Although great progress has been made in Camouflaged Object Detection (COD), it still faces challenges in complex real-world scenes. Existing methods are primarily designed for visible images but face limitations when detecting highly camouflaged or partially occluded objects. Integrating multiple complementary information sources, such as visible images and infrared images, is an effective way to improve the performance of COD. However, research in this field is limited by the lack of comprehensive and high-quality benchmark datasets. To solve this problem, a Visible-Infrared Artificial Camouflage (VIAC) dataset is constructed. Building on this dataset, we propose a novel Visible-Infrared Camouflaged Object Detection (VICOD) framework, termed the Confidence-Guided Fusion and Inpainting Network (CGFINet). The network utilizes a cross-modal collaborative fusion module (CMCF) to achieve adaptive integration of visible and infrared information. Simultaneously, low-confidence regions segmentation boundaries are refined by leveraging high-confidence pixel information within the confidence-driven inpainting module (CDIM). To focus on low-confidence areas, pixel-level uncertainty is incorporated into the loss function as a dynamic weight factor, which prompts the model to focus on high-uncertainty areas. Extensive experiments on VIAC demonstrate that our method achieves state-of-the-art performance, surpassing existing COD and visible-infrared SOD approaches.

Camouflaged object detection (COD), which aims to segment objects that are highly similar to their background, is a valuable yet challenging task. Due to the interference of clutter and noise in the background, existing methods often struggle to avoid misleading and accurately segment the camouflaged object. In this paper, we propose a novel Certainty-guided Reasoning and Refinement Network (CRRNet) for COD. The core idea is to first explicitly reason under accurate knowledge of camouflaged objects, and then refine the uncertain regions by a certainty propagation strategy. To achieve this, we innovatively propose two modules: the Certainty-guided Reasoning Module and Uncertain Region Refinement Module. Experimental results prove that the proposed CRRNet outperforms 12 state-of-the-art methods on three COD benchmark datasets.

Camouflaged object detection (COD) is a task of identifying and locating target objects that are camouflaged, masked, or confused. Research claims that depth cues can provide effective object location cues. However, depth images often contain noise interference, which may negatively affect object recognition. In addition, depth images also lack edge details, and most of previous works pay more attention to the integrity of the region, rather than the quality of the edge. To solve these two problems, we propose a joint edge and regional depth enhancement network (ERDENet) for Camouflaged Object Detection. The network first introduces the Locate and Generate Depth (LGD) module to locate the target region and generate its depth image. After that, the Feature Interactive Fusion (FIF) module is carried out, complementing the enhanced depth feature with the feature extracted from the original image, and then fuse the edge clues into it. Finally, we design a Muiti-modal Refinement Extraction (MRE) module to refine the feature to improve the detection performance. Extensive experiments show that our method has advantages and effectiveness.

To adapt to the resource-limited environment, this study introduces the lightweight boundary-aware camouflaged object detection(COD) network LMABnet. We enhance the feature representation capability of the lightweight network through a multi-scale feature fusion architecture, while effectively avoids the model inflation and parameter redundancy of traditional COD methods. The LMABnet adopts the streamlined two-branch architecture that incorporates with the lightweight feature extraction module, which provides the multi-scale feature perception capability and reduces the loss of spatial information caused by rapid downsampling. In addition, based on the principle of "easy before difficult", the lightweight multi-scale feature fusion module discovers the key parts of the camouflaged object through the details. Then enhances the recognition of the object edges by using the edge-attention fusion module, so as to improve the accuracy performance of detection. Empirical evaluations conducted on the COD dataset demonstrate that the proposed methodology not only attains state-of-the-art performance but also accomplishes a significant reduction of 40% in parameter count relative to existing approaches.

Non-Intrusive Load Monitoring (NILM) addresses the challenge of disaggregating total energy consumption into individual appliance usage, which is essential for enhancing energy efficiency and managing smart grids. Existing methods often overlook the impact of window sizes on the separation of appliance-level power signals, leading to appliance power aliasing (APA), where a single total power curve corresponds to multiple operating conditions and appliance couplings. A larger window captures global features noise, but it also exacerbates APA, thereby enlarging the solution space. In contrast, a smaller window mitigates APA but it introduces noise and increases complexity. To address these limitations, we propose the Multi-Scale Re-weighted Attention Feature Fusion (MRAFF) model, which employs a multi-scale framework to manage temporal variations and incorporates a re-weighted attention module to enhance multi-scale interactions, thereby improving feature representation. Experimental results demonstrate that our MRAFF model significantly outperforms baseline models on the REDD and UK-DALE datasets, achieving improvements ranging from 2.27% to 10.5% in MAE and from 33.74% to 58.34% in SAE.

