Infrared small target detection (IRSTD) plays a crucial role in numerous military and civilian applications. However, existing methods often face the gradual degradation of target edge pixels as the number of network layers increases, and traditional convolution struggles to differentiate between frequency components during feature extraction, leading to low-frequency backgrounds interfering with high-frequency targets and high-frequency noise triggering false detections. To address these limitations, we propose MDAFNet (Multi-scale Differential Edge and Adaptive Frequency Guided Network for Infrared Small Target Detection), which integrates the Multi-Scale Differential Edge (MSDE) module and Dual-Domain Adaptive Feature Enhancement (DAFE) module. The MSDE module, through a multi-scale edge extraction and enhancement mechanism, effectively compensates for the cumulative loss of target edge information during downsampling. The DAFE module combines frequency domain processing mechanisms with simulated frequency decomposition and fusion mechanisms in the spatial domain to effectively improve the network’s capability to adaptively enhance high-frequency targets and selectively suppress high-frequency noise. Experimental results on multiple datasets demonstrate the superior detection performance of MDAFNet.

Scene text reading is a crucial task for scene understanding. Text detection, as a fundamental task in scene text reading, has recently garnered significant attention. Among various approaches, segmentation-based methods stand out for their flexible pixel-level prediction capabilities. However, two main issues remain. 1) These methods treat all text instances as a pixel set during training, causing the features of large-scale instances to dominate the model optimization process. As a result, the optimization deviates from the instance-level objectives. 2) Segmentation methods filter candidates based on pixel-level class scores, whereas what is needed is an evaluation of whether an instance is text, which also deviates from the original goals. To address these issues, we propose an Instance-Equal Feature Guide Module (IEFGM), a Cross-Level Feature Interaction Module (CLIFM), and a Pixel-Instance Fusion Discriminator (PIFD) to balance optimization strategies with practical goals. The IEFGM introduces instance-level features and positional information, guiding the model to treat instances of different scales equally at the feature level. The CLIFM encourages feature interaction across different levels, enabling the model to recognize text from various perspectives. Unlike existing methods that filter candidates using pixel-level results, the PIFD integrates both instance-level and pixel-level information to identify candidate regions, aligning with the original goals of text detection. A series of ablation studies demonstrates the effectiveness of the proposed modules. Extensive experiments across six datasets from different scenes demonstrate that our method outperforms existing state-of-the-art approaches.

To pursue an efficient text assembling process, existing methods detect texts via the shrink-mask expansion strategy. However, the shrinking operation loses the visual features of text margins and confuses the foreground and background difference, which brings intrinsic limitations to recognize text features. We follow this issue and design Text-Pass Filter (TPF) for arbitrary-shaped text detection. It segments the whole text directly, which avoids the intrinsic limitations. It is noteworthy that different from previous whole text region-based methods, TPF can separate adhesive texts naturally without complex decoding or post-processing processes, which makes it possible for real-time text detection. Concretely, we find that the band-pass filter allows through components in a specified band of frequencies, called its passband but blocks components with frequencies above or below this band. It provides a natural idea for extracting whole texts separately. By simulating the band-pass filter, TPF constructs a unique feature-filter pair for each text. In the inference stage, every filter extracts the corresponding matched text by passing its pass-feature and blocking other features. Meanwhile, considering the large aspect ratio problem of ribbon-like texts makes it hard to recognize texts wholly, a Reinforcement Ensemble Unit (REU) is designed to enhance the feature consistency of the same text and to enlarge the filter's recognition field to help recognize whole texts. Furthermore, a Foreground Prior Unit (FPU) is introduced to encourage TPF to discriminate the difference between the foreground and background, which improves the feature-filter pair quality. Experiments demonstrate the effectiveness of REU and FPU while showing the TPF's superiority.

