Search Results for author: Ashish Shrivastava

Found 15 papers, 4 papers with code

SHIFT3D: Synthesizing Hard Inputs For Tricking 3D Detectors

no code implementations ICCV 2023 Hongge Chen, Zhao Chen, Gregory P. Meyer, Dennis Park, Carl Vondrick, Ashish Shrivastava, Yuning Chai

We present SHIFT3D, a differentiable pipeline for generating 3D shapes that are structurally plausible yet challenging to 3D object detectors.

Autonomous Driving

NOVA: NOvel View Augmentation for Neural Composition of Dynamic Objects

1 code implementation24 Aug 2023 Dakshit Agrawal, Jiajie Xu, Siva Karthik Mustikovela, Ioannis Gkioulekas, Ashish Shrivastava, Yuning Chai

We propose a novel-view augmentation (NOVA) strategy to train NeRFs for photo-realistic 3D composition of dynamic objects in a static scene.

Optical Flow Estimation

AptSim2Real: Approximately-Paired Sim-to-Real Image Translation

no code implementations9 Mar 2023 Charles Y Zhang, Ashish Shrivastava

To mitigate this gap, sim-to-real domain transfer modifies simulated images to better match real-world data, enabling the effective use of simulation data in model training.

Image-to-Image Translation Translation

NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation

2 code implementations6 Dec 2021 Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann, Aadesh Gupta, Zhenhao Li, Saad Mahamood, Abinaya Mahendiran, Simon Mille, Ashish Shrivastava, Samson Tan, Tongshuang Wu, Jascha Sohl-Dickstein, Jinho D. Choi, Eduard Hovy, Ondrej Dusek, Sebastian Ruder, Sajant Anand, Nagender Aneja, Rabin Banjade, Lisa Barthe, Hanna Behnke, Ian Berlot-Attwell, Connor Boyle, Caroline Brun, Marco Antonio Sobrevilla Cabezudo, Samuel Cahyawijaya, Emile Chapuis, Wanxiang Che, Mukund Choudhary, Christian Clauss, Pierre Colombo, Filip Cornell, Gautier Dagan, Mayukh Das, Tanay Dixit, Thomas Dopierre, Paul-Alexis Dray, Suchitra Dubey, Tatiana Ekeinhor, Marco Di Giovanni, Tanya Goyal, Rishabh Gupta, Louanes Hamla, Sang Han, Fabrice Harel-Canada, Antoine Honore, Ishan Jindal, Przemyslaw K. Joniak, Denis Kleyko, Venelin Kovatchev, Kalpesh Krishna, Ashutosh Kumar, Stefan Langer, Seungjae Ryan Lee, Corey James Levinson, Hualou Liang, Kaizhao Liang, Zhexiong Liu, Andrey Lukyanenko, Vukosi Marivate, Gerard de Melo, Simon Meoni, Maxime Meyer, Afnan Mir, Nafise Sadat Moosavi, Niklas Muennighoff, Timothy Sum Hon Mun, Kenton Murray, Marcin Namysl, Maria Obedkova, Priti Oli, Nivranshu Pasricha, Jan Pfister, Richard Plant, Vinay Prabhu, Vasile Pais, Libo Qin, Shahab Raji, Pawan Kumar Rajpoot, Vikas Raunak, Roy Rinberg, Nicolas Roberts, Juan Diego Rodriguez, Claude Roux, Vasconcellos P. H. S., Ananya B. Sai, Robin M. Schmidt, Thomas Scialom, Tshephisho Sefara, Saqib N. Shamsi, Xudong Shen, Haoyue Shi, Yiwen Shi, Anna Shvets, Nick Siegel, Damien Sileo, Jamie Simon, Chandan Singh, Roman Sitelew, Priyank Soni, Taylor Sorensen, William Soto, Aman Srivastava, KV Aditya Srivatsa, Tony Sun, Mukund Varma T, A Tabassum, Fiona Anting Tan, Ryan Teehan, Mo Tiwari, Marie Tolkiehn, Athena Wang, Zijian Wang, Gloria Wang, Zijie J. Wang, Fuxuan Wei, Bryan Wilie, Genta Indra Winata, Xinyi Wu, Witold Wydmański, Tianbao Xie, Usama Yaseen, Michael A. Yee, Jing Zhang, Yue Zhang

Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.

Data Augmentation

Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models

no code implementations6 Oct 2021 Jen-Hao Rick Chang, Ashish Shrivastava, Hema Swetha Koppula, Xiaoshuai Zhang, Oncel Tuzel

However, under an unsupervised-style setting, typical training algorithms for controllable sequence generative models suffer from the training-inference mismatch, where the same sample is used as content and style input during training but unpaired samples are given during inference.

CANDLE: Decomposing Conditional and Conjunctive Queries for Task-Oriented Dialogue Systems

no code implementations8 Jul 2021 Aadesh Gupta, Kaustubh D. Dhole, Rahul Tarway, Swetha Prabhakar, Ashish Shrivastava

Domain-specific dialogue systems generally determine user intents by relying on sentence level classifiers that mainly focus on single action sentences.

Task-Oriented Dialogue Systems

Saying No is An Art: Contextualized Fallback Responses for Unanswerable Dialogue Queries

1 code implementation ACL 2021 Ashish Shrivastava, Kaustubh Dhole, Abhinav Bhatt, Sharvani Raghunath

Despite end-to-end neural systems making significant progress in the last decade for task-oriented as well as chit-chat based dialogue systems, most dialogue systems rely on hybrid approaches which use a combination of rule-based, retrieval and generative approaches for generating a set of ranked responses.


Optimize what matters: Training DNN-HMM Keyword Spotting Model Using End Metric

no code implementations2 Nov 2020 Ashish Shrivastava, Arnav Kundu, Chandra Dhir, Devang Naik, Oncel Tuzel

The DNN, in prior methods, is trained independent of the HMM parameters to minimize the cross-entropy loss between the predicted and the ground-truth state probabilities.

Keyword Spotting

Unsupervised Style and Content Separation by Minimizing Mutual Information for Speech Synthesis

no code implementations9 Mar 2020 Ting-yao Hu, Ashish Shrivastava, Oncel Tuzel, Chandra Dhir

We present a method to generate speech from input text and a style vector that is extracted from a reference speech signal in an unsupervised manner, i. e., no style annotation, such as speaker information, is required.

Speech Synthesis

Divide, Denoise, and Defend against Adversarial Attacks

no code implementations19 Feb 2018 Seyed-Mohsen Moosavi-Dezfooli, Ashish Shrivastava, Oncel Tuzel

Improving the robustness of neural networks against these attacks is important, especially for security-critical applications.


Learning from Simulated and Unsupervised Images through Adversarial Training

9 code implementations CVPR 2017 Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang, Russ Webb

With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations.

Ranked #3 on Image-to-Image Translation on Cityscapes Labels-to-Photo (Per-class Accuracy metric)

Domain Adaptation Gaze Estimation +2

Class Consistent Multi-Modal Fusion With Binary Features

no code implementations CVPR 2015 Ashish Shrivastava, Mohammad Rastegari, Sumit Shekhar, Rama Chellappa, Larry S. Davis

Many existing recognition algorithms combine different modalities based on training accuracy but do not consider the possibility of noise at test time.


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