Search Results for author:
Found 8 papers, 6 papers with code
Exploring Federated Self-Supervised Learning for General Purpose Audio Understanding
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.
Large Separable Kernel Attention: Rethinking the Large Kernel Attention Design in CNN
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.
AudioInceptionNeXt: TCL AI LAB Submission to EPIC-SOUND Audio-Based-Interaction-Recognition Challenge 2023
This report presents the technical details of our submission to the 2023 Epic-Kitchen EPIC-SOUNDS Audio-Based Interaction Recognition Challenge.
L-DAWA: Layer-wise Divergence Aware Weight Aggregation in Federated Self-Supervised Visual Representation Learning
The ubiquity of camera-enabled devices has led to large amounts of unlabeled image data being produced at the edge.
Federated Self-supervised Learning for Video Understanding
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)
VCGAN: Video Colorization with Hybrid Generative Adversarial Network
We propose a hybrid recurrent Video Colorization with Hybrid Generative Adversarial Network (VCGAN), an improved approach to video colorization using end-to-end learning.
SCGAN: Saliency Map-guided Colorization with Generative Adversarial Network
It jointly predicts the colorization and saliency map to minimize semantic confusion and color bleeding in the colorized image.
UDC 2020 Challenge on Image Restoration of Under-Display Camera: Methods and Results
The results in the paper are state-of-the-art restoration performance of Under-Display Camera Restoration.