Egocentric interaction perception is one of the essential branches in investigating human-environment interaction, which lays the basis for developing next-generation intelligent systems. However, existing egocentric interaction understanding methods cannot yield coherent textual and pixel-level responses simultaneously according to user queries, which lacks flexibility for varying downstream application requirements. To comprehend egocentric interactions exhaustively, this paper presents a novel task named Egocentric Interaction Reasoning and pixel Grounding (Ego-IRG). Taking an egocentric image with the query as input, Ego-IRG is the first task that aims to resolve the interactions through three crucial steps: analyzing, answering, and pixel grounding, which results in fluent textual and fine-grained pixel-level responses. Another challenge is that existing datasets cannot meet the conditions for the Ego-IRG task. To address this limitation, this paper creates the Ego-IRGBench dataset based on extensive manual efforts, which includes over 20k egocentric images with 1.6 million queries and corresponding multimodal responses about interactions. Moreover, we design a unified ANNEXE model to generate text- and pixel-level outputs utilizing multimodal large language models, which enables a comprehensive interpretation of egocentric interactions. The experiments on the Ego-IRGBench exhibit the effectiveness of our ANNEXE model compared with other works.

Pursuing efficient text shape representations helps scene text detection models focus on compact foreground regions and optimize the contour reconstruction steps to simplify the whole detection pipeline. Current approaches either represent irregular shapes via box-to-polygon strategy or decomposing a contour into pieces for fitting gradually, the deficiency of coarse contours or complex pipelines always exists in these models. Considering the above issues, we introduceEdgeTextto fit text contours compactly while alleviating excessive contour rebuilding processes. Concretely, it is observed that the two long edges of texts can be regarded as smooth curves. It allows us to build contours via continuous and smooth edges that cover text regions tightly instead of fitting piecewise, which helps avoid the two limitations in current models. Inspired by this observation, EdgeText formulates the text representation as the edge approximation problem via parameterized curve fitting functions. In the inference stage, our model starts with locating text centers, and then creating curve functions for approximating text edges relying on the points. Meanwhile, truncation points are determined based on the location features. In the end, extracting curve segments from curve functions by using the pixel coordinate information brought by truncation points to reconstruct text contours. Furthermore, considering the deep dependency of EdgeText on text edges, a bilateral enhanced perception (BEP) module is designed. It encourages our model to pay attention to the recognition of edge features. Additionally, to accelerate the learning of the curve function parameters, we introduce a proportional integral loss (PI-loss) to force the proposed model to focus on the curve distribution and avoid being disturbed by text scales. Ablation experiments demonstrate that EdgeText can fit scene texts compactly and naturally. Comparisons show that EdgeText is superior to existing methods on multiple public datasets. Code is available at https://github.com/omtcyang/EdgeTD.

The rapid advancement of deep generative models (DGMs) has significantly advanced research in computer vision, providing a cost-effective alternative to acquiring vast quantities of expensive imagery. However, existing methods predominantly focus on synthesizing remote sensing (RS) images aligned with real images in a global layout view, which limits their applicability in RS image object detection (RSIOD) research. To address these challenges, we propose a multiclass and multiscale object (MMO) image generator based on DGMs, termed MMO-IG, designed to generate RS images with supervised object labels from global and local aspects simultaneously. Specifically, from the local view, MMO-IG encodes various RS instances using an iso-spacing instance map (ISIM). During the generation process, it decodes each instance region with iso-spacing value in ISIM—corresponding to both background and foreground instances—to produce RS images through the denoising process of diffusion models. Considering the complex interdependencies among MMOs, we construct a spatial-cross dependence knowledge graph (SCDKG). This ensures a realistic and reliable multidirectional distribution among MMOs for region embedding, thereby reducing the discrepancy between source and target domains. Besides, we propose a structured object distribution instruction (SODI) to guide the generation of synthesized RS image content from a global aspect with SCDKG-based ISIM together. Extensive experimental results demonstrate that our MMO-IG exhibits superior generation capabilities for RS images with dense MMO-supervised labels, and RS detectors pretrained with MMO-IG show excellent performance on real-world datasets. Code is available athttps://github.com/omtcyang/MMO-IG.

