In recent years, scene text detection research has increasingly focused on arbitrary-shaped texts, where text representation is a fundamental problem. However, most existing methods still struggle to separate adjacent or overlapping texts due to ambiguous spatial positions of points or segmentation masks. Besides, the time efficiency of the entire pipeline is often neglected, resulting in sub-optimal inference speed. To tackle these problems, we first propose a novel text representation method based on robust subspace recovery, which robustly represents complex text shapes by combining orthogonal basis vectors learned from labeled text contours. These basis vectors capture basis contour patterns with distinct information, enabling clearer boundaries even in densely populated text scenarios. Moreover, we propose a dynamic sparse assignment scheme for positive samples that adaptively adjusts their weights during training, which not only accelerates inference speed by eliminating redundant predictions but also enhances feature learning by providing sufficient supervision signals. Building on these innovations, we present TextRSR, an accurate and efficient scene text detection network. Extensive experiments on challenging benchmarks demonstrate the superior accuracy and efficiency of TextRSR compared to state-of-the-art methods. Particularly, TextRSR achieves an F-measure of 88.5% at 37.8 frames per second (FPS) for CTW1500 dataset and an F-measure of 89.1% at 23.1 FPS for Total-Text dataset.

Mirror detection is a challenging task, due to the reflective properties of mirrors. Most existing approaches rely on exploiting the relationship between the content inside the mirror and the surrounding environment to aid in locating mirrors. A typical solution is to utilize contextual contrasted features. However, the discontinuity in content at the edges of mirrors may not always be prominent. To overcome this limitation, we propose a novel mirror detection framework called S2MD including two main modules, multi-directional similarity perception module (MSPM) and spectral saliency enhancement decoder module (SSEDM). Specifically, we employ a backbone network to extract multi-scale global information from images using a dual-path approach. Then, we feed these high-level dual-path features into MSPMs to generate direction-sensitive similarity-consistent features. MSPM utilizes active rotating filters and oriented response pooling to model the similarity relations in different orientations. Moreover, the SSEDM is utilized to enhance the spatial contextual contrasted features using feature spectral residuals and fuse the dual-path features to obtain the final predicted mirror mask. Extensive experiments demonstrate that our method achieves state-of-the-art performance on challenging MSD, PMD, and RGBD-Mirror benchmarks. The code is available at https://github.com/RuiChen-stack/M2SD.

In recent years, the lookup tables (LUTs) with deep learning for image enhancement have achieved remarkable results with extremely high inference efficiency. However, when dealing with severely degraded low-light images, lookup-table-based methods tend to exhibit poor enhancement results due to the lack of contextual and global information. To address the limitations of current lookup-table-based methods in the low-light image enhancement task, we propose the novel Wide Vision Lookup Tables (WV-LUT) by introducing Complementary-Hierarchical 4D-LUTs into 3D-LUT, which allows 3D-LUT to have a wider range of vision. Specifically, the 4D-LUTs are used to expand the receptive field and process local information on a single channel, while a 3D-LUT is used for sRGB channel post-processing. Additionally, we propose a lightweight Global Adjustment Module that further enhances the performance and generalization of WV-LUT by obtaining global adjustment parameters for gamma and color correction matrix to adaptively process images. Experimental results demonstrate that our method outperforms other state-of-the-art methods in low-light image enhancement with the highest average ranking and superior inference efficiency. Furthermore, deployment experiments on mobile devices demonstrate that our WV-LUT achieves superior results and inference efficiency, showcasing promising application prospects for edge devices.

Facial micro-expression recognition (MER) is a challenging task, due to the transience, subtlety, and dynamics of micro-expressions (MEs). Most existing methods resort to hand-crafted features or deep networks, in which the former often additionally requires key frames, and the latter suffers from small-scale and low-diversity training data. In this article, we develop a novel fine-grained dynamic perception (FDP) framework for MER. We propose to rank frame-level features of a sequence of raw frames in chronological order, in which the rank process encodes the dynamic information of both ME appearances and motions. Specifically, a novel local-global feature-aware transformer is proposed for frame representation learning. A rank scorer is further adopted to calculate rank scores of each frame-level feature. Afterwards, the rank features from rank scorer are pooled in temporal dimension to capture dynamic representation. Finally, the dynamic representation is shared by a MER module and a dynamic image construction module, in which the former predicts the ME category, and the latter uses an encoder-decoder structure to construct the dynamic image. The design of dynamic image construction task is beneficial for capturing facial subtle actions associated with MEs and alleviating the data scarcity issue. Extensive experiments show that our method (i) significantly outperforms the state-of-the-art MER methods, and (ii) works well for dynamic image construction. Particularly, our FDP improves by 4.05%, 2.50%, 7.71%, and 2.11% over the previous best results in terms of F1-score on the CASME II, SAMM, CAS(ME) 2 , and CAS(ME) 3 datasets, respectively. The code is available at https://github.com/CYF-cuber/FDP .

Sharding technique is viewed as the most promising solution to improving blockchain scalability. However, to implement a sharded blockchain, developers have to address two major challenges. The first challenge is that the ratio of cross-shard transactions (TXs) across blockchain shards is very high. This issue significantly degrades the throughput of a blockchain. The second challenge is that the workloads across blockchain shards are largely imbalanced. If workloads are imbalanced, some shards have to handle an overwhelming number of TXs and become congested very possibly. Facing these two challenges, a dilemma is that it is difficult to guarantee a low cross-shard TX ratio and maintain the workload balance across all shards, simultaneously. We believe that a fine-grained account-allocation strategy can address this dilemma. To this end, we first formulate the tradeoff between such two metrics as a network-partition problem. We then solve this problem using a community-aware account partition algorithm. Furthermore, we also propose a sharding protocol, named Transformers, to apply the proposed algorithm into the sharded blockchain system. Finally, trace-driven evaluation results demonstrate that the proposed protocol outperforms other baselines in terms of throughput, latency, cross-shard TX ratio, and the queue size of transaction pool.

Bitcoin is currently the cryptocurrency with the largest market share. Many previous studies have explored the security of Bitcoin from the perspective of blockchain mining. Especially on the double-spending attacks (DSA), some state-of-the-art studies have proposed various analytical models, aiming to understand the insights behind the double-spending attacks. However, we believe that advanced versions of DSA can be developed to create new threats for the Bitcoin ecosystem. To this end, this paper mainly presents a new type of double-spending attack named Adaptive DSA in the context of the Bitcoin blockchain, and discloses the associated insights. In our analytical model, the double-spending attack is converted into a Markov Decision Process. We then exploit the Stochastic Dynamic Programming (SDP) approach to obtain the optimal attack strategies towards Adaptive DSA. Through the proposed analytical model and the disclosed insights behind Adaptive DSA, we aim to alert the Bitcoin ecosystem that the threat of double-spending attacks is still at a dangerous level.

Purely rotation-based self-calibration receives the most attention among various self-calibration methods owing to its algorithmic simplicity. However, it is actually impossible to ensure that the camera motion for this kind of self-calibration is a pure rotation. Thus, significant errors of calibrating results could be inevitably introduced because of ignoring nonzero translation. In this paper, we propose a practical and effective approach to improve the purely rotation-based self-calibration approach. According to the fact that the rotational angles between images have a very strong impact on the calibrating errors from the translations, we compute the rotational angles between images prior to calibrating, and then use different and very appropriate strategies for self-calibration in different angle circumstances. Real data has been used to validate the proposed approach.