no code implementations • MTSummit 2021 • Kamal Gupta, Dhanvanth Boppana, Rejwanul Haque, Asif Ekbal, Pushpak Bhattacharyya
It also improves the present state-of-the-art by 0. 35 and 0. 12 BLEU points for German-English and Spanish-English and respectively.
no code implementations • MTSummit 2021 • Kamal Gupta, Soumya Chennabasavaraj, Nikesh Garera, Asif Ekbal
Given that 44% of Indian population speaks and operates in Hindi language and we address the above challenges by presenting an English–to–Hindi neural machine translation (NMT) system to translate the product reviews available on e-commerce websites by creating an in-domain parallel corpora and handling various types of noise in reviews via two data augmentation techniques and viz.
no code implementations • 10 Sep 2024 • Archana Swaminathan, Anubhav Gupta, Kamal Gupta, Shishira R. Maiya, Vatsal Agarwal, Abhinav Shrivastava
Previous works have tackled this issue by focusing on part-level reconstruction and motion estimation for objects, but they often rely on heuristics regarding the number of moving parts or object categories, which can limit their practical use.
no code implementations • 11 Jun 2024 • Shuaiyi Huang, Saksham Suri, Kamal Gupta, Sai Saketh Rambhatla, Ser-Nam Lim, Abhinav Shrivastava
To improve the quality of VIS predictions in the unsupervised setup, we introduce a dual-memory design.
1 code implementation • 1 Apr 2024 • Gowthami Somepalli, Anubhav Gupta, Kamal Gupta, Shramay Palta, Micah Goldblum, Jonas Geiping, Abhinav Shrivastava, Tom Goldstein
We also propose a method to extract style descriptors that can be used to attribute style of a generated image to the images used in the training dataset of a text-to-image model.
no code implementations • 21 Mar 2024 • Saksham Suri, Matthew Walmer, Kamal Gupta, Abhinav Shrivastava
We present a simple self-supervised method to enhance the performance of ViT features for dense downstream tasks.
1 code implementation • 7 Dec 2023 • Sharath Girish, Kamal Gupta, Abhinav Shrivastava
Our approach develops a pruning stage which results in scene representations with fewer Gaussians, leading to faster training times and rendering speeds for real-time rendering of high resolution scenes.
no code implementations • ICCV 2023 • Sharath Girish, Abhinav Shrivastava, Kamal Gupta
Implicit Neural Representations (INR) or neural fields have emerged as a popular framework to encode multimedia signals such as images and radiance fields while retaining high-quality.
no code implementations • ICCV 2023 • Nirat Saini, Hanyu Wang, Archana Swaminathan, Vinoj Jayasundara, Bo He, Kamal Gupta, Abhinav Shrivastava
Recognizing and generating object-state compositions has been a challenging task, especially when generalizing to unseen compositions.
no code implementations • ICCV 2023 • Kamal Gupta, Varun Jampani, Carlos Esteves, Abhinav Shrivastava, Ameesh Makadia, Noah Snavely, Abhishek Kar
We present a self-supervised technique that directly optimizes on a sparse collection of images of a particular object/object category to obtain consistent dense correspondences across the collection.
1 code implementation • CVPR 2023 • Matthew Walmer, Saksham Suri, Kamal Gupta, Abhinav Shrivastava
We compare ViTs trained through different methods of supervision, and show that they learn a diverse range of behaviors in terms of their attention, representations, and downstream performance.
no code implementations • 27 Oct 2022 • Mohammadhadi Mohandes, Behnam Moradi, Kamal Gupta, Mehran Mehrandezh
We present a robot-to-human object handover algorithm and implement it on a 7-DOF arm equipped with a 3-finger mechanical hand.
no code implementations • 18 Apr 2022 • Hanyu Wang, Kamal Gupta, Larry Davis, Abhinav Shrivastava
We present Neural Space-filling Curves (SFCs), a data-driven approach to infer a context-based scan order for a set of images.
1 code implementation • 6 Apr 2022 • Sharath Girish, Kamal Gupta, Saurabh Singh, Abhinav Shrivastava
We introduce LilNetX, an end-to-end trainable technique for neural networks that enables learning models with specified accuracy-rate-computation trade-off.
1 code implementation • NeurIPS 2021 • Kamal Gupta, Gowthami Somepalli, Anubhav Gupta, Vinoj Jayasundara, Matthias Zwicker, Abhinav Shrivastava
We study a referential game (a type of signaling game) where two agents communicate with each other via a discrete bottleneck to achieve a common goal.
no code implementations • 1 Jan 2021 • Kamal Gupta, Vijay Mahadevan, Alessandro Achille, Justin Lazarow, Larry S. Davis, Abhinav Shrivastava
We address the problem of scene layout generation for diverse domains such as images, mobile applications, documents and 3D objects.
1 code implementation • CVPR 2021 • Sharath Girish, Shishira R. Maiya, Kamal Gupta, Hao Chen, Larry Davis, Abhinav Shrivastava
The recently proposed Lottery Ticket Hypothesis (LTH) states that deep neural networks trained on large datasets contain smaller subnetworks that achieve on par performance as the dense networks.
1 code implementation • 7 Sep 2020 • Kamal Gupta, Susmija Jabbireddy, Ketul Shah, Abhinav Shrivastava, Matthias Zwicker
Our simple encoder-decoder framework, comprised of a novel identity encoder and class-conditional viewpoint generator, generates 3D consistent depth maps.
2 code implementations • ICCV 2021 • Kamal Gupta, Justin Lazarow, Alessandro Achille, Larry Davis, Vijay Mahadevan, Abhinav Shrivastava
Generating a new layout or extending an existing layout requires understanding the relationships between these primitives.
no code implementations • 23 Jun 2020 • Michael Hegedus, Kamal Gupta, Mehran Mehrandezh
We present a generalized grasping algorithm that uses point clouds (i. e. a group of points and their respective surface normals) to discover grasp pose solutions for multiple grasp types, executed by a mechanical gripper, in near real-time.
1 code implementation • CVPR 2020 • Kamal Gupta, Saurabh Singh, Abhinav Shrivastava
Unsupervised representation learning holds the promise of exploiting large amounts of unlabeled data to learn general representations.