Rameswar Panda

I am a research staff member at MIT-IBM Watson AI Lab, where I work on computer vision and machine learning.

I received my Ph.D. from UC Riverside in 2018, under the supervision of Prof. Amit K. Roy-Chowdhury. During Ph.D., I was very fortunate to have interned at NEC Labs, Adobe Research and Siemens Research.

Email  /  CV  /  Google Scholar  /  LinkedIn

profile photo
Research

My research interests mainly lie in the areas of computer vision and machine learning. In particular, my current focus is on making AI systems more efficient, i.e., developing novel deep learning methods that can operate with less human-annotated data (data efficient), and less computation (model efficient). I am also interested in image/video understanding, unsupervised/self-supervised representation learning and multimodal learning (e.g., combining vision and language).

News
Publications
AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition
Rameswar Panda*, Chun-Fu (Richard) Chen*, Quanfu Fan, Ximeng Sun, Kate Saenko, Aude Oliva, Rogerio Feris
International Conference on Computer Vision (ICCV), 2021
[Project Page] [Code] [Supplementary Material]

We propose an adaptive multi-modal learning framework that selects on-the-fly the optimal modalities for each segment conditioned on the input for efficient video recognition.


Dynamic Network Quantization for Efficient Video Inference
Ximeng Sun, Rameswar Panda, Chun-Fu (Richard) Chen, Aude Oliva, Rogerio Feris, Kate Saenko
International Conference on Computer Vision (ICCV), 2021
[Project Page] [Code] [Supplementary Material]

We introduce video instance-aware quantization that decides what precision should be used on a per frame basis for efficient video inference.


CrossViT: Cross Attention Multi-Scale Vision Transformer for Image Classification
Chun-Fu (Richard) Chen, Quanfu Fan, Rameswar Panda
International Conference on Computer Vision (ICCV), 2021 (Oral)
[Code] [Supplementary Material]

We develop a dual-branch vision transformer by combining image patches of different sizes to extract multi-scale feature representations for image classification.


A Broad Study on the Transferability of Visual Representations with Contrastive Learning
Ashraful Islam, Chun-Fu (Richard) Chen, Rameswar Panda, Leonid Karlinsky, Richard Radke, Rogerio Feris
International Conference on Computer Vision (ICCV), 2021
[Code] [Supplementary Material]

We conduct extensive analysis on the transferability of contrastive learning on the downstream image classification, few-shot recognition, and object detection tasks.


Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos
Brian Chen, Andrew Rouditchenko, Kevin Duarte, Hilde Kuehne, Samuel Thomas, Angie Boggust, Rameswar Panda, Brian Kingsbury, Rogerio Feris, David Harwath, James Glass, Michael Picheny, Shih-Fu Chang
International Conference on Computer Vision (ICCV), 2021

We extend the concept of instance-level contrastive learning with a multimodal clustering step in the training pipeline to capture semantic similarities across modalities.


Detector-Free Weakly Supervised Grounding by Separation
Assaf Arbelle, Sivan Doveh, Amit Alfassy, Joseph Shtok, Guy Lev, Eli Schwartz, Hilde Kuehne, Hila Barak Levi, Prasanna Sattigeri, Rameswar Panda, Chun-Fu (Richard) Chen, Alex Bronstein, Kate Saenko, Shimon Ullman, Raja Giryes, Rogerio Feris, Leonid Karlinsky
International Conference on Computer Vision (ICCV), 2021 (Oral)

We propose a detector-free approach for weakly supervised grounding by learning to separate randomly blended images conditioned on the corresponding texts.


Fair Selective Classification Via Sufficiency
Joshua Lee, Yuheng Bu, Deepta Rajan, Prasanna Sattigeri, Rameswar Panda, Subhro Das, Gregory Wornell
International Conference on Machine Learning (ICML), 2021 (Oral)

We prove that sufficiency can be used to train fairer selective classifiers which ensure that precision always increases as coverage is decreased for all groups.


Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition
Chun-Fu (Richard) Chen*, Rameswar Panda*, Kandan Ramakrishnan, Rogerio Feris, John Cohn, Aude Oliva, Quanfu Fan*
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021
[Code] [Supplementary Material]

We develop a unified framework for action recognition models and systematically compare them to better understand the differences and spatio-temporal behavior on large-scale datasets.


