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Found 12 papers, 4 papers with code
Meta-Auxiliary Learning for Micro-Expression Recognition
Micro-expressions (MEs) are involuntary movements revealing people's hidden feelings, which has attracted numerous interests for its objectivity in emotion detection.
Intriguing Properties of Positional Encoding in Time Series Forecasting
Motivated by these findings, we introduce two new PEs: Temporal Position Encoding (T-PE) for temporal tokens and Variable Positional Encoding (V-PE) for variable tokens.
Hacking Task Confounder in Meta-Learning
However, our experiments reveal an unexpected result: there is negative knowledge transfer between tasks, affecting generalization performance.
Unleash Model Potential: Bootstrapped Meta Self-supervised Learning
The long-term goal of machine learning is to learn general visual representations from a small amount of data without supervision, mimicking three advantages of human cognition: i) no need for labels, ii) robustness to data scarcity, and iii) learning from experience.
CSSL-RHA: Contrastive Self-Supervised Learning for Robust Handwriting Authentication
In this paper, we propose a novel Contrastive Self-Supervised Learning framework for Robust Handwriting Authentication (CSSL-RHA) to address these issues.
Towards Task Sampler Learning for Meta-Learning
We obtain three conclusions: (i) there is no universal task sampling strategy that can guarantee the optimal performance of meta-learning models; (ii) over-constraining task diversity may incur the risk of under-fitting or over-fitting during training; and (iii) the generalization performance of meta-learning models are affected by task diversity, task entropy, and task difficulty.
Towards the Sparseness of Projection Head in Self-Supervised Learning
In recent years, self-supervised learning (SSL) has emerged as a promising approach for extracting valuable representations from unlabeled data.
Text-based Person Search without Parallel Image-Text Data
Text-based person search (TBPS) aims to retrieve the images of the target person from a large image gallery based on a given natural language description.
Awesome-META+: Meta-Learning Research and Learning Platform
The project aims to promote the development of meta-learning and the expansion of the community, including but not limited to the following functions: 1) Complete and reliable meta-learning framework, which can adapt to multi-field tasks such as target detection, image classification, and reinforcement learning.
Efficient Image-Text Retrieval via Keyword-Guided Pre-Screening
The proposed framework equipped with only two embedding layers achieves $O(1)$ querying time complexity, while improving the retrieval efficiency and keeping its performance, when applied prior to the common image-text retrieval methods.
SSD-Faster Net: A Hybrid Network for Industrial Defect Inspection
For the former, we propose a novel slice localization mechanism to help SSD scan quickly.
UTD-Yolov5: A Real-time Underwater Targets Detection Method based on Attention Improved YOLOv5
In order to make the network more efficient, we also propose optimization methods such as WBF and iterative refinement mechanism.
