Rameswar Panda

I am a Research Scientist at IBM Research, Cambridge, where I work on solving real world problems using computer vision and machine learning.

I received my Ph.D. from UC Riverside in 2018, under the supervision of Prof. Amit K. Roy-Chowdhury. Previously, I obtained my M.S. from Jadavpur University (India) in 2013 supervised by Prof. Ananda S. Chowdhury.

I was very fortunate to have interned at NEC Labs America (Cupertino, Summer 2018), Adobe Research (San Jose, Summer/Fall 2017) and Siemens Corporate Research (Princeton, Summer 2016).

Email / Curriculum Vitae / Google scholar / Linkedin


Research

My research interests mainly lie in the areas of computer vision, machine learning and multimedia computing. In particular, my current focus is on learning with limited supervision (transfer learning, few-shot learning) and AutoAI for several computer vision problems. During my Ph.D., I worked on video summarization, person re-identification in video surveillance, and multi-modal embedding.

News

2019

  • Paper on Adaptation of Person Re-ID Models accepted to Pattern Recognition (PR).
  • We are organizing a Workshop on Multi-modal Video Analysis at ICCV 2019.
  • We are organizing a tutorial on Recent Advances in Visual Data Summarization at CVPR 2019.
  • Paper on Construction of Diverse Image Datasets accepted to IEEE TCSVT.
  • 2018

  • I joined IBM Research, Cambridge as a research scientist.
  • Successfully defended my Ph.D. dissertation defense! My Ph.D. thesis can be found here.
  • Paper on Visual Emotion Analysis accepted at ECCV 2018.
  • Paper on Image-Text Retrieval accepted at ACM MM 2018.
  • Research Intern with Media Analytics group at NEC Labs America, Cupertino.
  • Paper on Video Fast-Forwarding accepted at CVPR 2018.
  • Journal Publications

    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 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 associated textual information from the web for dataset construction.
    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
    Extension of our ICASSP 2017 paper. We propose 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.
    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 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 experimenting on more features and datasets.

    Selected Conference Publications

    (see my Google Scholar for the full list of papers)
    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 approch 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 International Conference on 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 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 annotation for summarizing 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 for re-identification models in a dynamic camera network 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.
    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.
    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 diveregence to preserve correlations in multi-view learning.
    Generating Diverse Image Datasets with Limited Labeling
    Niluthpol C. Mithun, Rameswar Panda, Amit K. Roy-Chowdhury
    ACM International Conference on Multimedia (MM), 2016
    This paper presents a semi-supervised sparse coding framework to collect a diverse set of images with minimal human effort.
    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.
    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 video summarization framework which is scalable during both the analysis and the generation stages of video summarization.
    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.

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