Semi-Supervised Action Recognition with Temporal Contrastive Learning
Ankit Singh, Omprakash Chakraborty, Ashutosh Varshney, Rameswar Panda, Rogerio Feris, Kate Saenko, Abir Das
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021
[Project Page] [Code] [Supplementary Material]

We propose a temporal contrastive learning framework for semi-supervised action recognition by using contrastive losses between different videos and groups of videos with similar actions.


A Simple Framework for Cross-Domain Few-Shot Recognition with Unlabeled Data
Ashraful Islam, Richard Chen, Rameswar Panda, Leonid Karlinsky, Rogerio Feris, Richard Radke
CVPR Workshop on Learning from Limited or Imperfect Data (CVPR-W), 2021

We introduce a simple pre-training framework to utilize unlabeled data from the target domain for cross-domain few-shot learning.


AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition
Yue Meng, Rameswar Panda, Chung-Ching Lin, Prasanna Sattigeri, Leonid Karlinsky, Kate Saenko, Aude Oliva, Rogerio Feris
International Conference on Learning Representations (ICLR), 2021
[Project Page] [Code]

We introduce an adaptive temporal fusion network that dynamically fuses channels from current and past feature maps for strong temporal modelling in action recognition.


VA-RED2: Video Adaptive Redundancy Reduction
Bowen Pan, Rameswar Panda, Camilo Fosco, Chung-Ching Lin, Alex Andonian, Yue Meng, Kate Saenko, Aude Oliva, Rogerio Feris
International Conference on Learning Representations (ICLR), 2021
[Project Page] [Code]

We propose an input-dependent adaptive framework for efficient video recognition that automatically decides what feature maps to compute per input instance.


NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search
Rameswar Panda, Michele Merler, Mayoore Jaiswal, Hui Wu, Kandan Ramakrishnan, Ulrich Finkler, Richard Chen, Minsik Cho, Rogerio Feris, David Kung, Bishwaranjan Bhattacharjee
AAAI Conference on Artificial Intelligence (AAAI), 2021

We analyze the architecture transferability of different NAS methods by performing a series of experiments on several large scale image benchmarks.


Large Scale Neural Architecture Search with Polyharmonic Splines
Ulrich Finkler, Michele Merler, Rameswar Panda, Mayoore Jaiswal, Hui Wu, Kandan Ramakrishnan, Chun-Fu Chen, Minsik Cho, David Kung, Rogerio Feris, Bishwaranjan Bhattacharjee
AAAI Workshop on Meta-Learning for Computer Vision (AAAI-W), 2021

We propose a NAS method based on polyharmonic splines that can perform architecture search directly on large scale image datasets like ImageNet22K.


AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning
Ximeng Sun, Rameswar Panda, Rogerio Feris, Kate Saenko
Neural Information Processing Systems (NeurIPS), 2020
[Project Page] [Code] [Supplementary Material]

We propose a novel approach for adaptively determining the feature sharing pattern across multiple tasks (what layers to share across which tasks) in deep multi-task learning.


Exploiting Global Camera Network Constraints for Unsupervised Video Person Re-identification
Xueping Wang, Rameswar Panda, Min Liu, Yaonan Wang, Amit K. Roy-Chowdhury
IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2020

We propose a consistent cross-view matching framework, in which global camera network constraints are exploited to address the problem of unsupervised video-based re-identification.


Adversarial Knowledge Transfer from Unlabeled Data
Akash Gupta*, Rameswar Panda*, Sujoy Paul, Jianming Zhang, Amit K. Roy-Chowdhury
ACM Multimedia (MM), 2020
[Project Page] [Code]

We present a novel adversarial framework for transferring knowledge from internet-scale unlabeled data to improve the performance of a classifier on a given visual recognition task.


AR-Net: Adaptive Frame Resolution for Efficient Action Recognition
Yue Meng, Chung-Ching Lin, Rameswar Panda, Prasanna Sattigeri, Leonid Karlinsky, Aude Oliva, Kate Saenko, Rogerio Feris
European Conference on Computer Vision (ECCV), 2020
[Project Page] [Code]

We propose an adaptive approach to select optimal resolution for each frame conditioned on the input for efficient action recognition in long untrimmed video.


Mitigating Dataset Imbalance via Joint Generation and Classification
Aadarsh Sahoo, Ankit Singh, Rameswar Panda, Rogerio Feris, Abir Das
ECCV Workshop on Imbalance Problems in Computer Vision (ECCV-W), 2020

We introduce a joint dataset repairment strategy by combining classifier with a GAN that makes up for the deficit of training examples from the minority class by producing additional examples.


