8 citations · 18 across the 5 of their papers we have counts for
8 papers · 1 filter
Event-LSTM: An Unsupervised and Asynchronous Learning-based Representation for Event-based Data
Lakshmi Annamalai, Vignesh Ramanathan, Chetan Singh Thakur
Event cameras are activity-driven bio-inspired vision sensors, thereby resulting in advantages such as sparsity,high temporal resolution, low latency, and power consumption. Given…
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…
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…
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,…