Face anti-spoofing (FAS) plays a critical role in ensuring the security of face recognition system from different kinds of presentation attacks. Most existing FAS research faces several limitations: 1) insufficient consideration of the role of local fine-grained information, 2) the assumption that spoofing patterns are uniformly distributed across the entire image, neglecting the uneven distribution of spoofing clues, and 3) an overemphasis on intra-domain scenarios, leading to limited generalization capabilities for unseen domains. In this paper, we propose a Salient Patch-Clue Aware Network (SPAN) for cross-domain face anti-spoofing to tackle the aforementioned issues. Specifically, we use all patches cropped from the complete image as input to the FAS network, enabling the network to focus on local information while avoiding information loss. Additionally, we propose a patch perception mechanism to extract key regions containing salient spoofing clues, such as reflections and edges, thereby reducing interference from irrelevant information. Furthermore, we introduce a pixel perception mechanism to capture finer-grained details. Based on these two mechanisms, we design a Salient Clue Perception Module (SCPM). We conduct cross-domain experiments on CASIA-FASD, Idiap Replay-Attack, MSU-MFSD, and OULU-NPU datasets. Our method achieves state-of-the-art HTER on seven protocols, especially excelling on M&I to C and M&I to O, surpassing the second place by 9.79% and 6.19%, showcasing strong generalization capability. The codes are available at https://github.com/SPAN2025/SPAN.

Task inharmony problem commonly occurs in modern object detectors, leading to inconsistent qualities between classification and regression tasks. The predicted boxes with high classification scores but poor localization positions or low classification scores but accurate localization positions will worsen the performance of detectors after Non-Maximum Suppression. Furthermore, when object detectors collaborate with Quantization- Aware Training (QAT), we observe that the task inharmony problem will be further exacerbated, which is considered one of the main causes of the performance degradation of quantized detectors. To tackle this issue, we propose the Harmonious Quantization for Object Detection (HQOD) framework, which consists of two components. Firstly, we propose a task-correlated loss to encourage detectors to focus on improving samples with lower task harmony quality during QAT. Secondly, a harmonious Intersection over Union (IoU) loss is incorporated to balance the optimization of the regression branch across different IoU levels. The proposed HQOD can be easily integrated into different QAT algorithms and detectors. Remarkably, on the MS COCO dataset, our 4-bit ATSS with ResNet-50 backbone achieves a state-of-the- art mAP of 39.6%, even surpassing the full-precision one. Codes are available at https://github.com/Menace-Dragon/VP-QOD.

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.

Non-autoregressive (NAR) transformer models have been studied intensively in automatic speech recognition (ASR), and many NAR transformer models is to use the causal mask to limit token dependencies. However, the causal mask is designed for the left-to-right decoding process of the non-parallel autoregressive (AR) transformer, which is inappropriate for the parallel NAR transformer since it ignores the right-to-left contexts. Some methods are proposed to utilize right-to-left contexts with an extra decoder, but these methods increase the model complexity. To tackle the above problems, we propose a new non-autoregressive transformer with a unified bidirectional decoder (NAT-UBD), which can simultaneously utilize left-to-right and right-to-left contexts for ASR. However, direct use of bidirectional contexts will cause information leakage, which means the decoder output can be affected by the character information of the input in the same position. To avoid information leakage, we propose a novel attention mask and modify vanilla queries, keys, and values matrices for NAT-UBD. Experimental results verify that NAT-UBD can achieve character error rates (CERs) of 5.0%/5.5% on the Aishell-1 dev/test sets, outperforming all previous NAR transformer models. Moreover, NAT-UBD can run 49.8× faster than the AR transformer baseline when decoding in a single step.

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.

Face anti-spoofing is a vital part to protect the security of face recognition systems. Many existing face anti-spoofing methods rely on convolutional neural networks (CNNs) and achieve competitive performance. However, due to the power of CNNs, these methods will extract information that is irrelevant to spoof patterns, such as acquisition equipment and environmental characteristics, which makes the network vulnerable to changes of the illumination or camera. In this work, we propose a plug-and-play module called DyAttention, which can improve the robustness against environmental changes. Moreover, we build a network named DANet with DyAttention, which can accurately capture the spoof patterns from coarse to fine. DANet can dynamically capture the texture differences between live and spoof samples in the facial area. Specifically, we use the spatial attention mechanism to generate a mask of the facial area. Then, we extract the intrinsic texture patterns and piecewise enhance them via dynamic activation for clean representation, where the texture patterns are not affected by the environmental and domain factors. Through experiments on three benchmark datasets, our DANet achieves state-of-the-art intra-dataset accuracy on CASIA-MFSD, Replay-Attack, and OULU-NPU. Meanwhile, DANet can enhance the cross-dataset performance between CASIA-MFSD and Replay-Attack, improving the average HTER by 1.3%.

Multi-scale object detection in natural scenes is still challenging. To enhance the multi-scale perception capability, some algorithms combine the lower-level and higher-level information via multi-scale feature fusion strategies. However, the inherent spatial properties among instances and relations between foreground and background are ignored. In addition, the human-defined “center-based” regression quality evaluation strategy, predicting a high-to-low score based on a linear relationship with the distance to the center of ground-truth box, is not robust to scale-variant objects. In this work, we propose a Depth-Guided Progressive Network (DGPNet) for multi-scale object detection. Specifically, besides the prediction of classification and localization, the depth is estimated and used to guide the image features in a weighted manner to obtain a better spatial representation. Therefore, depth estimation and 2D object detection are simultaneously learned via a unified network, where the depth features are merged as auxiliary information into the detection branch to enhance the discrimination among multi-scale objects. Moreover, to overcome the difficulty of empirically fitting the localization quality function, high-quality predicted boxes on scale-variant objects are more adaptively obtained by an IoU-aware progressive sampling strategy. We divide the sampling process into two stages, i.e., “statistical-aware” and “IoU-aware”. The former selects thresholds for positive samples based on statistical characteristics of multi-scale instances, and the latter further selects high-quality samples by IoU on the basis of the former. Therefore, the final ranking scores better reflect the quality of localization. Experiments verify that our method outperforms state-of-the-art methods on the KINS and Cityscapes dataset.

Vehicle and license plate detection plays an important role in intelligent transportation systems and is still a challenging task in real applications, such as on-road scenarios. Recently, Convolutional Neural Network (CNN)-based detectors achieve the state-of-the-art performance. However, it is difficult to efficiently detect the vehicle and license plate simultaneously in most cases. With a single network, the vehicle can affect the detection of the license plate due to the inclusion relation. In this paper, we propose an end-to-end deep neural network for detecting the vehicle and the license plate simultaneously in a given image, where two separate branches with different convolutional layers are designed for vehicle detection and license plate detection, respectively. In consideration of the license plate's small size and fairly obvious features as well as the vehicle's various size and rather complex features, the license plates are detected with low-level features and the vehicles are localized with multi-level features in corresponding convolutional layers. Moreover, a task-specific anchor design strategy is employed to obtain better predictions. Besides, the attention mechanisms and feature-fusion strategies are utilized to improve the detection performance of small-scale objects. A variety of experiments on real datasets and public datasets verify that our proposed method has fairly high accuracy and efficiency.