Subhajit Chaudhury | The University of Tokyo (original) (raw)

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Papers by Subhajit Chaudhury

2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP)

The growing use of virtual autonomous agents in applications like games and entertainment demands... more The growing use of virtual autonomous agents in applications like games and entertainment demands better control policies for natural-looking movements and actions. Unlike the conventional approach of hard-coding motion routines, we propose a deep learning method for obtaining control policies by directly mimicking raw video demonstrations. Previous methods in this domain rely on extracting low-dimensional features from expert videos followed by a separate hand-crafted reward estimation step. We propose an imitation learning framework that reduces the dependence on hand-engineered reward functions by jointly learning the feature extraction and reward estimation steps using Generative Adversarial Networks (GANs). Our main contribution in this paper is to show that under injective mapping between low-level joint state (angles and velocities) trajectories and corresponding raw video stream, performing adversarial imitation learning on video demonstrations is equivalent to learning from the state trajectories. Experimental results show that the proposed adversarial learning method from raw videos produces a similar performance to state-of-the-art imitation learning techniques while frequently outperforming existing hand-crafted video imitation methods. Furthermore, we show that our method can learn action policies by imitating video demonstrations on YouTube with similar performance to learned agents from true reward signals. Please see the supplementary video submission at https://ibm.biz/BdzzNA.

2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA)

We present a fully convolutional network(FCN) based approach for color image restoration. FCNs ha... more We present a fully convolutional network(FCN) based approach for color image restoration. FCNs have recently shown remarkable performance for high-level vision problem like semantic segmentation. In this paper, we investigate if FCN models can show promising performance for low-level problems like image restoration as well. We propose a fully convolutional model, that learns a direct end-to-end mapping between the corrupted images as input and the desired clean images as output. Our proposed method takes inspiration from domain transformation techniques but presents a data-driven task specific approach where filters for novel basis projection, task dependent coefficient alterations, and image reconstruction are represented as convolutional networks. Experimental results show that our FCN model outperforms traditional sparse coding based methods and demonstrates competitive performance compared to the state-of-the-art methods for image denoising. We further show that our proposed model can solve the difficult problem of blind image inpainting and can produce reconstructed images of impressive visual quality.

Journal of Electronic Imaging

2020 IEEE Winter Conference on Applications of Computer Vision (WACV)

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Proceedings of the AAAI Conference on Artificial Intelligence

Neural networks have contributed to tremendous progress in the domains of computer vision, speech... more Neural networks have contributed to tremendous progress in the domains of computer vision, speech processing, and other real-world applications. However, recent studies have shown that these state-of-the-art models can be easily compromised by adding small imperceptible perturbations. My thesis summary frames the problem of adversarial robustness as an equivalent problem of learning suitable features that leads to good generalization in neural networks. This is motivated from learning in humans which is not trivially fooled by such perturbations due to robust feature learning which shows good out-of-sample generalization.

Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing - ICVGIP '14, 2014

2014 IEEE Haptics Symposium (HAPTICS), 2014

2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA), May 1, 2017