Search Results for author: Manish Kumar Singh

Found 6 papers, 1 papers with code

FutureDepth: Learning to Predict the Future Improves Video Depth Estimation

no code implementations19 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.

Future prediction Monocular Depth Estimation

DeCoTR: Enhancing Depth Completion with 2D and 3D Attentions

no code implementations18 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.

Depth Completion

Differentiable bit-rate estimation for neural-based video codec enhancement

no code implementations24 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.

Video Compression

Multitask Bandit Learning Through Heterogeneous Feedback Aggregation

1 code implementation29 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.

Dynamic Relational Inference in Multi-Agent Trajectories

no code implementations16 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.

Linguistic Resources for Bhojpuri, Magahi and Maithili: Statistics about them, their Similarity Estimates, and Baselines for Three Applications

no code implementations29 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.

Chunking POS +1

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