Poster

Two-Stream Network for Sign Language Recognition and Translation

Yutong Chen · Ronglai Zuo · Fangyun Wei · Yu Wu · Shujie LIU · Brian Mak

Hall J #136

Keywords: [ sign language translation ] [ sign language recognition ]

[ Abstract ]
[ Poster [ OpenReview
Thu 1 Dec 2 p.m. PST — 4 p.m. PST
 
Spotlight presentation: Lightning Talks 6A-4
Thu 8 Dec 6:30 p.m. PST — 6:45 p.m. PST

Abstract:

Sign languages are visual languages using manual articulations and non-manual elements to convey information. For sign language recognition and translation, the majority of existing approaches directly encode RGB videos into hidden representations. RGB videos, however, are raw signals with substantial visual redundancy, leading the encoder to overlook the key information for sign language understanding. To mitigate this problem and better incorporate domain knowledge, such as handshape and body movement, we introduce a dual visual encoder containing two separate streams to model both the raw videos and the keypoint sequences generated by an off-the-shelf keypoint estimator. To make the two streams interact with each other, we explore a variety of techniques, including bidirectional lateral connection, sign pyramid network with auxiliary supervision, and frame-level self-distillation. The resulting model is called TwoStream-SLR, which is competent for sign language recognition (SLR). TwoStream-SLR is extended to a sign language translation (SLT) model, TwoStream-SLT, by simply attaching an extra translation network. Experimentally, our TwoStream-SLR and TwoStream-SLT achieve state-of-the-art performance on SLR and SLT tasks across a series of datasets including Phoenix-2014, Phoenix-2014T, and CSL-Daily.

Chat is not available.