Search Results for author: Rajat Vikram Singh

Found 5 papers, 3 papers with code

Physics-based Differentiable Depth Sensor Simulation

no code implementations ICCV 2021 Benjamin Planche, Rajat Vikram Singh

Gradient-based algorithms are crucial to modern computer-vision and graphics applications, enabling learning-based optimization and inverse problems.

Domain Adaptation Pose Estimation +2

ViewSynth: Learning Local Features from Depth using View Synthesis

1 code implementation22 Nov 2019 Jisan Mahmud, Rajat Vikram Singh, Peri Akiva, Spondon Kundu, Kuan-Chuan Peng, Jan-Michael Frahm

By learning view synthesis, we explicitly encourage the feature extractor to encode information about not only the visible, but also the occluded parts of the scene.

Camera Localization Keypoint Detection

Attention Guided Anomaly Localization in Images

no code implementations ECCV 2020 Shashanka Venkataramanan, Kuan-Chuan Peng, Rajat Vikram Singh, Abhijit Mahalanobis

Without the need of anomalous training images, we propose Convolutional Adversarial Variational autoencoder with Guided Attention (CAVGA), which localizes the anomaly with a convolutional latent variable to preserve the spatial information.

Ranked #23 on Anomaly Detection on MVTec AD (Segmentation AUROC metric, using extra training data)

Anomaly Detection

Learning without Memorizing

1 code implementation CVPR 2019 Prithviraj Dhar, Rajat Vikram Singh, Kuan-Chuan Peng, Ziyan Wu, Rama Chellappa

Incremental learning (IL) is an important task aimed at increasing the capability of a trained model, in terms of the number of classes recognizable by the model.

Incremental Learning

Sharpen Focus: Learning with Attention Separability and Consistency

1 code implementation ICCV 2019 Lezi Wang, Ziyan Wu, Srikrishna Karanam, Kuan-Chuan Peng, Rajat Vikram Singh, Bo Liu, Dimitris N. Metaxas

Recent developments in gradient-based attention modeling have seen attention maps emerge as a powerful tool for interpreting convolutional neural networks.

General Classification Image Classification

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