Manual annotation is costly and limits the availability of sufficient annotated license plates for training recognition models. Small-scale license plate datasets (i.e., low-resource) often exhibit a long-tailed distribution in character classes at some character positions, primarily due to their limited variation in character permutations. Previous methods tend to prioritize head classes with high occurrence probability when applied to small-scale datasets. To solve this problem, we propose feature shuffling to balance the occurrence distribution across various character classes, thereby improving the recognition of tail classes with low occurrence probability. Moreover, we introduce global perception to holistically understand the overall character layout for effective feature shuffling. Extensive experiments on the small-scale UFPR and SSIG-SegPlate datasets demonstrate that our method achieves state-of-the-art results, with an average improvement of 43.70% over the baseline. Experiments on RodoSol and CCPD prove our method achieves state-of-the-art performance on large-scale datasets, verifying its generality.

Previous license plate recognition (LPR) methods have achieved impressive performance on single-type license plates. However, multi-type license plate recognition is still challenging due to various character layouts and fonts. There are two main problems: one is that recognition models are prone to incorrectly perceive the location of characters due to diverse character layouts, and the other is that characters of different categories may have similar glyphs due to various fonts, causing character misidentification. Therefore, to solve the above problems, we propose two plug-and-play modules based on an attention-based framework for multi-type license plate recognition. First, we propose a global modeling module to integrate character layout information to precisely perceive the location of characters, thus generating accurate predictions. Second, a position-aware contrastive learning module is proposed to enhance the robustness and discriminability of features to alleviate character misidentification of similar glyphs. Finally, to verify the effectiveness and generality, we apply the proposed modules to six baseline models, and the results demonstrate that the proposed method can achieve state-of-the-art performance on three multi-type license plate datasets. Moreover, extensive experiments prove that our proposed modules can significantly improve performance by 6.8% on RODOSOL-ALPR with a small parameter increase.

Self-attention plays a significant role in recent automatic speech recognition (ASR) models with promising results. However, it suffers from high computational complexity and weak capability in modeling local information. In contrast, the convolutional neural network (CNN) is computationally effective and superior in learning local information. Whereas it fails in self-interaction and capturing long-range dependence among input tokens. Accordingly, we take their complementary advantages and propose a new module, namely self-convolution, to compensate for each individual limitations. Specifically, self-convolution generates convolution kernels at each token (to model local information) which are then used to convolve itself (for self-interaction). Moreover, we bring in global information during the generation of convolution kernel to enhance the learning of long-range dependencies. In this way, the advantages of self-attention and CNN are both utilized. We conduct rigorous experiments on LibriSpeech, Tedlium2, and AIShell1 datasets and demonstrate that our proposed self-convolution can achieve superior ASR performance than self-attention with less computational cost.

For fine-grained classification, it is extremely difficult and costly to acquire the annotated data. Hence, some studies propose to use web data for fine-grained classification. However, the web data contains tremendous noisy labels, which can affect the classification results. Although many previous studies propose to discard noisy data via sample selection, they also discard some valid data. The valid data denotes hard or mislabeled samples that can enhance the robustness of the model. To solve the above problems, we propose a novel method to discard irrelevant noisy data from web data while keeping valid data for fine-grained classification. Specifically, we divide the web data into clean and noisy samples and then distinguish the noisy samples into open-set and close-set noises. Finally, the model is constructed in a semi-supervised manner, where the clean samples are used as the labeled set, and the close-set noises are used as the unlabeled set. Extensive experiments verify that our method can improve the classification performance by an average of 1.89% on three fine-grained benchmark datasets compared with the current methods. The experimental results prove the effectiveness of the combination of sample selection and semi-supervised training strategy.

Due to the angle variations especially in unconstrained scenarios, face detection and alignment have become challenging tasks. In existing methods, face detection and alignment are always conducted separately, which can greatly increase the computation cost. Moreover, this separation will abandon the inherent correlation underlying the two tasks. In this paper, we propose a simple but effective architecture, named Angle-Sensitivity Cascaded Networks (ASCN), for jointly conducting rotation-invariance face detection and alignment. ASCN mainly consists of three consecutive cascaded networks. Specifically, in the first stage, the rotation angle is predicted and candidate bounding boxes are proposed simultaneously. In the second stage, ASCN further refines the candidates and orientations. In the last stage, ASCN jointly learns the accurate bounding boxes and alignment. Besides, for accurately locating landmarks in hard examples, we introduce a pose-equitable loss to balance the faces with large poses. Extensive experiments conducted on benchmark datasets demonstrate the surprising performance of our method. Notably, our method maintains real-time efficiency for both detection and alignment tasks on the ordinary CPU platform.