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Found 9 papers, 4 papers with code
InstructVEdit: A Holistic Approach for Instructional Video Editing
Video editing according to instructions is a highly challenging task due to the difficulty in collecting large-scale, high-quality edited video pair data.
Research on gesture recognition method based on SEDCNN-SVM
The DCNN can automatically extract and learn the feature information of sEMG through the convolution operation of the convolutional layer, so that it can capture the complex and high-level features in the data.
Unleashing the Power of Generic Segmentation Models: A Simple Baseline for Infrared Small Target Detection
Recent advancements in deep learning have greatly advanced the field of infrared small object detection (IRSTD).
IRSAM: Advancing Segment Anything Model for Infrared Small Target Detection
The recent Segment Anything Model (SAM) is a significant advancement in natural image segmentation, exhibiting potent zero-shot performance suitable for various downstream image segmentation tasks.
Digital Twin-assisted Reinforcement Learning for Resource-aware Microservice Offloading in Edge Computing
To address this challenge, we formulate an online joint microservice offloading and bandwidth allocation problem, JMOBA, to minimize the average completion time of services.
ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution
Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation.
SAR-to-Optical Image Translation via Thermodynamics-inspired Network
Synthetic aperture radar (SAR) is prevalent in the remote sensing field but is difficult to interpret in human visual perception.
ISNet: Shape Matters for Infrared Small Target Detection
TOAA block calculates the low-level information with attention mechanism in both row and column directions and fuses it with the high-level information to capture the shape characteristic of targets and suppress noises.
E-Tree Learning: A Novel Decentralized Model Learning Framework for Edge AI
In this paper, we propose a novel decentralized model learning approach, namely E-Tree, which makes use of a well-designed tree structure imposed on the edge devices.