SAR-to-RGB translation, which transforms Synthetic Aperture Radar (SAR) images into visually interpretable RGB counterparts, is critical for enhancing applications in visual analysis, deep learning, and multi-source data fusion. However, existing methods often fail to preserve both global structural integrity and fine-grained local textures. This deficiency stems from weak feature extraction and the lack of a robust layout framework, leading to outputs with information loss, geometric distortions, and unnatural textures. To overcome these limitations, we propose the Robust Layout-based Iterative Diffusion Model (RLI-DM), a novel three-stage framework for high-fidelity translation. The framework begins with an Optical Reconstruction Module that employs a conditional diffusion model to ensure precise spectral mapping. At its core, the Geometric Robustness Module leverages a Brownian bridge model that we train to derive a noise-resilient layout, overcoming the limitations of conventional edge detection and significantly enhancing global structural fidelity. Finally, this robust layout guides a Customized Multi-Level Refinement Module to iteratively reconstruct local textures, ensuring structural clarity and cross-feature consistency. Extensive experiments on multiple benchmark datasets demonstrate that RLI-DM achieves state-of-the-art performance, significantly outperforming existing methods in both structural integrity and perceptual quality.

Scene text reading plays a crucial role in scene understanding. As its precondition task, scene text detection has garnered increasing interest from researchers. Segmentation-based text detection methods have gained prominence due to their adaptable pixel-level predictions. Many existing methods predict the shrink mask and utilize the Vatti clipping algorithm to reconstruct text contours. However, the shrink mask only focuses on the global geometry feature and shrinks the same distance everywhere, which neglects local contour information and disrupts the instance shape feature. In addition, the post-processing based on the Vatti clipping algorithm heavily relies on the predictions and is relatively complex, causing suboptimal performance in both detection accuracy and efficiency. To address the above problems, we propose an efficient and effective method named Magnetic Text Detector (MTD), inspired by magnetism. It is constructed by a text representation method flexible mask (FM) and a magnetic pull module (MPM). Unlike the shrink mask and concentric mask, the former concerns the local contours and shrinks unfixed distances on different positions, which avoids the truncation issue while preserving distinctiveness from the text regions. The latter generates magnetic fields and pulls pole points of FM to the text contour by magnetism. This allows accurate reconstruction of text contours, even when predictions deviate from the actual text severely, while saving 50% of the post-processing time approximately. Several ablation studies verify the effectiveness of the proposed FM and MPM. Extensive experiments show that our MTD achieves state-of-the-art (SOTA) methods on multiple datasets from different scenes. The code is available at https://github.com/fengmulin/MTD.

Texts on the intelligent transportation scene include mass information. Fully harnessing this information is one of the critical drivers for advancing intelligent transportation. Unlike the general scene, detecting text in transportation has extra demand, such as a fast inference speed, except for high accuracy. Most existing real-time text detection methods are based on the shrink mask, which loses some geometry semantic information and needs complex post-processing. In addition, the previous method usually focuses on correct output, which ignores feature correction and lacks guidance during the intermediate process. To this end, we propose an efficient multi-scene text detector that contains an effective text representation similar mask (SM) and a feature correction module (FCM). Unlike previous methods, the former aims to preserve the geometric information of the instances as much as possible. Its post-progressing saves 50% of the time, accurately and efficiently reconstructing text contours. The latter encourages false positive features to move away from the positive feature center, optimizing the predictions from the feature level. Some ablation studies demonstrate the efficiency of the SM and the effectiveness of the FCM. Moreover, the deficiency of existing traffic datasets (such as the low-quality annotation or closed source data unavailability) motivated us to collect and annotate a traffic text dataset, which introduces motion blur. In addition, to validate the scene robustness of the SM-Net, we conduct experiments on traffic, industrial, and natural scene datasets. Extensive experiments verify it achieves (SOTA) performance on several benchmarks. The code and dataset are available at:https://github.com/fengmulin/SMNet.

Traffic signs play a key role in assisting autonomous driving systems (ADS) by enabling the assessment of vehicle behavior in compliance with traffic regulations and providing navigation instructions. However, current works are limited to basic sign understanding without considering the egocentric vehicle’s spatial position, which fails to support further regulation assessment and direction navigation. Following the above issues, we introduce a new task: traffic sign interpretation from the vehicle’s first-person view, referred to asTSI-FPV. Meanwhile, we develop a traffic guidance assistant (TGA) scenario application to re-explore the role of traffic signs in ADS as a complement to popular autonomous technologies (such as obstacle perception). Notably, TGA is not a replacement for electronic map navigation; rather, TGA can be an automatic tool for updating it and complementing it in situations such as offline conditions or temporary sign adjustments. Lastly, a spatial and semantic logic-aware stepwise reasoning pipeline (SignEye) is constructed to achieve the TSI-FPV and TGA, and an application-specific dataset (Traffic-CN) is built. Experiments show that TSI-FPV and TGA are achievable via our SignEye trained on Traffic-CN. The results also demonstrate that the TGA can provide complementary information to ADS beyond existing popular autonomous technologies.

