TL;DR
The proposed LTTL combines the power of meta-learning and self-training, achieving superior performance compared with the baseline methods on two public benchmarks, and uses the meta-learning paradigm to optimize the parameters in the whole framework.
Few-Shot Image Classification (FSL) aims to identify and classify new image categories using only a very small number of labeled samples. Addressing the shortcomings of existing meta-learning and metric learning methods, such as insufficient generalization of feature representations with very few samples, overfitting,...
This survey presents a seven-category taxonomy of self-supervised learning methods covering: 1) input reconstruction or restoration, 2) context prediction, 3) contrastive learning, 4) feature clustering, 5) self-distillation-based feature reconstruction, 6) redundancy reduction, and 7) masked image modeling.
In semi-supervised object detection (SSOD), due to the limited availability of labeled data, the quality and quantity of pseudo labels generated from unlabeled images are crucial for model training. Our study reveals that in the early stages of training, the number of usable pseudo labels is very low, which hampers the...
Supervised object detection models rely on large training datasets to achieve good performance. However, data labeling is often an expensive and time-consuming task. To mitigate this challenge, self-supervised methods have been utilized to learn representations from unlabeled data. Accordingly, this study uses self-sup...
Results on the UC Merced (UCM) and NWPU benchmarks indicate that SE-CLIP significantly outperforms existing semi-supervised approaches and provides a viable solution for adapting VLMs to the remote sensing domain with minimal human intervention.
Few-shot image classification remains difficult because a model must identify novel classes from only one or a few labeled examples while preserving discriminative local information. Metric-learning methods based on Earth Mover’s Distance (EMD) improve local correspondence by representing an image as a set of regional...
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