Geometric Understanding
Yunhan Zhao, Connelly Barnes, Yuqian Zhou +3
Reference-guided image inpainting restores image pixels by leveraging the content from another single reference image. The primary challenge is how to precisely place the pixels fr…
Camera Pose Matters: Improving Depth Prediction by Mitigating Pose Distribution Bias
Yunhan Zhao, Shu Kong, Charless Fowlkes
Monocular depth predictors are typically trained on large-scale training sets which are naturally biased w.r.t the distribution of camera poses. As a result, trained predictors fai…
Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation
Yunhan Zhao, Shu Kong, Daeyun Shin +1
Leveraging synthetically rendered data offers great potential to improve monocular depth estimation and other geometric estimation tasks, but closing the synthetic-real domain gap…
Resisting Large Data Variations via Introspective Transformation Network
Yunhan Zhao, Ye Tian, Charless Fowlkes +2
Training deep networks that generalize to a wide range of variations in test data is essential to building accurate and robust image classifiers. One standard strategy is to apply…
Stretching Domain Adaptation: How far is too far?
Yunhan Zhao, Haider Ali, Rene Vidal
While deep learning has led to significant advances in visual recognition over the past few years, such advances often require a lot of annotated data. Unsupervised domain adaptati…