Search Results for author: Vignesh Ramanathan

Found 17 papers, 1 papers with code

Event-LSTM: An Unsupervised and Asynchronous Learning-based Representation for Event-based Data

no code implementations10 May 2021 Lakshmi Annamalai, Vignesh Ramanathan, Chetan Singh Thakur

Compared to competing supervised approaches, ours is a task-agnostic approach ideally suited for the event domain, where task specific labeled data is scarce.

Activity Recognition Gesture Recognition

Adaptive Methods for Real-World Domain Generalization

no code implementations CVPR 2021 Abhimanyu Dubey, Vignesh Ramanathan, Alex Pentland, Dhruv Mahajan

We show that the existing approaches either do not scale to this dataset or underperform compared to the simple baseline of training a model on the union of data from all training domains.

Domain Generalization

Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency

no code implementations CVPR 2021 Qing Liu, Vignesh Ramanathan, Dhruv Mahajan, Alan Yuille, Zhenheng Yang

However, existing approaches which rely only on image-level class labels predominantly suffer from errors due to (a) partial segmentation of objects and (b) missing object predictions.

Instance Segmentation Semantic Segmentation +1

PreDet: Large-Scale Weakly Supervised Pre-Training for Detection

no code implementations ICCV 2021 Vignesh Ramanathan, Rui Wang, Dhruv Mahajan

State-of-the-art object detection approaches typically rely on pre-trained classification models to achieve better performance and faster convergence.

Classification Contrastive Learning +2

What leads to generalization of object proposals?

no code implementations13 Aug 2020 Rui Wang, Dhruv Mahajan, Vignesh Ramanathan

It is lucrative to train a good proposal model, that generalizes to unseen classes.

Object Proposal Generation

DLWL: Improving Detection for Lowshot Classes With Weakly Labelled Data

no code implementations CVPR 2020 Vignesh Ramanathan, Rui Wang, Dhruv Mahajan

This requires a detection framework that can be jointly trained with limited number of bounding box annotated images and large number of weakly labelled images.

Learning to Learn from Noisy Web Videos

no code implementations CVPR 2017 Serena Yeung, Vignesh Ramanathan, Olga Russakovsky, Liyue Shen, Greg Mori, Li Fei-Fei

Our method uses Q-learning to learn a data labeling policy on a small labeled training dataset, and then uses this to automatically label noisy web data for new visual concepts.

Action Recognition Q-Learning

Detecting events and key actors in multi-person videos

no code implementations CVPR 2016 Vignesh Ramanathan, Jonathan Huang, Sami Abu-El-Haija, Alexander Gorban, Kevin Murphy, Li Fei-Fei

In this paper, we propose a model which learns to detect events in such videos while automatically "attending" to the people responsible for the event.

Event Detection General Classification

Socially-aware Large-scale Crowd Forecasting

no code implementations CVPR 2014 Alexandre Alahi, Vignesh Ramanathan, Li Fei-Fei

In crowded spaces such as city centers or train stations, human mobility looks complex, but is often influenced only by a few causes.

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