110 citations · 142 across the 7 of their papers we have counts for
7 papers
C2F-TCN: A Framework for Semi and Fully Supervised Temporal Action Segmentation
Dipika Singhania, Rahul Rahaman, Angela Yao
Temporal action segmentation tags action labels for every frame in an input untrimmed video containing multiple actions in a sequence. For the task of temporal action segmentation,…
A Generalized & Robust Framework For Timestamp Supervision in Temporal Action Segmentation
Rahul Rahaman, Dipika Singhania, Alexandre Thiery +1
In temporal action segmentation, Timestamp supervision requires only a handful of labelled frames per video sequence. For unlabelled frames, previous works rely on assigning hard l…
Assembly101: A Large-Scale Multi-View Video Dataset for Understanding Procedural Activities
Fadime Sener, Dibyadip Chatterjee, Daniel Shelepov +4
Assembly101 is a new procedural activity dataset featuring 4321 videos of people assembling and disassembling 101 "take-apart" toy vehicles. Participants work without fixed instruc…
Iterative Contrast-Classify For Semi-supervised Temporal Action Segmentation
Dipika Singhania, Rahul Rahaman, Angela Yao
Temporal action segmentation classifies the action of each frame in (long) video sequences. Due to the high cost of frame-wise labeling, we propose the first semi-supervised method…
Coarse to Fine Multi-Resolution Temporal Convolutional Network
Dipika Singhania, Rahul Rahaman, Angela Yao
Temporal convolutional networks (TCNs) are a commonly used architecture for temporal video segmentation. TCNs however, tend to suffer from over-segmentation errors and require addi…
Temporal Aggregate Representations for Long-Range Video Understanding
Fadime Sener, Dipika Singhania, Angela Yao
Future prediction, especially in long-range videos, requires reasoning from current and past observations. In this work, we address questions of temporal extent, scaling, and level…
Rethinking CNN Models for Audio Classification
Kamalesh Palanisamy, Dipika Singhania, Angela Yao
In this paper, we show that ImageNet-Pretrained standard deep CNN models can be used as strong baseline networks for audio classification. Even though there is a significant differ…