Due to the diversity of scene text in aspects such as font, color, shape, and size, accurately and efficiently detecting text is still a formidable challenge. Among the various detection approaches, segmentation-based approaches have emerged as prominent contenders owing to their flexible pixel-level predictions. However, these methods typically model text instances in a bottom-up manner, which is highly susceptible to noise. In addition, the prediction of pixels is isolated without introducing pixel-feature interaction, which also influences the detection performance. To alleviate these problems, we propose a multi-information level arbitrary-shaped text detector consisting of a focus entirety module (FEM) and a perceive environment module (PEM). The former extracts instance-level features and adopts a top-down scheme to model texts to reduce the influence of noises. Specifically, it assigns consistent entirety information to pixels within the same instance to improve their cohesion. In addition, it emphasizes the scale information, enabling the model to distinguish varying scale texts effectively. The latter extracts region-level information and encourages the model to focus on the distribution of positive samples in the vicinity of a pixel, which perceives environment information. It treats the kernel pixels as positive samples and helps the model differentiate text and kernel features. Extensive experiments demonstrate the FEM's ability to efficiently support the model in handling different scale texts and confirm the PEM can assist in perceiving pixels more accurately by focusing on pixel vicinities. Comparisons show the proposed model outperforms existing state-of-the-art approaches on four public datasets.

The irregular contour representation is one of the tough challenges in scene text detection. Although segmentation-based methods have achieved significant progress with the help of flexible pixel prediction, the overlap of geographically close texts hinders detecting them separately. To alleviate this problem, some shrink-based methods predict text kernels and expand them to restructure texts. However, the text kernel is an artificial object with incomplete semantic features that are prone to incorrect or missing detection. In addition, different from the general objects, the geometry features (aspect ratio, scale, and shape) of scene texts vary significantly, which makes it difficult to detect them accurately. To consider the above problems, we propose an effective spotlight text detector (STD), which consists of a spotlight calibration module (SCM) and a multivariate information extraction module (MIEM). The former concentrates efforts on the candidate kernel, like a camera focus on the target. It obtains candidate features through a mapping filter and calibrates them precisely to eliminate some false positive samples. The latter designs different shape schemes to explore multiple geometric features for scene texts. It helps extract various spatial relationships to improve the model's ability to recognize kernel regions. Ablation studies prove the effectiveness of the designed SCM and MIEM. Extensive experiments verify that our STD is superior to existing state-of-the-art methods on various datasets, including ICDAR2015, CTW1500, MSRA-TD500, and Total-Text.

Most existing traffic sign-related works are dedicated to detecting and recognizing part of traffic signs separately, which fails to analyze the global semantic logic among signs and may convey inaccurate traffic instruction information. Following the above issues, we propose a traffic sign interpretation (TSI) task, which aims to interpret global semantic interrelated traffic signs (e.g., driving instruction-related texts, symbols, and guide panels) into a natural language for providing complete traffic instruction support to autonomous or assistant driving. Meanwhile, considering the lack of an effective framework for the proposed TSI task in existing works, we design a multi-task learning architecture (TSI-arch) to detect and recognize various traffic signs with drastic changes in sizes and aspect ratios. Meanwhile interpreting these signs into a natural language like a human according to Chinese design criteria of road traffic signs. Furthermore, the absence of a public TSI available dataset prompts us to build a traffic sign interpretation dataset, namely TSI-CN. The dataset consists of real road scene images, which are captured from the highway and the urban way in China from a driver’s perspective. It contains rich location labels of texts, symbols, and guide panels, and the corresponding natural language description labels. Experiments on our TSI-CN dataset demonstrate that the TSI task is achievable and the TSI architecture can interpret traffic signs from scenes successfully even if there is a complicated semantic logic among signs.

