21 citations · 28 across the 7 of their papers we have counts for
7 papers
SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processes
Abdelhak Lemkhenter, Manchen Wang, Luca Zancato +3
In this paper we introduce SemiGPC, a distribution-aware label refinement strategy based on Gaussian Processes where the predictions of the model are derived from the labels poster…
Denoising and Selecting Pseudo-Heatmaps for Semi-Supervised Human Pose Estimation
Zhuoran Yu, Manchen Wang, Yanbei Chen +2
We propose a new semi-supervised learning design for human pose estimation that revisits the popular dual-student framework and enhances it two ways. First, we introduce a denoisin…
ScaleDet: A Scalable Multi-Dataset Object Detector
Yanbei Chen, Manchen Wang, Abhay Mittal +4
Multi-dataset training provides a viable solution for exploiting heterogeneous large-scale datasets without extra annotation cost. In this work, we propose a scalable multi-dataset…
Benchmarking Zero-Shot Recognition with Vision-Language Models: Challenges on Granularity and Specificity
Zhenlin Xu, Yi Zhu, Tiffany Deng +6
This paper presents novel benchmarks for evaluating vision-language models (VLMs) in zero-shot recognition, focusing on granularity and specificity. Although VLMs excel in tasks li…
Semi-supervised Vision Transformers at Scale
Zhaowei Cai, Avinash Ravichandran, Paolo Favaro +5
We study semi-supervised learning (SSL) for vision transformers (ViT), an under-explored topic despite the wide adoption of the ViT architectures to different tasks. To tackle this…
Multi-Object Tracking with Hallucinated and Unlabeled Videos
Daniel McKee, Bing Shuai, Andrew Berneshawi +4
In this paper, we explore learning end-to-end deep neural trackers without tracking annotations. This is important as large-scale training data is essential for training deep neura…
Combining detection and tracking for human pose estimation in videos
Manchen Wang, Joseph Tighe, Davide Modolo
We propose a novel top-down approach that tackles the problem of multi-person human pose estimation and tracking in videos. In contrast to existing top-down approaches, our method…