Bangla sign language recognition using concatenated BdSL network

Sign language is the only medium of communication for the hearing impaired and the deaf and dumb community. Communication with the general mass is thus always a challenge for this minority group. Especially in Bangla sign language (BdSL), there are 38 alphabets with some having nearly identical symbols. As a result, in BdSL recognition, the posture of hand is an important factor in addition to visual features extracted from traditional Convolutional Neural Network (CNN). In this paper, a novel architecture "Concatenated BdSL Network" is proposed which consists of a CNN based image network and a pose estimation network. While the image network gets the visual features, the relative positions of hand keypoints are taken by the pose estimation network to obtain the additional features to deal with the complexity of the BdSL symbols. A score of 91.51% was achieved by this novel approach in test set and the effectiveness of the additional pose estimation network is suggested by the experimental results.

Semantically Sensible Video Captioning (SSVC)

Video captioning, i.e. the task of generating captions from video sequences creates a bridge between the Natural Language Processing and Computer Vision domains of computer science. Generating a semantically accurate description of a video is an arduous task. Considering the complexity of the problem, the results obtained in recent researches are quite outstanding. But still there is plenty of scope for improvement. This paper addresses this scope and proposes a novel solution. Most video captioning models comprise of two sequential/recurrent layers - one as a video-to-context encoder and the other as a context-to-caption decoder. This paper proposes a novel architecture, SSVC (Semantically Sensible Video Captioning) which modifies the context generation mechanism by using two novel approaches - "stacked attention" and "spatial hard pull". For evaluating the proposed architecture, along with the BLEU scoring metric for quantitative analysis, we have used a human evaluation metric for qualitative analysis. This paper refers to this proposed human evaluation metric as the Semantic Sensibility (SS) scoring metric. SS score overcomes the shortcomings of common automated scoring metrics. This paper reports that the use of the aforementioned novelties improves the performance of the state-of-the-art architectures.