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.
no code implementations • CVPR 2023 • Achin Jain, Gurumurthy Swaminathan, Paolo Favaro, Hao Yang, Avinash Ravichandran, Hrayr Harutyunyan, Alessandro Achille, Onkar Dabeer, Bernt Schiele, Ashwin Swaminathan, Stefano Soatto
The PPL improves the performance estimation on average by 37% across 16 classification and 33% across 10 detection datasets, compared to the power law.
no code implementations • 13 Sep 2022 • Achin Jain, Kibok Lee, Gurumurthy Swaminathan, Hao Yang, Bernt Schiele, Avinash Ravichandran, Onkar Dabeer
Combined with a matching loss, it can effectively find objects that are similar to the input patch and complete the missing annotations.
1 code implementation • 22 Jul 2022 • Kibok Lee, Hao Yang, Satyaki Chakraborty, Zhaowei Cai, Gurumurthy Swaminathan, Avinash Ravichandran, Onkar Dabeer
Most existing works on few-shot object detection (FSOD) focus on a setting where both pre-training and few-shot learning datasets are from a similar domain.
1 code implementation • CVPR 2022 • Tz-Ying Wu, Gurumurthy Swaminathan, Zhizhong Li, Avinash Ravichandran, Nuno Vasconcelos, Rahul Bhotika, Stefano Soatto
We hypothesize that a strong base model can provide a good representation for novel classes and incremental learning can be done with small adaptations.
1 code implementation • CVPR 2022 • Pei Wang, Zhaowei Cai, Hao Yang, Gurumurthy Swaminathan, Nuno Vasconcelos, Bernt Schiele, Stefano Soatto
This is enabled by a unified architecture, Omni-DETR, based on the recent progress on student-teacher framework and end-to-end transformer based object detection.
Ranked #14 on Semi-Supervised Object Detection on COCO 2% labeled data
no code implementations • 30 Apr 2020 • Ragav Venkatesan, Gurumurthy Swaminathan, Xiong Zhou, Anna Luo
We then demonstrate that if we found the profiles using a mid-sized dataset such as Cifar10/100, we are able to transfer them to even a large dataset such as Imagenet.
2 code implementations • 29 May 2019 • Xiang Xu, Xiong Zhou, Ragav Venkatesan, Gurumurthy Swaminathan, Orchid Majumder
Deep neural networks often require copious amount of labeled-data to train their scads of parameters.