no code implementations • CVPR 2020 • Manchen Wang, Joseph Tighe, Davide Modolo
Our approach consists of three components: (i) a Clip Tracking Network that performs body joint detection and tracking simultaneously on small video clips; (ii) a Video Tracking Pipeline that merges the fixed-length tracklets produced by the Clip Tracking Network to arbitrary length tracks; and (iii) a Spatial-Temporal Merging procedure that refines the joint locations based on spatial and temporal smoothing terms.
Ranked #1 on Pose Tracking on PoseTrack2017
no code implementations • 19 Aug 2021 • Daniel McKee, Bing Shuai, Andrew Berneshawi, Manchen Wang, Davide Modolo, Svetlana Lazebnik, Joseph Tighe
Next, to tackle harder tracking cases, we mine hard examples across an unlabeled pool of real videos with a tracker trained on our hallucinated video data.
1 code implementation • 11 Aug 2022 • Zhaowei Cai, Avinash Ravichandran, Paolo Favaro, Manchen Wang, Davide Modolo, Rahul Bhotika, Zhuowen Tu, Stefano Soatto
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.
no code implementations • CVPR 2023 • Yanbei Chen, Manchen Wang, Abhay Mittal, Zhenlin Xu, Paolo Favaro, Joseph Tighe, Davide Modolo
Our results show that ScaleDet achieves compelling strong model performance with an mAP of 50. 7 on LVIS, 58. 8 on COCO, 46. 8 on Objects365, 76. 2 on OpenImages, and 71. 8 on ODinW, surpassing state-of-the-art detectors with the same backbone.
Ranked #1 on Object Detection on OpenImages-v6 (using extra training data)
no code implementations • 28 Jun 2023 • Zhenlin Xu, Yi Zhu, Tiffany Deng, Abhay Mittal, Yanbei Chen, Manchen Wang, Paolo Favaro, Joseph Tighe, Davide Modolo
This paper introduces innovative benchmarks to evaluate Vision-Language Models (VLMs) in real-world zero-shot recognition tasks, focusing on the granularity and specificity of prompting text.
no code implementations • 29 Sep 2023 • Zhuoran Yu, Manchen Wang, Yanbei Chen, Paolo Favaro, Davide Modolo
First, we introduce a denoising scheme to generate reliable pseudo-heatmaps as targets for learning from unlabeled data.
no code implementations • 3 Nov 2023 • Abdelhak Lemkhenter, Manchen Wang, Luca Zancato, Gurumurthy Swaminathan, Paolo Favaro, Davide Modolo
We show that SemiGPC improves performance when paired with different Semi-Supervised methods such as FixMatch, ReMixMatch, SimMatch and FreeMatch and different pre-training strategies including MSN and Dino.