To pursue comprehensive performance, recent text detectors improve detection speed at the expense of accuracy. They adopt shrink-mask-based text representation strategies, which leads to a high dependence of detection accuracy on shrink-masks. Unfortunately, three disadvantages cause unreliable shrink-masks. Specifically, these methods try to strengthen the discrimination of shrink-masks from the background by semantic information. However, the feature defocusing phenomenon that coarse layers are optimized by fine-grained objectives limits the extraction of semantic features. Meanwhile, since both shrink-masks and the margins belong to texts, the detail loss phenomenon that the margins are ignored hinders the distinguishment of shrink-masks from the margins, which causes ambiguous shrink-mask edges. Moreover, false-positive samples enjoy similar visual features with shrink-masks. They aggravate the decline of shrink-masks recognition. To avoid the above problems, we propose a zoom text detector (ZTD) inspired by the zoom process of the camera. Specifically, zoomed-out view module (ZOM) is introduced to provide coarse-grained optimization objectives for coarse layers to avoid feature defocusing. Meanwhile, zoomed-in view module (ZIM) is presented to enhance the margins recognition to prevent detail loss. Furthermore, sequential-visual discriminator (SVD) is designed to suppress false-positive samples by sequential and visual features. Experiments verify the superior comprehensive performance of ZTD.

Recently, segmentation-based text detection methods develop rapidly, which achieve competitive accuracy and detection speed. However, these methods are hard to fit text instances accurately, which leads to the decrease of model performance. Meanwhile, the poor perception of the text center by the boundary pixels further affects the detection accuracy. We follow the issues and design an efficient framework for arbitrary-shaped text detection, which is constructed based on Optimal Kernel Representation (OKR) and Pixel Enhancement Module (PEM). Specifically, OKR is proposed to fit texts with optimal kernels. It erodes texts according to the corresponding geometric characteristics, which is simpler and more accurate compared with previous methods. PEM is used to enhance the perception of boundary pixels to the virtual character centers of text, thus improving the cohesion of the whole instance. Particularly, PEM only participates in the training process, which brings no extra computation costs to inference. Ablation experiments show the effectiveness of OKR and PEM. Comparisons on serveral benchmarks verify that our efficient detector is superior to the existing state-of-the-art (SOTA) methods.

Video frame interpolation aims to improve the video quality by increasing the frame rate. Existing methods adopt the cascaded architecture. They first estimate intermediate flow maps and then refine the synthesized intermediate frames with contextual features separately. However, the separated flow estimation and refined module ignore the mutual facilitation of them in frame interpolation. Following this issue, we propose a Dual-Stream Fused Network (DSF-Net) to joint flow estimation and refinement module for frame interpolation. Specifically, it first extracts the contextual features from input frames by a contextual feature extractor module, and then jointly refines the intermediate flow maps with the extracted features through a coarse-to-fine frame synthesis module. DSF-Net allows the intermediate flow and the contextual features to benefit each other while generating sharper moving objects and capturing better textual details. Experimental results demonstrate that DSF-Net performs consistently better than existing SOTA methods.

Existing real-time text detectors reconstruct text contours by shrink-masks only. Though they simplify the framework and can make the model run fast, the strong dependence on shrink-masks leads to unreliable detection results (e.g., miss detection and overdetection). Moreover, these methods ignore the information from surrounding pixels, which causes sensitive shrink-masks and accelerates the reliability decline of detection results. Considering the above problems, we construct an effective and efficient text detection network, termed as Reinforcement Shrink-Mask for Text Detection (RSMTD), which strengthens the model's ability to recognize texts while enjoying a high detection speed. Specifically, an effective text representation strategy (Reinforcement Shrink-Mask, RSM) is designed to decouple texts and shrink-masks. RSM builds texts through shrink-masks and reinforcement offsets to ensure stable detection results encountering shrink-masks that deviate from the ground-truth. It is worth noting that reinforcement offsets can force our method to focus on the foreground shapes to bring precise shrink-mask edges. For the robustness improvement of shrink-masks, Super-pixel Window (SPW) is proposed to encourage RSMTD to utilize the surroundings of each pixel to predict shrink-masks. Particularly, SPW treats the interval regions between texts and shrink-masks as background, which helps to suppress interval regions and to avoid text adhesion. Moreover, a lightweight feature merging branch is constructed to further accelerate the inference process. As demonstrated in the experiments, our method is superior to existing state-of-the-art (SOTA) methods in both detection accuracy and speed on multiple benchmarks.

