Search Results for author: Abir Das

Found 14 papers, 5 papers with code

Reinforcement Explanation Learning

no code implementations26 Nov 2021 Siddhant Agarwal, Owais Iqbal, Sree Aditya Buridi, Madda Manjusha, Abir Das

Black-box methods to generate saliency maps are particularly interesting due to the fact that they do not utilize the internals of the model to explain the decision.

Image Classification Object Detection

Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing

no code implementations NeurIPS 2021 Aadarsh Sahoo, Rutav Shah, Rameswar Panda, Kate Saenko, Abir Das

Unsupervised domain adaptation which aims to adapt models trained on a labeled source domain to a completely unlabeled target domain has attracted much attention in recent years.

Contrastive Learning Unsupervised Domain Adaptation +1

Semi-Supervised Action Recognition with Temporal Contrastive Learning

1 code implementation CVPR 2021 Ankit Singh, Omprakash Chakraborty, Ashutosh Varshney, Rameswar Panda, Rogerio Feris, Kate Saenko, Abir Das

We approach this problem by learning a two-pathway temporal contrastive model using unlabeled videos at two different speeds leveraging the fact that changing video speed does not change an action.

Action Recognition Contrastive Learning

Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation

no code implementations6 Dec 2020 Aadarsh Sahoo, Rameswar Panda, Rogerio Feris, Kate Saenko, Abir Das

Partial domain adaptation which assumes that the unknown target label space is a subset of the source label space has attracted much attention in computer vision.

Partial Domain Adaptation

Mitigating Dataset Imbalance via Joint Generation and Classification

1 code implementation12 Aug 2020 Aadarsh Sahoo, Ankit Singh, Rameswar Panda, Rogerio Feris, Abir Das

In this work we address these questions from the perspective of dataset imbalance resulting out of severe under-representation of annotated training data for certain classes and its effect on both deep classification and generation methods.

Classification General Classification

Revisiting Few-shot Activity Detection with Class Similarity Control

no code implementations31 Mar 2020 Huijuan Xu, Ximeng Sun, Eric Tzeng, Abir Das, Kate Saenko, Trevor Darrell

In this paper, we present a conceptually simple and general yet novel framework for few-shot temporal activity detection based on proposal regression which detects the start and end time of the activities in untrimmed videos.

Action Detection Activity Detection +2

RISE: Randomized Input Sampling for Explanation of Black-box Models

8 code implementations19 Jun 2018 Vitali Petsiuk, Abir Das, Kate Saenko

We compare our approach to state-of-the-art importance extraction methods using both an automatic deletion/insertion metric and a pointing metric based on human-annotated object segments.

Decision Making

Weakly Supervised Summarization of Web Videos

no code implementations ICCV 2017 Rameswar Panda, Abir Das, Ziyan Wu, Jan Ernst, Amit K. Roy-Chowdhury

Casting the problem as a weakly supervised learning problem, we propose a flexible deep 3D CNN architecture to learn the notion of importance using only video-level annotation, and without any human-crafted training data.

Top-down Visual Saliency Guided by Captions

6 code implementations CVPR 2017 Vasili Ramanishka, Abir Das, Jianming Zhang, Kate Saenko

Neural image/video captioning models can generate accurate descriptions, but their internal process of mapping regions to words is a black box and therefore difficult to explain.

Video Captioning

Video Summarization in a Multi-View Camera Network

no code implementations1 Aug 2016 Rameswar Panda, Abir Das, Amit K. Roy-Chowdhury

While most existing video summarization approaches aim to extract an informative summary of a single video, we propose a novel framework for summarizing multi-view videos by exploiting both intra- and inter-view content correlations in a joint embedding space.

Video Summarization

Continuous Adaptation of Multi-Camera Person Identification Models through Sparse Non-redundant Representative Selection

no code implementations1 Jul 2016 Abir Das, Rameswar Panda, Amit K. Roy-Chowdhury

We demonstrate the effectiveness of our approach on multi-camera person re-identification datasets, to demonstrate the feasibility of learning online classification models in multi-camera big data applications.

Person Identification Person Re-Identification

Cannot find the paper you are looking for? You can Submit a new open access paper.