Fairness of Classifiers Across Skin Tones in Dermatology
Newton M. Kinyanjui, Timothy Odonga, Celia Cintas, Noel C. F. Codella, Rameswar Panda, Prasanna Sattigeri, Kush R. Varshney
Medical Image Computing and Computer Assisted Interventions (MICCAI), 2020

We present an approach to estimate the consistency in performance of classifiers across populations with varying skin tones in skin disease benchmarks.


Non-Adversarial Video Synthesis with Learned Priors
Abhishek Aich, Akash Gupta, Rameswar Panda, Rakib Hyder, Salman Asif, Amit K. Roy-Chowdhury
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020
[Project Page] [Code]

We introduce a novel non-adversarial framework for generating a wide range of diverse videos from latent noise vectors without any any conditional input reference frame.


Camera On-boarding for Person Re-identification using Hypothesis Transfer Learning
Sk Miraj Ahmed, Aske R. Lejbolle, Rameswar Panda, Amit K. Roy-Chowdhury
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020

We propose an approach to swiftly on-board new camera(s) in an existing re-id network using only source models and limited labeled data, but without having access to source camera data.


Relationship Matters: Relation Guided Knowledge Transfer for Incremental Learning of Object Detectors
Kandan Ramakrishnan, Rameswar Panda, Quanfu Fan, John Henning, Aude Oliva, Rogerio Feris
CVPR Workshop on Continual Learning in Computer Vision (CVPR-W), 2020

We introduce a novel approach that focuses on object relations to effectively transfer knowledge for minimizing the effect of catastrophic forgetting in incremental learning of object detectors.


Adaptation of Person Re-identification Models for On-boarding New Camera(s)
Rameswar Panda, Amran Bhuiyan, Vittorio Murino, Amit K. Roy-Chowdhury
Pattern Recognition (PR), 2019

This paper extends our CVPR 2017 paper providing a new source-target selective adaptation strategy and rigorous experiments on more person re-id datasets.


Construction of Diverse Image Datasets from Web Collections with Limited Labeling
Niluthpol C. Mithun, Rameswar Panda, Amit K. Roy-Chowdhury
IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2019

This paper extends our MM 2016 paper where we employ a joint visual-semantic space to simultaneously utilize both images and text from the web for dataset construction.


Estimating Skin Tone and Effects on Classification Performance in Dermatology Datasets
Newton M. Kinyanjui, Timothy Odonga, Celia Cintas, Noel C. F. Codella, Rameswar Panda, Prasanna Sattigeri, Kush R. Varshney
NeurIPS Fair Machine Learning for Health Workshop (NeurIPS-W), 2019

We present an approach to estimate skin tone in benchmark skin disease datasets, and investigate whether model performance is dependent on this measure


Contemplating Visual Emotions: Understanding and Overcoming Dataset Bias
Rameswar Panda, Jianming Zhang, Haoxiang Li, Joon-Young Lee, Xin Lu, Amit K. Roy-Chowdhury
European Conference on Computer Vision (ECCV), 2018
[Project Page] [Supplementary Material]

We investigate different dataset biases and propose a curriculum guided webly supervised approach for learning a generalizable emotion recognition model.


Webly Supervised Joint Embedding for Cross-Modal Image-Text Retrieval
Niluthpol C. Mithun, Rameswar Panda, Evangelos E. Papalexakis, Amit K. Roy-Chowdhury
ACM Multimedia (MM), 2018

This work exploits large scale web data for learning an effective multi-modal embedding without requiring large amount of human-crafted training data.


FFNet: Video Fast-Forwarding via Reinforcement Learning
Shuyue Lan, Rameswar Panda, Qi Zhu, Amit K. Roy-Chowdhury
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018

We introduce an online framework for fast-forwarding a video while presenting its important and interesting content on the fly without processing or even obtaining the entire video.


Weakly Supervised Summarization of Web Videos
Rameswar Panda, Abir Das, Ziyan Wu, Jan Ernst, Amit K. Roy-Chowdhury
International Conference on Computer Vision (ICCV), 2017

We introduce a weakly supervised approach that requires only video-level annotations for summarizing long unconstrained web videos.


