activity
20172025
most citedSqueezeformer: An Efficient Transformer for Automatic Speech Recognition

75 citations · 303 across the 30 of their papers we have counts for

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Showing 2022Show all

6 papers · 1 filter

cs.CL2022★ 2 cited

Re-evaluating the Need for Multimodal Signals in Unsupervised Grammar Induction

Boyi Li, Rodolfo Corona, Karttikeya Mangalam +7

Are multimodal inputs necessary for grammar induction? Recent work has shown that multimodal training inputs can improve grammar induction. However, these improvements are based on…

cs.CV2022★ 1 cited

Re^2TAL: Rewiring Pretrained Video Backbones for Reversible Temporal Action Localization

Chen Zhao, Shuming Liu, Karttikeya Mangalam +1

Temporal action localization (TAL) requires long-form reasoning to predict actions of various durations and complex content. Given limited GPU memory, training TAL end to end (i.e.…

cs.CV2022★ 1 cited

Structured Video Tokens @ Ego4D PNR Temporal Localization Challenge 2022

Elad Ben-Avraham, Roei Herzig, Karttikeya Mangalam +5

This technical report describes the SViT approach for the Ego4D Point of No Return (PNR) Temporal Localization Challenge. We propose a learning framework StructureViT (SViT for sho…

cs.CV2022★ 8 cited

Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens

Elad Ben-Avraham, Roei Herzig, Karttikeya Mangalam +5

Recent action recognition models have achieved impressive results by integrating objects, their locations and interactions. However, obtaining dense structured annotations for each…

eess.AS2022★ 75 cited

Squeezeformer: An Efficient Transformer for Automatic Speech Recognition

Sehoon Kim, Amir Gholami, Albert Shaw +5

The recently proposed Conformer model has become the de facto backbone model for various downstream speech tasks based on its hybrid attention-convolution architecture that capture…

cs.CV2022★ 9 cited

MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition

Chao-Yuan Wu, Yanghao Li, Karttikeya Mangalam +4

While today's video recognition systems parse snapshots or short clips accurately, they cannot connect the dots and reason across a longer range of time yet. Most existing video ar…