GL-RG: Global-Local Representation Granularity for Video Captioning

Azad, Amitoz

doi:10.24963/ijcai.2022/384

Cited by 40 publications

(2 citation statements)

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“…The results showed enhanced performance in comparison to the two-stage method in terms of accuracy and inference speed using the YouTube-VIS dataset. Yan et al [14] proposed a Global-Local Representation Granularity (GL-RG) scheme in the domain of video captioning. The proposed approach improved the modeling of the representation of global-local vision across video frames for caption generation.…”

Section: Literature Reviewmentioning

confidence: 99%

Emotional Speaker Verification Using Novel Modified Capsule Neural Network

Nassif

Shahin²,

Nemmour³

et al. 2023

Mathematics

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Capsule Neural Network (CapsNet) models are regarded as efficient substitutes for convolutional neural networks (CNN) due to their powerful hierarchical representation capability. Nevertheless, CNN endure their inability of recording spatial information in spectrograms. The main constraint of CapsNet is related to the compression method which can be implemented in CNN models but cannot be directly employed in CapsNet. As a result, we propose a novel architecture based on dual-channel long short-term memory compressed CapsNet (DC-LSTM–COMP CapsNet) for speaker verification in emotional as well as stressful talking environments. The proposed approach is perceived as a modified Capsule network that attempts to overcome the limitations that exist within the original CapsNet, as well as in CNN while enhancing the verification performance. The proposed architecture is assessed on four distinct databases. The experimental analysis reveals that the average speaker verification performance is improved in comparison with CNN, the original CapsNet, as well as the conventional classifiers. The proposed algorithm notably achieves the best verification accuracy across the four speech databases. For example, using the Emirati dataset, the average percentage equal error rates (EERs) obtained is 10.50%, based on the proposed architecture which outperforms other deep and classical models.

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Section: Literature Reviewmentioning

confidence: 99%

Emotional Speaker Verification Using Novel Modified Capsule Neural Network

Nassif

Shahin²,

Nemmour³

et al. 2023

Mathematics

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“…In the past decade, the computer vision community has achieved significant progress in many tasks with the development of deep learning [1]- [4]. Among various visual tasks, instance segmentation [5] has drawn wide attention due to its importance in many emerging applications, such as autonomous driving [6]- [11], augmented reality [12], [13], and video captioning [14], [15]. Technically, it is quite challenging as it is a compound task consisting of both object detection and segmentation, each of which is a difficult task and has been studied for a long time.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation

Cui,

Han,

Liu

2024

ACM J. Auton. Transport. Syst.

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The advancement of computer vision has pushed visual analysis tasks from still images to the video domain. In recent years, video instance segmentation, which aims to track and segment multiple objects in video frames, has drawn much attention for its potential applications in various emerging areas such as autonomous driving, intelligent transportation, and smart retail. In this paper, we propose an effective framework for instance-level visual analysis on video frames, which can simultaneously conduct object detection, instance segmentation, and multi-object tracking. The core idea of our method is collaborative multi-task learning which is achieved by a novel structure, named associative connections among detection, segmentation, and tracking task heads in an end-to-end learnable CNN. These additional connections allow information propagation across multiple related tasks, so as to benefit these tasks simultaneously. We evaluate the proposed method extensively on KITTI MOTS and MOTS Challenge datasets and obtain quite encouraging results.

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