330 citations · 765 across the 27 of their papers we have counts for
32 papers
Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack
Xiaoliang Dai, Ji Hou, Chih-Yao Ma +23
Training text-to-image models with web scale image-text pairs enables the generation of a wide range of visual concepts from text. However, these pre-trained models often face chal…
PACO: Parts and Attributes of Common Objects
Vignesh Ramanathan, Anmol Kalia, Vladan Petrovic +11
Object models are gradually progressing from predicting just category labels to providing detailed descriptions of object instances. This motivates the need for large datasets whic…
Filtering, Distillation, and Hard Negatives for Vision-Language Pre-Training
Filip Radenovic, Abhimanyu Dubey, Abhishek Kadian +6
Vision-language models trained with contrastive learning on large-scale noisy data are becoming increasingly popular for zero-shot recognition problems. In this paper we improve th…
Neural Basis Models for Interpretability
Filip Radenovic, Abhimanyu Dubey, Dhruv Mahajan
Due to the widespread use of complex machine learning models in real-world applications, it is becoming critical to explain model predictions. However, these models are typically b…
Scalable Interpretability via Polynomials
Abhimanyu Dubey, Filip Radenovic, Dhruv Mahajan
Generalized Additive Models (GAMs) have quickly become the leading choice for inherently-interpretable machine learning. However, unlike uninterpretable methods such as DNNs, they…
Making Heads or Tails: Towards Semantically Consistent Visual Counterfactuals
Simon Vandenhende, Dhruv Mahajan, Filip Radenovic +1
A visual counterfactual explanation replaces image regions in a query image with regions from a distractor image such that the system's decision on the transformed image changes to…
Revisiting Weakly Supervised Pre-Training of Visual Perception Models
Mannat Singh, Laura Gustafson, Aaron Adcock +7
Model pre-training is a cornerstone of modern visual recognition systems. Although fully supervised pre-training on datasets like ImageNet is still the de-facto standard, recent st…
Adaptive Methods for Aggregated Domain Generalization
Xavier Thomas, Dhruv Mahajan, Alex Pentland +1
Domain generalization involves learning a classifier from a heterogeneous collection of training sources such that it generalizes to data drawn from similar unknown target domains,…
Large-Scale Attribute-Object Compositions
Filip Radenovic, Animesh Sinha, Albert Gordo +2
We study the problem of learning how to predict attribute-object compositions from images, and its generalization to unseen compositions missing from the training data. To the best…
Adaptive Methods for Real-World Domain Generalization
Abhimanyu Dubey, Vignesh Ramanathan, Alex Pentland +1
Invariant approaches have been remarkably successful in tackling the problem of domain generalization, where the objective is to perform inference on data distributions different f…
Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency
Qing Liu, Vignesh Ramanathan, Dhruv Mahajan +2
Weakly supervised instance segmentation reduces the cost of annotations required to train models. However, existing approaches which rely only on image-level class labels predomina…
What leads to generalization of object proposals?
Rui Wang, Dhruv Mahajan, Vignesh Ramanathan
Object proposal generation is often the first step in many detection models. It is lucrative to train a good proposal model, that generalizes to unseen classes. This could help sca…
Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias
Krishna Kumar Singh, Dhruv Mahajan, Kristen Grauman +3
Existing models often leverage co-occurrences between objects and their context to improve recognition accuracy. However, strongly relying on context risks a model's generalizabili…
Measuring Dataset Granularity
Yin Cui, Zeqi Gu, Dhruv Mahajan +3
Despite the increasing visibility of fine-grained recognition in our field, "fine-grained'' has thus far lacked a precise definition. In this work, building upon clustering theory,…
From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality
Zhenqiang Ying, Haoran Niu, Praful Gupta +3
Blind or no-reference (NR) perceptual picture quality prediction is a difficult, unsolved problem of great consequence to the social and streaming media industries that impacts bil…
ClusterFit: Improving Generalization of Visual Representations
Xueting Yan, Ishan Misra, Abhinav Gupta +2
Pre-training convolutional neural networks with weakly-supervised and self-supervised strategies is becoming increasingly popular for several computer vision tasks. However, due to…
Self-Supervised Learning by Cross-Modal Audio-Video Clustering
Humam Alwassel, Dhruv Mahajan, Bruno Korbar +3
Visual and audio modalities are highly correlated, yet they contain different information. Their strong correlation makes it possible to predict the semantics of one from the other…
Stochastic Spread Pairs Trading in the Indian Commodity Market
Dhruv Mahajan, Abhijeet Chandra
In this study, we applied a stochastic spread pairs trading strategy on the Indian commodity market. The complete set of commodities were taken whose spot price was available for t…
Scaling and Benchmarking Self-Supervised Visual Representation Learning
Priya Goyal, Dhruv Mahajan, Abhinav Gupta +1
Self-supervised learning aims to learn representations from the data itself without explicit manual supervision. Existing efforts ignore a crucial aspect of self-supervised learnin…
Large-scale weakly-supervised pre-training for video action recognition
Deepti Ghadiyaram, Matt Feiszli, Du Tran +3
Current fully-supervised video datasets consist of only a few hundred thousand videos and fewer than a thousand domain-specific labels. This hinders the progress towards advanced v…
Billion-scale semi-supervised learning for image classification
I. Zeki Yalniz, Hervé Jégou, Kan Chen +2
This paper presents a study of semi-supervised learning with large convolutional networks. We propose a pipeline, based on a teacher/student paradigm, that leverages a large collec…
Activity Driven Weakly Supervised Object Detection
Zhenheng Yang, Dhruv Mahajan, Deepti Ghadiyaram +2
Weakly supervised object detection aims at reducing the amount of supervision required to train detection models. Such models are traditionally learned from images/videos labelled…
Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Search
Abhimanyu Dubey, Laurens van der Maaten, Zeki Yalniz +2
A plethora of recent work has shown that convolutional networks are not robust to adversarial images: images that are created by perturbing a sample from the data distribution as t…
Exploring the Limits of Weakly Supervised Pretraining
Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan +5
State-of-the-art visual perception models for a wide range of tasks rely on supervised pretraining. ImageNet classification is the de facto pretraining task for these models. Yet,…