no code implementations • 4 Apr 2024 • Zixuan Huang, Justin Johnson, Shoubhik Debnath, James M. Rehg, Chao-yuan Wu
We present PointInfinity, an efficient family of point cloud diffusion models.
10 code implementations • CVPR 2023 • Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie
This co-design of self-supervised learning techniques and architectural improvement results in a new model family called ConvNeXt V2, which significantly improves the performance of pure ConvNets on various recognition benchmarks, including ImageNet classification, COCO detection, and ADE20K segmentation.
Ranked #45 on Semantic Segmentation on ADE20K
1 code implementation • 13 Oct 2022 • Ronghang Hu, Shoubhik Debnath, Saining Xie, Xinlei Chen
Masked Autoencoding (MAE) has emerged as an effective approach for pre-training representations across multiple domains.
1 code implementation • CVPR 2021 • Luyang Zhu, Arsalan Mousavian, Yu Xiang, Hammad Mazhar, Jozef van Eenbergen, Shoubhik Debnath, Dieter Fox
Key to our approach is a local implicit neural representation built on ray-voxel pairs that allows our method to generalize to unseen objects and achieve fast inference speed.
no code implementations • ICCV 2021 • Aayush Prakash, Shoubhik Debnath, Jean-Francois Lafleche, Eric Cameracci, Gavriel State, Stan Birchfield, Marc T. Law
Synthetic data is emerging as a promising solution to the scalability issue of supervised deep learning, especially when real data are difficult to acquire or hard to annotate.
no code implementations • 28 Sep 2020 • Aayush Prakash, Shoubhik Debnath, Jean Francois Lafleche, Eric Cameracci, Gavriel State, Marc T Law
However, neural network models trained on synthetic data, do not perform well on real data because of the domain gap.
no code implementations • ICML 2020 • Weili Nie, Tero Karras, Animesh Garg, Shoubhik Debnath, Anjul Patney, Ankit B. Patel, Anima Anandkumar
Disentanglement learning is crucial for obtaining disentangled representations and controllable generation.
no code implementations • 4 Jan 2019 • Shoubhik Debnath, Lantao Liu, Gaurav Sukhatme
A new mechanism for efficiently solving the Markov decision processes (MDPs) is proposed in this paper.
no code implementations • 4 Jan 2019 • Shoubhik Debnath, Gaurav Sukhatme, Lantao Liu
Then, we leverage this approximate model along with a notion of reachability using Mean First Passage Times to perform Model-Based reinforcement learning.
no code implementations • 3 Jan 2019 • Shoubhik Debnath, Lantao Liu, Gaurav Sukhatme
The solution convergence of Markov Decision Processes (MDPs) can be accelerated by prioritized sweeping of states ranked by their potential impacts to other states.