Search Results for author: Ramakrishna Vedantam

Found 19 papers, 11 papers with code

Hyperbolic Image-Text Representations

1 code implementation18 Apr 2023 Karan Desai, Maximilian Nickel, Tanmay Rajpurohit, Justin Johnson, Ramakrishna Vedantam

Visual and linguistic concepts naturally organize themselves in a hierarchy, where a textual concept "dog" entails all images that contain dogs.

Image Classification Retrieval +1

An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers

no code implementations NeurIPS 2021 Ramakrishna Vedantam, David Lopez-Paz, David J. Schwab

Recent work demonstrates that deep neural networks trained using Empirical Risk Minimization (ERM) can generalize under distribution shift, outperforming specialized training algorithms for domain generalization.

Domain Generalization Out-of-Distribution Generalization

CURI: A Benchmark for Productive Concept Learning Under Uncertainty

1 code implementation6 Oct 2020 Ramakrishna Vedantam, Arthur Szlam, Maximilian Nickel, Ari Morcos, Brenden Lake

Humans can learn and reason under substantial uncertainty in a space of infinitely many concepts, including structured relational concepts ("a scene with objects that have the same color") and ad-hoc categories defined through goals ("objects that could fall on one's head").

Meta-Learning Systematic Generalization

Learning Optimal Representations with the Decodable Information Bottleneck

1 code implementation NeurIPS 2020 Yann Dubois, Douwe Kiela, David J. Schwab, Ramakrishna Vedantam

We address the question of characterizing and finding optimal representations for supervised learning.

DS-VIC: Unsupervised Discovery of Decision States for Transfer in RL

no code implementations25 Sep 2019 Nirbhay Modhe, Prithvijit Chattopadhyay, Mohit Sharma, Abhishek Das, Devi Parikh, Dhruv Batra, Ramakrishna Vedantam

We learn to identify decision states, namely the parsimonious set of states where decisions meaningfully affect the future states an agent can reach in an environment.

Generative Models of Visually Grounded Imagination

no code implementations ICLR 2018 Ramakrishna Vedantam, Ian Fischer, Jonathan Huang, Kevin Murphy

It is easy for people to imagine what a man with pink hair looks like, even if they have never seen such a person before.


Sound-Word2Vec: Learning Word Representations Grounded in Sounds

no code implementations EMNLP 2017 Ashwin K. Vijayakumar, Ramakrishna Vedantam, Devi Parikh

In this work, we treat sound as a first-class citizen, studying downstream textual tasks which require aural grounding.

Retrieval Word Embeddings

Context-aware Captions from Context-agnostic Supervision

1 code implementation CVPR 2017 Ramakrishna Vedantam, Samy Bengio, Kevin Murphy, Devi Parikh, Gal Chechik

We introduce an inference technique to produce discriminative context-aware image captions (captions that describe differences between images or visual concepts) using only generic context-agnostic training data (captions that describe a concept or an image in isolation).

Image Captioning Language Modelling

Grad-CAM: Why did you say that?

2 code implementations22 Nov 2016 Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, Dhruv Batra

We propose a technique for making Convolutional Neural Network (CNN)-based models more transparent by visualizing input regions that are 'important' for predictions -- or visual explanations.

Image Captioning Visual Question Answering

Counting Everyday Objects in Everyday Scenes

1 code implementation CVPR 2017 Prithvijit Chattopadhyay, Ramakrishna Vedantam, Ramprasaath R. Selvaraju, Dhruv Batra, Devi Parikh

In this work, we build dedicated models for counting designed to tackle the large variance in counts, appearances, and scales of objects found in natural scenes.

Object Object Counting +4

Visual Word2Vec (vis-w2v): Learning Visually Grounded Word Embeddings Using Abstract Scenes

1 code implementation CVPR 2016 Satwik Kottur, Ramakrishna Vedantam, José M. F. Moura, Devi Parikh

While word embeddings trained using text have been extremely successful, they cannot uncover notions of semantic relatedness implicit in our visual world.

Common Sense Reasoning Image Retrieval +3

Cannot find the paper you are looking for? You can Submit a new open access paper.