Few-shot learning are methods and scenarios learned from a small amount of labeled data. While recent meta-metric learning methods have made significant progress, there are still questions about what is the key point of these methods and how they work. To address these problems, in this paper, we evaluate the effects of different parts in classical models. To be specific, we 1) use four typical networks AlexNet, VGG16, GoogLeNet, and ResNet50 to replace the original feature extraction part in the Matching Network, Prototypical Network, and Relation Network, and compare the best results with 17 state-of-the-art meta-metric learning algorithms. 2) fix the feature extraction part of the Matching Network, Prototypical Network and Relation Network, and change the similarity measurement part of each into L1, L2, Cosine. 3) conduct above three models on datasets of different granularity. The experimental results show that for all models evaluated, the addition of non-pretrained networks will make the classification results worse, which shows that it is easy to overfit when using deep networks for few-shot learning. Changes in similarity measurement methods have a significant impact on results, which shows the importance to choose a suitable measurement. Moreover, there are differences in performance on different granularity datasets.

All-in-one image restoration has recently developed to be a new research trend in the low-level computer vision field, aiming to tackle multiple image degradation types simultaneously in a unified model. As a typical multi-task learning, existing approaches focus on modeling either the specificity or commonality among different image restoration tasks. To exploit the unique strengths of both worlds, we propose a method of Image Pyramid Transformer coupled with Information Loss Regularization (IPT-ILR), in which the multi-scale architecture structure can excavate more information for multiple restoration tasks concurrently, while the learning strategy can identify the difference among multiple restoration tasks depending on the degree of information loss in each restoration task. Specifically, it first establishes a new Image Pyramid Transformer Network (IPT-Network) to accommodate multiple image restoration tasks. Given original degraded images, the IPT-Network exploits the image pyramid technique to establish a series of images with different scales, which are then restored by transformer-like auto-encoders. Moreover, the restored image on a low-level scale is referenced to assist restoring the degraded image on a high-level scale. Next, Information Loss Regularization (ILR) is presented to optimize the IPT-Network. ILR calculates the average distance between degraded images and their clean counterparts as the weights, which automatically implement different penalties for different image restoration tasks, thus avoiding the short-cut phenomenon for the easy task while encouraging the hard task. Extensive experiments have been conducted with 6 image restoration tasks in the all-in-one setting. The results show our method performs favorably against numerous state-of-the-art methods across most tasks, including image denoising, image deblurring, image dehazing, image deraining, image desnowing, as well as low-light enhancement.

In this paper, we propose a multistage KNN collaborative coding based Bag-of-Feature (MKCC-BoF) method to address SSPP problem, which tries to weaken the semantic gap between facial features and facial identification. First, local descriptors are extracted from the single training face images and a visual dictionary is obtained offline by clustering a large set of descriptors with K-means. Then, we design a multistage KNN collaborative coding scheme to project local features into the semantic space, which is much more efficient than the most commonly used non-negative sparse coding algorithm in face recognition. To describe the spatial information as well as reduce the feature dimension, the encoded features are then pooled on spatial pyramid cells by max-pooling, which generates a histogram of visual words to represent a face image. Finally, a SVM classifier based on linear kernel is trained with the concatenated features from pooling results. Experimental results on three public face databases show that the proposed MKCC-BoF is much superior to those specially designed methods for SSPP problem. Moreover, it also has great robustness to expression, illumination, occlusion and, time variation.