यश भळगट | Yash Bhalgat
4th year PhD candidate in the Visual Geometry Group (VGG), Oxford. Co-advised by Andrew
Zisserman, Andrea Vedaldi, João Henriques and Iro Laina. Funded by the EPSRC+AWS fellowship with AIMS CDT.
Research Interests:
Parallelly, I also work as an AI consultant for an on-device AI startup and a LLM content
moderation company.
Before, I was a Senior Researcher at Qualcomm AI Research. I have also been fortunate to spend time at Voxel51, IBM Research
(Bangalore and Almaden Lab), IFPEN (Paris), TCS Research.
Education:
Open to research internships for 2025 - feel free to connect if my work interests you!
Email  / 
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News
- [09/2024] Our 3D-Aware Egocentric Tracking
paper accepted to ACCV 2024! 🎉
- [07/2024] N2F2
accepted to ECCV 2024! 🎉
- [02/2024] NeFeS
accepted to CVPR 2024! 🎉
- [01/2024] SiLVR
accepted to ICRA 2024! 🎉
- [01/2024] We are organizing the 2nd Workshop on Learning 3D with Multi-View Supervision at
CVPR2024.
- [09/2023] Contrastive-Lift paper accepted to NeurIPS 2023 as a
Spotlight presentation! 🚀 Code available: github.
- [03/2023] Epipolar-guided
Transformers paper accepted to CVPR 2023! 🎉
- [01/2023] HashNeRF-Pytorch hits 900+ stars on
Github! ⭐
- [06/2022] Serving as a Website Chair for BMVC 2022.
- [10/2021] 3D
Hand Pose Estimation work with my intern John Yang has been accepted to WACV
2022.
- [10/2021] Started my DPhil (PhD) at University
of Oxford in AIMS CDT, funded by EPSRC+AWS fellowship.
😊
- [11/2020] I got promoted to Senior Machine Learning Researcher
at Qualcomm AI Research. 😊
- [09/2020] Structured
Convolutions paper accepted to NeurIPS 2020!
- [03/2020] LSQ+ paper
accepted to the Efficient Deep Learning in Computer Vision workshop at CVPR 2020
- [02/2020] Preprint for our work on "Learned Threshold Pruning"
available.
- [11/2019] 3rd position in NeurIPS 2019 MicroNet competition
ImageNet track! - Leaderboard.
Code: here and
here.
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Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion
[Code].
Yash Bhalgat,
Iro
Laina,
João
Henriques,
Andrew
Zisserman,
Andrea
Vedaldi
NeurIPS, 2023 (Spotlight presentation)
We present a novel "slow-fast" contrastive fusion method to lift 2D predictions to 3D for scalable
instance segmentation, achieving significant improvements without requiring an upper bound on the
number of objects in the scene.
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A Light Touch Approach to Teaching Transformers Multi-view Geometry
Yash Bhalgat,
João
Henriques,
Andrew
Zisserman
CVPR, 2023
An "Epipolar-guided training" method to incorporate multi-view geometric priors into Transformer
models, which can be implemented in 150 lines of code.
During test-time, the Transformer implicitly estimates the epipolar geometry given 2 images and uses
it for downstream predictions, e.g. for pose-invariant retrieval.
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Dynamic Iterative Refinement for Efficient 3D Hand Pose Estimation
John Yang,
Yash Bhalgat,
Simyung Chang,
Fatih Porikli,
Nojun Kwak
WACV, 2022
We propose a tiny deep network of which partial layers are recursively exploited for refining its
previous estimations. During its iterative refinements, we employ learned gating criteria to decide
whether to exit from the weight-sharing loop, allowing per-sample adaptation in our model. We also
predict and exploit uncertainty estimations in the gating mechanism.
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Structured Convolutions for Efficient Neural Network Design
Yash Bhalgat,
Yizhe Zhang,
Jamie Lin,
Fatih Porikli
NeurIPS, 2020
We introduce a neat trick to enable the execution of convolution operations in the form of
efficient, scaled, sum-pooling components. We present a Structural Regularization loss that enables
this decomposition with negligible performance loss. Our method is competitive with other tensor
decomposition and structured pruning methods.
