1 code implementation • 7 Dec 2017 • Aditya Ganeshan
To understand how DCNN based models work at the task of semantic segmentation, we try to analyze the DCNN models in semantic segmentation.
2 code implementations • 24 Jan 2018 • Konda Reddy Mopuri, Aditya Ganeshan, R. Venkatesh Babu
Further, via exploiting simple priors related to the data distribution, our objective remarkably boosts the fooling ability of the crafted perturbations.
2 code implementations • 3 Aug 2018 • Jogendra Nath Kundu, Aditya Ganeshan, Rahul M. V., Aditya Prakash, R. Venkatesh Babu
Such image comparison based approach also alleviates the problem of data scarcity and hence enhances scalability of the proposed approach for novel object categories with minimal annotation.
2 code implementations • 3 Sep 2018 • Jogendra Nath Kundu, Rahul M. V., Aditya Ganeshan, R. Venkatesh Babu
In this work, we propose a data-efficient method which utilizes the geometric regularity of intraclass objects for pose estimation.
1 code implementation • ICCV 2019 • Aditya Ganeshan, B. S. Vivek, R. Venkatesh Babu
Though Deep Neural Networks (DNN) show excellent performance across various computer vision tasks, several works show their vulnerability to adversarial samples, i. e., image samples with imperceptible noise engineered to manipulate the network's prediction.
1 code implementation • 17 Feb 2020 • David Samuel, Aditya Ganeshan, Jason Naradowsky
We propose a hierarchical meta-learning-inspired model for music source separation (Meta-TasNet) in which a generator model is used to predict the weights of individual extractor models.
Ranked #23 on Music Source Separation on MUSDB18
no code implementations • ICCV 2021 • Aditya Ganeshan, Alexis Vallet, Yasunori Kudo, Shin-ichi Maeda, Tommi Kerola, Rares Ambrus, Dennis Park, Adrien Gaidon
Deep learning models for semantic segmentation rely on expensive, large-scale, manually annotated datasets.
Ranked #33 on Semantic Segmentation on NYU Depth v2
no code implementations • ICCV 2023 • Aditya Ganeshan, R. Kenny Jones, Daniel Ritchie
Programs offer compactness and structure that makes them an attractive representation for visual data.
no code implementations • 5 Feb 2024 • Rio Aguina-Kang, Maxim Gumin, Do Heon Han, Stewart Morris, Seung Jean Yoo, Aditya Ganeshan, R. Kenny Jones, Qiuhong Anna Wei, Kailiang Fu, Daniel Ritchie
Unlike most prior work on indoor scene generation, our system does not require a large training dataset of existing 3D scenes.