no code implementations • 16 Mar 2024 • Yash Bhalgat, Iro Laina, João F. Henriques, Andrew Zisserman, Andrea Vedaldi
To address this, we introduce Nested Neural Feature Fields (N2F2), a novel approach that employs hierarchical supervision to learn a single feature field, wherein different dimensions within the same high-dimensional feature encode scene properties at varying granularities.
no code implementations • 11 Mar 2024 • Yifu Tao, Yash Bhalgat, Lanke Frank Tarimo Fu, Matias Mattamala, Nived Chebrolu, Maurice Fallon
We present a neural-field-based large-scale reconstruction system that fuses lidar and vision data to generate high-quality reconstructions that are geometrically accurate and capture photo-realistic textures.
1 code implementation • NeurIPS 2023 • Yash Bhalgat, Iro Laina, João F. Henriques, Andrew Zisserman, Andrea Vedaldi
Our approach outperforms the state-of-the-art on challenging scenes from the ScanNet, Hypersim, and Replica datasets, as well as on our newly created Messy Rooms dataset, demonstrating the effectiveness and scalability of our slow-fast clustering method.
1 code implementation • 17 Mar 2023 • Shuai Chen, Yash Bhalgat, Xinghui Li, Jiawang Bian, Kejie Li, ZiRui Wang, Victor Adrian Prisacariu
To enhance the robustness of our model, we introduce a feature fusion module and a progressive training strategy.
no code implementations • CVPR 2023 • Yash Bhalgat, Joao F. Henriques, Andrew Zisserman
Transformers are powerful visual learners, in large part due to their conspicuous lack of manually-specified priors.
1 code implementation • 22 Mar 2022 • Hugo Berg, Siobhan Mackenzie Hall, Yash Bhalgat, Wonsuk Yang, Hannah Rose Kirk, Aleksandar Shtedritski, Max Bain
Vision-language models can encode societal biases and stereotypes, but there are challenges to measuring and mitigating these multimodal harms due to lacking measurement robustness and feature degradation.
no code implementations • 11 Nov 2021 • John Yang, Yash Bhalgat, Simyung Chang, Fatih Porikli, Nojun Kwak
While hand pose estimation is a critical component of most interactive extended reality and gesture recognition systems, contemporary approaches are not optimized for computational and memory efficiency.
no code implementations • 2 May 2021 • Debasmit Das, Yash Bhalgat, Fatih Porikli
The initialization is cast as an optimization problem where we minimize a combination of encoding and decoding losses of the input activations, which is further constrained by a user-defined latent code.
no code implementations • NeurIPS 2020 • Yash Bhalgat, Yizhe Zhang, Jamie Lin, Fatih Porikli
We show how this decomposition can be applied to 2D and 3D kernels as well as the fully-connected layers.
4 code implementations • 20 Apr 2020 • Yash Bhalgat, Jinwon Lee, Markus Nagel, Tijmen Blankevoort, Nojun Kwak
To solve this problem, we propose LSQ+, a natural extension of LSQ, wherein we introduce a general asymmetric quantization scheme with trainable scale and offset parameters that can learn to accommodate the negative activations.
Ranked #18 on Quantization on ImageNet
no code implementations • 28 Feb 2020 • Kambiz Azarian, Yash Bhalgat, Jinwon Lee, Tijmen Blankevoort
This is in contrast to other methods that search for per-layer thresholds via a computationally intensive iterative pruning and fine-tuning process.
no code implementations • 28 Nov 2019 • Jangho Kim, Yash Bhalgat, Jinwon Lee, Chirag Patel, Nojun Kwak
First, Self-studying (SS) phase fine-tunes a quantized low-precision student network without KD to obtain a good initialization.
no code implementations • 25 Sep 2019 • Yash Bhalgat, Zhe Liu, Pritam Gundecha, Jalal Mahmud, Amita Misra
Given that labeled data is expensive to obtain in real-world scenarios, many semi-supervised algorithms have explored the task of exploitation of unlabeled data.
no code implementations • 29 Dec 2018 • Yash Bhalgat, Meet Shah, Suyash Awate
For medical image segmentation, most fully convolutional networks (FCNs) need strong supervision through a large sample of high-quality dense segmentations, which is taxing in terms of costs, time and logistics involved.
no code implementations • 29 Sep 2018 • Yash Bhalgat
The last section gives a complete comparison of all the approaches implemented during this challenge, including the one presented in the baseline paper.
no code implementations • 16 Sep 2016 • Yash Bhalgat, Mandar Kulkarni, Shirish Karande, Sachin Lodha
Document digitization is becoming increasingly crucial.