no code implementations • 19 Mar 2024 • Rajeev Yasarla, Manish Kumar Singh, Hong Cai, Yunxiao Shi, Jisoo Jeong, Yinhao Zhu, Shizhong Han, Risheek Garrepalli, Fatih Porikli
In this paper, we propose a novel video depth estimation approach, FutureDepth, which enables the model to implicitly leverage multi-frame and motion cues to improve depth estimation by making it learn to predict the future at training.
Ranked #2 on Monocular Depth Estimation on KITTI Eigen split
no code implementations • 18 Mar 2024 • Yunxiao Shi, Manish Kumar Singh, Hong Cai, Fatih Porikli
Leveraging the initial depths and features from this network, we uplift the 2D features to form a 3D point cloud and construct a 3D point transformer to process it, allowing the model to explicitly learn and exploit 3D geometric features.
no code implementations • 24 Jan 2023 • Amir Said, Manish Kumar Singh, Reza Pourreza
Neural networks (NN) can improve standard video compression by pre- and post-processing the encoded video.
1 code implementation • 29 Oct 2020 • Zhi Wang, Chicheng Zhang, Manish Kumar Singh, Laurel D. Riek, Kamalika Chaudhuri
In many real-world applications, multiple agents seek to learn how to perform highly related yet slightly different tasks in an online bandit learning protocol.
no code implementations • 16 Jul 2020 • Ruichao Xiao, Manish Kumar Singh, Rose Yu
Neural relational inference (NRI) is a deep generative model that can reason about relations in complex dynamics without supervision.
no code implementations • 29 Apr 2020 • Rajesh Kumar Mundotiya, Manish Kumar Singh, Rahul Kapur, Swasti Mishra, Anil Kumar Singh
Corpus preparation for low-resource languages and for development of human language technology to analyze or computationally process them is a laborious task, primarily due to the unavailability of expert linguists who are native speakers of these languages and also due to the time and resources required.