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Found 10 papers, 3 papers with code
Affinity-Graph-Guided Contractive Learning for Pretext-Free Medical Image Segmentation with Minimal Annotation
The combination of semi-supervised learning (SemiSL) and contrastive learning (CL) has been successful in medical image segmentation with limited annotations.
SoccerNet 2024 Challenges Results
The SoccerNet 2024 challenges represent the fourth annual video understanding challenges organized by the SoccerNet team.
Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better
Hence, we propose a novel fine-tuned detector, Pecola, bridging metric-based and fine-tuned methods by contrastive learning on selective perturbation.
Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices.
Symphony Generation with Permutation Invariant Language Model
In this work, we propose a permutation invariant language model, SymphonyNet, as a solution for symbolic symphony music generation.
Ranked #1 on
Audio Generation
on Symphony music
Distributed Low Precision Training Without Mixed Precision
However, most low precision training solution is based on a mixed precision strategy.
Segmentation is All You Need
Region proposal mechanisms are essential for existing deep learning approaches to object detection in images.
Ranked #1 on
Head Detection
on Rebar Head
FoxNet: A Multi-face Alignment Method
Multi-face alignment aims to identify geometry structures of multiple faces in an image, and its performance is essential for the many practical tasks, such as face recognition, face tracking, and face animation.
Learning with Collaborative Neural Network Group by Reflection
CNNG is a progression of neural systems that work cooperatively to deal with various errands independently in a similar learning framework.
Bandwidth Reduction using Importance Weighted Pruning on Ring AllReduce
In order to save more communication bandwidth and preserve the accuracy on ring structure, which break the restrict as the node increase, we propose a new algorithm to measure the importance of gradients on large-scale cluster implementing ring all-reduce based on the size of the ratio of parameter calculation gradient to parameter value.
