Collecting Image Description Datasets using Crowdsourcing. - Ramakrishna Vedantam; C. Lawrence Zitnick; Devi Parikh (CoRR, 2014)

CIDEr: Consensus-based image description evaluation. - Ramakrishna Vedantam; C. Lawrence Zitnick; Devi Parikh (CVPR, 2015)

Learning Common Sense through Visual Abstraction. - Ramakrishna Vedantam; Xiao Lin; Tanmay Batra; C. Lawrence Zitnick; Devi Parikh (ICCV, 2015)

Microsoft COCO Captions: Data Collection and Evaluation Server. - Xinlei Chen; Hao Fang 002; Tsung-Yi Lin; Ramakrishna Vedantam; Saurabh Gupta 001; Piotr Dollár; C. Lawrence Zitnick (CoRR, 2015)

Visual Word2Vec (vis-w2v): Learning Visually Grounded Word Embeddings Using Abstract Scenes. - Satwik Kottur; Ramakrishna Vedantam; José M. F. Moura; Devi Parikh (CoRR, 2015)

VisualWord2Vec (Vis-W2V): Learning Visually Grounded Word Embeddings Using Abstract Scenes. - Satwik Kottur; Ramakrishna Vedantam; José M. F. Moura; Devi Parikh (CVPR, 2016)

Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization. - Ramprasaath R. Selvaraju; Abhishek Das; Ramakrishna Vedantam; Michael Cogswell; Devi Parikh; Dhruv Batra (CoRR, 2016)

Grad-CAM: Why did you say that? - Ramprasaath R. Selvaraju; Abhishek Das; Ramakrishna Vedantam; Michael Cogswell; Devi Parikh; Dhruv Batra (CoRR, 2016)

Adopting Abstract Images for Semantic Scene Understanding. - C. Lawrence Zitnick; Ramakrishna Vedantam; Devi Parikh (IEEE Trans. Pattern Anal. Mach. Intell., 2016)

Counting Everyday Objects in Everyday Scenes. - Prithvijit Chattopadhyay; Ramakrishna Vedantam; Ramprasaath R. Selvaraju; Dhruv Batra; Devi Parikh (CVPR, 2017)

Context-Aware Captions from Context-Agnostic Supervision. - Ramakrishna Vedantam; Samy Bengio; Kevin Murphy 002; Devi Parikh; Gal Chechik (CVPR, 2017)

Sound-Word2Vec: Learning Word Representations Grounded in Sounds. - Ashwin K. Vijayakumar; Ramakrishna Vedantam; Devi Parikh (EMNLP, 2017)

Generative Models of Visually Grounded Imagination. - Ramakrishna Vedantam; Ian Fischer; Jonathan Huang; Kevin Murphy 002 (ICLR (Poster), 2018)

Probabilistic Neural Symbolic Models for Interpretable Visual Question Answering. - Ramakrishna Vedantam; Karan Desai; Stefan Lee; Marcus Rohrbach; Dhruv Batra; Devi Parikh (ICML, 2019)

Unsupervised Discovery of Decision States for Transfer in Reinforcement Learning. - Nirbhay Modhe; Prithvijit Chattopadhyay; Mohit Sharma; Abhishek Das; Devi Parikh; Dhruv Batra; Ramakrishna Vedantam (CoRR, 2019)

Interpretation, grounding and imagination for machine intelligence. - Ramakrishna Vedantam (2019)

IR-VIC: Unsupervised Discovery of Sub-goals for Transfer in RL. - Nirbhay Modhe; Prithvijit Chattopadhyay; Mohit Sharma; Abhishek Das; Devi Parikh; Dhruv Batra; Ramakrishna Vedantam (IJCAI, 2020)

Learning Optimal Representations with the Decodable Information Bottleneck. - Yann Dubois; Douwe Kiela; David J. Schwab; Ramakrishna Vedantam (NeurIPS, 2020)

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. - Ramprasaath R. Selvaraju; Michael Cogswell; Abhishek Das; Ramakrishna Vedantam; Devi Parikh; Dhruv Batra (Int. J. Comput. Vis., 2020)

CURI: A Benchmark for Productive Concept Learning Under Uncertainty. - Ramakrishna Vedantam; Arthur Szlam; Maximilian Nickel; Ari Morcos; Brenden M. Lake (ICML, 2021)

An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers. - Ramakrishna Vedantam; David Lopez-Paz; David J. Schwab (NeurIPS, 2021)

COAT: Measuring Object Compositionality in Emergent Representations. - Sirui Xie; Ari S. Morcos; Song-Chun Zhu; Ramakrishna Vedantam (ICML, 2022)

Improving Selective Visual Question Answering by Learning from Your Peers. - Corentin Dancette; Spencer Whitehead; Rishabh Maheshwary; Ramakrishna Vedantam; Stefan Scherer; Xinlei Chen; Matthieu Cord; Marcus Rohrbach (CVPR, 2023)

Don't forget the nullspace! Nullspace occupancy as a mechanism for out of distribution failure. - Daksh Idnani; Vivek Madan; Naman Goyal 001; David J. Schwab; Ramakrishna Vedantam (ICLR, 2023)

Hyperbolic Image-text Representations. - Karan Desai; Maximilian Nickel; Tanmay Rajpurohit; Justin Johnson 001; Shanmukha Ramakrishna Vedantam (ICML, 2023)

Understanding the detrimental class-level effects of data augmentation. - Polina Kirichenko; Mark Ibrahim; Randall Balestriero; Diane Bouchacourt; Shanmukha Ramakrishna Vedantam; Hamed Firooz; Andrew Gordon Wilson (NeurIPS, 2023)

Embarassingly Simple Dataset Distillation. - Yunzhen Feng; Ramakrishna Vedantam; Julia Kempe (CoRR, 2023)

Embarrassingly Simple Dataset Distillation. - Yunzhen Feng; Shanmukha Ramakrishna Vedantam; Julia Kempe (ICLR, 2024)

Faster Vision Mamba is Rebuilt in Minutes via Merged Token Re-training. - Mingjia Shi; Yuhao Zhou; Ruiji Yu; Zekai Li; Zhiyuan Liang; Xuanlei Zhao; Xiaojiang Peng; Tanmay Rajpurohit; Shanmukha Ramakrishna Vedantam; Wangbo Zhao; Kai Wang 036; Yang You 001 (CoRR, 2024)

Emphasizing Discriminative Features for Dataset Distillation in Complex Scenarios. - Kai Wang 036; Zekai Li; Zhi-Qi Cheng; Samir Khaki; Ahmad Sajedi; Ramakrishna Vedantam; Konstantinos N. Plataniotis; Alexander Hauptmann 001; Yang You 001 (CVPR, 2025)