Unsupervised Adaptive Re-identification in Open World Dynamic Camera Networks
Rameswar Panda*, Amran Bhuiyan*, Vittorio Murino, Amit K. Roy-Chowdhury
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017

We propose an unsupervised adaptation scheme for re-identification models where a new camera may be temporarily inserted into an existing system to get additional information.


Collaborative Summarization of Topic-Related Videos
Rameswar Panda, Amit K. Roy-Chowdhury
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017

This paper presents a collaborative video summarization approach that exploits visual context from a set of topic-related videos to extract an informative summary of a given video.


Multi-View Surveillance Video Summarization via Joint Embedding and Sparse Optimization
Rameswar Panda, Amit K. Roy-Chowdhury
IEEE Transactions on Multimedia (TMM), 2017

This paper extends our ICPR 2016 paper providing new theoretical insights with a joint optimization and experimenting on spatio-temporal features and datasets.


Diversity-aware Multi-Video Summarization
Rameswar Panda, Niluthpol C. Mithun, Amit K. Roy-Chowdhury
IEEE Transactions on Image Processing (TIP), 2017

This paper introduces a new generalized sparse optimization framework for summarizing multiple videos generated from a video search or from a multi-view camera network.


Nystrom approximated temporally constrained multi-similarity spectral clustering approach for movie scene detection
Rameswar Panda, Sanjay K. Kuanar, Ananda S. Chowdhury
IEEE Transactions on Cybernetics (TCYB), 2017

We present a fast solution for movie scene detection using Nystrom approximated multi-similarity spectral clustering with a temporal integrity constraint.


Sparse Modeling for Topic-oriented Video Summarization
Rameswar Panda, Amit K. Roy-Chowdhury
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017

This paper presents a diversity-aware sparse optimization framework for summarizing topi-related videos generated from a video search.


Continuous Adaptation of Multi-Camera Person Identification Models through Sparse Non-redundant Representative Selection
Abir Das, Rameswar Panda, Amit K. Roy-Chowdhury
Computer Vision and Image Understanding (CVIU), 2016

We addressed the problem of online learning of identification systems where unlabeled data comes in small minibatches, with human in the loop.


Video Summarization in a Multi-View Camera Network
Rameswar Panda, Abir Das, Amit K. Roy-Chowdhury
IEEE International Conference on Pattern Recognition (ICPR), 2016

This paper presents a framework for summarizing multi-view videos by exploiting both intra- and inter-view content correlations in a joint embedding space.


Embedded Sparse Coding for Summarizing Multi-View Videos
Rameswar Panda, Abir Das, Amit K. Roy-Chowdhury
IEEE International Conference on Image Processing (ICIP), 2016

This paper presents a stochastic multi-view frame embedding based on KL divergence to preserve correlations in multi-view learning.


Generating Diverse Image Datasets with Limited Labeling
Niluthpol C. Mithun, Rameswar Panda, Amit K. Roy-Chowdhury
ACM Multimedia (MM), 2016

This paper presents a semi-supervised sparse coding framework which can be used to both create a dataset from scratch or enrich an existing dataset with diverse examples.


Active Image Pair Selection for Continuous Person Re-identification
Abir Das, Rameswar Panda, Amit K. Roy-Chowdhury
IEEE International Conference on Image Processing (ICIP), 2015

We present a continuous learning re-id system with a human in the loop which not only provides image labels but also improves the learned model by providing attribute based explanations..


Scalable Video Summarization using Skeleton Graph and Random Walk
Rameswar Panda, Sanjay K. Kuanar, Ananda S. Chowdhury
IEEE International Conference on Pattern Recognition (ICPR), 2014

This paper presents a scalable video summarization framework for both the analysis of the input video as well as the generation of summaries according to user-specified length constraints.


Video Key frame Extraction through Dynamic Delaunay Clustering with a Structural Constraint
Sanjay K. Kuanar, Rameswar Panda, Ananda S. Chowdhury
Journal of Visual Communication and Image Representation (JVCIR), 2013

This paper extends our ICPR 2012 paper providing new theoretical insights and experiments on more datasets including different key frame visualization techniques.


Video Storyboard Design using Delaunay Graphs
Ananda S. Chowdhury, Sanjay K. Kuanar, Rameswar Panda, Moloy N. Das
IEEE International Conference on Pattern Recognition (ICPR), 2012

This paper uses dynamic Delunay grpah clustering for summarizing videos.

Services

* Equal Contribution  /  Website template from this great guy!