activity
20162026
most citedCoarse to Fine Multi-Resolution Temporal Convolutional Network

21 citations · 106 across the 33 of their papers we have counts for

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Showing 2020Show all

6 papers · 1 filter

cs.CV2020

Multi-Stage Fusion for One-Click Segmentation

Soumajit Majumder, Ansh Khurana, Abhinav Rai +1

Segmenting objects of interest in an image is an essential building block of applications such as photo-editing and image analysis. Under interactive settings, one should achieve g…

cs.CV2020

Localized Interactive Instance Segmentation

Soumajit Majumder, Angela Yao

In current interactive instance segmentation works, the user is granted a free hand when providing clicks to segment an object; clicks are allowed on background pixels and other ob…

cs.CV2020★ 7 cited

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…

cs.CV2020

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…

cs.LG2020

Neural network compression via learnable wavelet transforms

Moritz Wolter, Shaohui Lin, Angela Yao

Wavelets are well known for data compression, yet have rarely been applied to the compression of neural networks. This paper shows how the fast wavelet transform can be used to com…

cs.CV2020

Measuring Generalisation to Unseen Viewpoints, Articulations, Shapes and Objects for 3D Hand Pose Estimation under Hand-Object Interaction

Anil Armagan, Guillermo Garcia-Hernando, Seungryul Baek +32

We study how well different types of approaches generalise in the task of 3D hand pose estimation under single hand scenarios and hand-object interaction. We show that the accuracy…