no code implementations • ICCV 2023 • Haofu Liao, Aruni RoyChowdhury, Weijian Li, Ankan Bansal, Yuting Zhang, Zhuowen Tu, Ravi Kumar Satzoda, R. Manmatha, Vijay Mahadevan
We present a new formulation for structured information extraction (SIE) from visually rich documents.
Ranked #4 on
Entity Linking
on FUNSD
1 code implementation • 1 Dec 2021 • Shlok Mishra, Anshul Shah, Ankan Bansal, Abhyuday Jagannatha, Janit Anjaria, Abhishek Sharma, David Jacobs, Dilip Krishnan
This assumption is mostly satisfied in datasets such as ImageNet where there is a large, centered object, which is highly likely to be present in random crops of the full image.
1 code implementation • 3 Nov 2020 • Shlok Mishra, Anshul Shah, Ankan Bansal, Janit Anjaria, Jonghyun Choi, Abhinav Shrivastava, Abhishek Sharma, David Jacobs
Recent literature has shown that features obtained from supervised training of CNNs may over-emphasize texture rather than encoding high-level information.
Ranked #19 on
Object Detection
on PASCAL VOC 2007
1 code implementation • 16 Oct 2020 • Anshul Shah, Shlok Mishra, Ankan Bansal, Jun-Cheng Chen, Rama Chellappa, Abhinav Shrivastava
Unlike other modalities, constellation of joints and their motion generate models with succinct human motion information for activity recognition.
Ranked #1 on
Action Recognition
on Mimetics
no code implementations • ECCV 2020 • Ankan Bansal, Yuting Zhang, Rama Chellappa
To enable research in this new topic, we introduce two ISVQA datasets - indoor and outdoor scenes.
no code implementations • 9 Apr 2020 • Ankan Bansal, Sai Saketh Rambhatla, Abhinav Shrivastava, Rama Chellappa
The proposed method consists of a layout module which primes a visual module to predict the type of interaction between a human and an object.
no code implementations • 12 Oct 2019 • Prithviraj Dhar, Ankan Bansal, Carlos D. Castillo, Joshua Gleason, P. Jonathon Phillips, Rama Chellappa
In the final fully connected layer of the networks, we found the order of expressivity for facial attributes to be Age > Sex > Yaw.
no code implementations • 5 Apr 2019 • Ankan Bansal, Sai Saketh Rambhatla, Abhinav Shrivastava, Rama Chellappa
We present an approach for detecting human-object interactions (HOIs) in images, based on the idea that humans interact with functionally similar objects in a similar manner.
no code implementations • 20 Sep 2018 • Rajeev Ranjan, Ankan Bansal, Jingxiao Zheng, Hongyu Xu, Joshua Gleason, Boyu Lu, Anirudh Nanduri, Jun-Cheng Chen, Carlos D. Castillo, Rama Chellappa
We provide evaluation results of the proposed face detector on challenging unconstrained face detection datasets.
no code implementations • ECCV 2018 • Ankan Bansal, Karan Sikka, Gaurav Sharma, Rama Chellappa, Ajay Divakaran
We introduce and tackle the problem of zero-shot object detection (ZSD), which aims to detect object classes which are not observed during training.
no code implementations • 3 Apr 2018 • Rajeev Ranjan, Ankan Bansal, Hongyu Xu, Swami Sankaranarayanan, Jun-Cheng Chen, Carlos D. Castillo, Rama Chellappa
We show that integrating this simple step in the training pipeline significantly improves the performance of face verification and recognition systems.
no code implementations • 4 Jul 2017 • Sayantan Sarkar, Ankan Bansal, Upal Mahbub, Rama Chellappa
In this paper, targeted fooling of high performance image classifiers is achieved by developing two novel attack methods.
no code implementations • 21 May 2017 • Ankan Bansal, Carlos Castillo, Rajeev Ranjan, Rama Chellappa
While the research community appears to have developed a consensus on the methods of acquiring annotated data, design and training of CNNs, many questions still remain to be answered.
1 code implementation • 4 Nov 2016 • Ankan Bansal, Anirudh Nanduri, Carlos Castillo, Rajeev Ranjan, Rama Chellappa
Recent progress in face detection (including keypoint detection), and recognition is mainly being driven by (i) deeper convolutional neural network architectures, and (ii) larger datasets.
no code implementations • 30 Jul 2015 • Ankan Bansal, K. S. Venkatesh
The method estimates counts by fusing information from multiple sources.