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Data-driven Weight Initialization with Sylvester Solvers
Debasmit Das,
Yash Bhalgat,
Fatih Porikli
Practical Machine Learning for Developing Countries Workshop, ICLR, 2021
We propose a data-driven scheme to initialize the parameters of a neural network. The
initialization is cast as an optimization problem, which is restructured into the well-known
Sylvester equation that has fast and efficient gradient-free solutions. We show that our proposed
method is especially effective in few-shot and fine-tuning settings.
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LSQ+: Improving low-bit quantization through learnable offsets and better initialization
Yash Bhalgat,
Jinwon Lee,
Markus Nagel,
Tijmen Blankevoort,
Nojun Kwak
Efficient Deep Learning in Computer Vision Workshop, CVPR, 2020
We introduce a general asymmetric quantization scheme with trainable scale and offset parameters.
LSQ+ shows SOTA results for EfficientNet and MixNet outperforming LSQ for low-bit quantization.
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Learned Threshold Pruning
Kambiz Azarian,
Yash Bhalgat,
Jinwon Lee,
Tijmen Blankevoort
arxiv, 2020
We propose an end-to-end differentiable method for learning layerwise pruning thresholds which
results in SOTA model compression ratios with AlexNet, ResNet and EfficientNet. Our method also
generates a trail of checkpoints with different accuracy-efficiency operating points.
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QKD: Quantization-aware Knowledge Distillation for Low-bit Quantization
Yash Bhalgat*,
Jangho Kim*,
Jinwon Lee,
Chirag Patel,
Nojun Kwak
arxiv, 2020
Low-bit quantization and KD often don't go well together, but both are important approaches to
reduces a model's memory footprint. We propose an effective method to combine these two methods and
show results that outperform all existing quantization/KD approaches.
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Teacher-Student Learning Paradigm for Tri-training: An Efficient
Method for Unlabeled Data Exploitation
Yash Bhalgat,
Zhe Liu,
Pritam
Gundecha,
Jalal Mahmud,
Amita Misra
KONVENS, 2019
Teacher-student tri-training is a method for semi-supervised learning using 3 classifiers working
using adaptive teacher and student thresholds.
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Annotation-cost Minimization for Medical Image Segmentation using Suggestive Mixed
Supervision Fully Convolutional Networks
Yash Bhalgat*,
Meet Shah*
Suyash Awate
Medical Imaging meets NeurIPS workshop, 2019
For Medical Image segmentation, we present a budget-based cost-minimization framework in a
mixed-supervision setting via dense segmentations, bounding boxes, and landmarks.
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CATSEYES: Categorizing Seismic structures with tessellated scattering wavelet networks
Yash Bhalgat,
Jean Charlety,
Laurent Duval
ICASSP, 2018
We use Scattering Wavelets transforms to extract sparse feature sets from seismic data. We show
that using this method combined with simple PCA-based feature selection leads to promising
classification performance in affordable computation time.
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Hall of Fame
- All India Rank 12 in IITJEE-Mains 2013 exam among 1.5 million students
- All India Rank 155 in IITJEE-Advanced 2013 exam among 0.2 million students
- Featured in National Top 30 for the International Astronomy Olympiad, 2013
- All India Rank 60 and awarded the KVPY Scholarship by Govt. of India
- Among top 300 in India to compete in the Physics, Chemistry and Mathematics olympiads
- Cargill Global Scholarship 2014-15 and selected in the 10-member Indian cohort to represent at
the global seminar in Minneapolis, USA in 2016
- Undergraduate Research Award (URA02) for Bachelors thesis at IIT Bombay
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Music
I play the Tabla, an Indian percussion
instrument and have a Visharad (≈ Bachelor of Music) in Indian Classical music. I've
also briefly tried to learn the Piano and Harmonica. I am a natural beatboxer (check this out) and I sometimes post music videos here:
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Website template borrowed from here.
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