Irregular-shaped texts bring challenges to Scene Text Detection (STD). Although existing regression-based approaches achieve comparable performances, they fail to cover some highly curved ribbon-like text lines. Inspired by morphology, we found that the leaf vein can easily cover various geometries. Specifically, lateral and thin veins are emitted to margin along main vein gradually with the leaf growth. This process can decompose a concave object into consecutive convex regions, which are easier to fit. Hence, the leaf vein is suitable for representing highly curved texts. Considering the aforementioned advantage, we design a leaf vein-based text representation method (LVT), where text contour is treated as leaf margin and represented through main, lateral, and thin veins. We further construct a detection framework based on LVT, namely LeafText. In the text reconstruction stage, LeafText simulates the leaf growth process to rebuild text contours. It grows main veins in Cartesian coordinates to locate texts roughly at first. Then, lateral and thin veins are generated along the main vein growth direction in polar coordinates. They are responsible for generating the coarse contour and refining it, respectively. Meanwhile, Multi-Oriented Smoother (MOS) is designed to smooth the main vein for ensuring reliable growth directions of lateral and thin veins. Additionally, a global incentive loss is proposed to enhance the predictions of lateral and thin veins. Ablation experiments demonstrate LVT can fit irregular-shaped texts precisely and verify the effectiveness of MOS and global incentive loss. Comparisons show that LeafText is superior to existing state-of-the-art (SOTA) methods on MSRA-TD500, CTW1500, Total-Text, and ICDAR2015 datasets.

Detecting irregular-shaped text instances is the main challenge for text detection. Existing approaches can be roughly divided into top-down and bottom-up perspective methods. The former encodes text contours into unified units, which always fails to fit highly curved text contours. The latter represents text instances by a number of local units, where the complicated network and post-processing lead to slow detection speed. In this paper, to detect arbitrary-shaped text instances with high detection accuracy and speed simultaneously, we propose a Bidirectional Perspective strategy based Network (BiP-Net). Specifically, a new text representation strategy is proposed to represent text contours from a topdown perspective, which can fit highly curved text contours effectively. Moreover, a contour connecting (CC) algorithm is proposed to avoid the information loss of text contours by rebuilding interval contours from a bottom-up perspective. The experimental results on MSRA-TD500, CTW1500, and ICDAR2015 datasets demonstrate the superiority of BiP-Net against several state-of-the-art methods.

Recently fast arbitrary-shaped text detection has become an attractive research topic. However, most existing methods are non-real-time, which may fall short in intelligent systems. Although a few real-time text methods are proposed, the detection accuracy is far behind non-real-time methods. To improve the detection accuracy and speed simultaneously, we propose a novel fast and accurate text detection framework, namely CM-Net, which is constructed based on a new text representation method and a multi-perspective feature (MPF) module. The former can fit arbitrary-shaped text contours by concentric mask (CM) in an efficient and robust way. The latter encourages the network to learn more CM-related discriminative features from multiple perspectives and brings no extra computational cost. Benefiting the advantages of CM and MPF, the proposed CM-Net only needs to predict one CM of the text instance to rebuild the text contour and achieves the best balance between detection accuracy and speed compared with previous works. Moreover, to ensure that multi-perspective features are effectively learned, the multi-factor constraints loss is proposed. Extensive experiments demonstrate the proposed CM is efficient and robust to fit arbitrary-shaped text instances, and also validate the effectiveness of MPF and constraints loss for discriminative text features recognition. Furthermore, experimental results show that the proposed CM-Net is superior to existing state-of-the-art (SOTA) real-time text detection methods in both detection speed and accuracy on MSRA-TD500, CTW1500, Total-Text, and ICDAR2015 datasets.