Search Results for author: Yasar Abbas Ur Rehman

Found 8 papers, 6 papers with code

Exploring Federated Self-Supervised Learning for General Purpose Audio Understanding

no code implementations5 Feb 2024 Yasar Abbas Ur Rehman, Kin Wai Lau, Yuyang Xie, Lan Ma, Jiajun Shen

The integration of Federated Learning (FL) and Self-supervised Learning (SSL) offers a unique and synergetic combination to exploit the audio data for general-purpose audio understanding, without compromising user data privacy.

Federated Learning Retrieval +1

Large Separable Kernel Attention: Rethinking the Large Kernel Attention Design in CNN

1 code implementation4 Sep 2023 Kin Wai Lau, Lai-Man Po, Yasar Abbas Ur Rehman

Our extensive experimental results show that the proposed LSKA module in VAN provides a significant reduction in computational complexity and memory footprints with increasing kernel size while outperforming ViTs, ConvNeXt, and providing similar performance compared to the LKA module in VAN on object recognition, object detection, semantic segmentation, and robustness tests.

object-detection Object Detection +2

AudioInceptionNeXt: TCL AI LAB Submission to EPIC-SOUND Audio-Based-Interaction-Recognition Challenge 2023

1 code implementation14 Jul 2023 Kin Wai Lau, Yasar Abbas Ur Rehman, Yuyang Xie, Lan Ma

This report presents the technical details of our submission to the 2023 Epic-Kitchen EPIC-SOUNDS Audio-Based Interaction Recognition Challenge.

Federated Self-supervised Learning for Video Understanding

2 code implementations5 Jul 2022 Yasar Abbas Ur Rehman, Yan Gao, Jiajun Shen, Pedro Porto Buarque de Gusmao, Nicholas Lane

The ubiquity of camera-enabled mobile devices has lead to large amounts of unlabelled video data being produced at the edge.

 Ranked #1 on Action Recognition on UCF-101 (Accuracy metric)

Action Recognition Federated Learning +3

VCGAN: Video Colorization with Hybrid Generative Adversarial Network

1 code implementation26 Apr 2021 Yuzhi Zhao, Lai-Man Po, Wing-Yin Yu, Yasar Abbas Ur Rehman, Mengyang Liu, Yujia Zhang, Weifeng Ou

We propose a hybrid recurrent Video Colorization with Hybrid Generative Adversarial Network (VCGAN), an improved approach to video colorization using end-to-end learning.

Colorization Generative Adversarial Network +1

SCGAN: Saliency Map-guided Colorization with Generative Adversarial Network

1 code implementation23 Nov 2020 Yuzhi Zhao, Lai-Man Po, Kwok-Wai Cheung, Wing-Yin Yu, Yasar Abbas Ur Rehman

It jointly predicts the colorization and saliency map to minimize semantic confusion and color bleeding in the colorized image.

Colorization Decoder +1

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