Bhavya Goyal

Bhavya Goyal

Graduate Student
CS Dept. UW-Madison
Email: bhavya at

I am a graduate Student at CS Dept. of UW-Madison.

I am part of the Wision Lab where I am advised by Prof. Mohit Gupta. My research revolves around the areas of Computer Vision and Deep Learning. Currently, I am working on Visual Recognition under low-light and motion blur conditions.

Earlier, I worked as a Research Engineer at Visual Understanding Lab of Samsung Research, Seoul for 3 years. Before that, I completed my undergraduate in CS from IIT Delhi where I was advised by Prof. Vinay Riberio


  • Dual Corruptions
    Robust Scene Inference under Noise-Blur Dual Corruptions
    ICCP 2022
    Bhavya Goyal, Jean-François Lalonde, Yin Li, Mohit Gupta
  • Photon Net
    Photon-Starved Scene Inference using Single Photon Cameras
    ICCV 2021
    Bhavya Goyal, Mohit Gupta
  • ABE
    Attention-based Ensemble for Deep Metric Learning
    ECCV 2018
    Wonsik Kim, Bhavya Goyal, Kunal Chawla, Jungmin Lee, Keunjoo Kwon


  • University of Wisconsin-Madison
    (Jun'20 - Present)

    Wision Lab

    Noise-Blur Dual
    • Robust scene inference models under low-light and motion.
    • Object detection models based on FCOS architecture using multi-exposure ensemble.

    Photon Net
    • SOTA Fine-grained Image Classification under extreme low-light conditions (photons per pixel<0.1).
    • Monocular Depth Estimation on noisy images using architectures with increased shot-noise robustness.

  • Samsung Research, South Korea
    (Sep'16 - Jul'19)

    Visual Understanding Lab

    Object Recognition and Retrieval, Smart Refrigerators [Link]
    • Image recognition algorithms for detecting grocery items inside the refrigerator, models used for product recommendation engine in Samsung Smart Refrigerators
    • Designed techniques using global and attentive deep local descriptors for feature matching, paired with geometric verification of selected keypoints, to recognize products that are partially occluded by other items.

    Product Search, Bixby Vision [Link]
    • Developed large scale retrieval models for products in online shopping mall images.
    • Designed attention mechanism in Deep Neural Networks to ignore background noise in query images, achieves SOTA results on all image major retrieval benchmarks, published in ECCV 2018


  • AIMeetsBeauty

    AI Meets Beauty Challenge [ Link ] [ Code ]
    (Jul’18 - Aug'18)

    ACM Multimedia Conference 2018
    • Winner with SOTA results for half million product image recognition
    • Developed CNN based retrieval model using attention module to ignore background clutter in product images, approx nearest neighbor search for product images in large scale retrieval DB
  • Tiger Re-ID

    Tiger ReID in Wild [ Code ] [ Slides ] [ Paper ]
    (Jun’19 - Jul'19)

    Prof. Yin Li

    • Proposed architecture using object detection and re-id which encourages diversity among feature embeddings to get more discriminative features which boosts the performance on most retrieval benchmarks.
  • Stack Exchange

    Stack-Exchange Tag Prediction [ Code ] [ Slides ] [ Paper ]
    (April’16 - May'16)

    Prof. Mausam

    • State-of-the-art results for predicting tags/labels for questions on different Stack Exchange portals
    • Classification of meta features from text and code snippets in questions using SVM
    • KMeans to cluster word embeddings from Google's pre-trained Word2Vec model and ensembled with model trained with term affinity of tags and words