Hyperspectral images had made many applications in the medical field with their rich spectral information. However, there were currently problems with feature extraction based on hyperspectral images, especially in extracting contextual feature information from spectral bands, and a single convolutional kernel may restrict the receptive field and inadequately capture the sequential properties of the data. Meanwhile, due to the large data volume of hyperspectral images, current medical hyperspectral images focus more on individual segmentation or classification. This paper proposed a 3D swin transformer with multi-task joint learning framework, to simultaneously learn multiple tasks for hyperspectral tongue images. Based on the 3D swin transformer model, the framework regards cross-band context feature learning of hyperspectral images as a sequence-to-sequence prediction process. The 3D transformer encoder used as the basic framework for shared feature extraction, and set up corresponding decoders for prediction according to different visual tasks such as segmentation and classification. We conducted simultaneous segmentation and classification tasks of the tongue coating region on the hyperspectral image of tongue images. The results showed that the proposed model had good results in single tasks of segmentation and classification, and performed better than other multi-task convolutional neural network models.

We present LawDIS, a language-window-based controllable dichotomous image segmentation (DIS) framework that produces high-quality object masks. Our framework recasts DIS as an image-conditioned mask generation task within a latent diffusion model, enabling seamless integration of user controls. LawDIS is enhanced with macro-to-micro control modes. Specifically, in macro mode, we introduce a language-controlled segmentation strategy (LS) to generate an initial mask based on user-provided language prompts. In micro mode, a window-controlled refinement strategy (WR) allows flexible refinement of user-defined regions (i.e., size-adjustable windows) within the initial mask. Coordinated by a mode switcher, these modes can operate independently or jointly, making the framework well-suited for high-accuracy, personalised applications. Extensive experiments on the DIS5K benchmark reveal that our LawDIS significantly outperforms 11 cutting-edge methods across all metrics. Notably, compared to the second-best model MVANet, we achieve $F_β^ω$ gains of 4.6\% with both the LS and WR strategies and 3.6\% gains with only the LS strategy on DIS-TE. Codes will be made available at https://github.com/XinyuYanTJU/LawDIS.

Camouflaged Object Detection (COD) is a visual task aimed at identifying objects hidden within their surroundings. Current methods often enhance detection accuracy through boundary structures or uncertainty guidance, but they frequently overlook the identification cues embedded in gradient features. Inspired by the high sensitivity of gradient features to differences between the object and the background, we introduce a novel Gradient-guided Attention Fusion Network (GAFNet) that emphasizes extracting valuable cues by focusing on gradient and high-frequency features of the object. In GAFNet, we propose a Deep Gradient Attention Fusion module (DGAF) to strengthen the response to multi-scale gradient features, utilizing the deep information of gradient features to further refine object localization and identification. Additionally, to globally capture more gradient feature and high-frequency feature cues, we design a High-Frequency Feature Perception module (HFFP) based on a deep residual attention mechanism. Our experimental results on three established COD datasets demonstrate that GAFNet significantly outperforms existing state-of-the-art methods.

Camouflaged Object Detection (COD) has historically been a significant challenge in the field of computer vision. Most existing methods for COD predominantly rely on complex designs to maximize the confidence of foreground regions within spatial features. In contrast, an alternative perspective is that suppressing background distractions to highlight the foreground might be a more effective approach. To address this issue, we propose Distraction Suppression Network, named DSNet. Specifically, Distracion Suppression Module (DSM) is implemented prior to the decoding stage to suppress the distracting information based on the Object-Related Information (ORI) extracted from Object Mining Module (OMM). Then, the Feature Modulation Decoder (FMD) modulates features with varing frequencies and obtains the prediction in a coarse-to-fine way. Experimental results show that our model outperforms existing state-of-the-art models on benchmark datasets by a large margin. Notably, our model maintains its performance even in more complex camouflage scenes.

In the biosphere, camouflaged objects take the advantage of visional wholeness by keeping the color and texture of the objects highly consistent with the background, thereby confusing the visual mechanism of other creatures and achieving a concealed effect. This is also the main reason why the task of camouflaged object detection is challenging. In this article, we break the visual wholeness and see through the camouflage from the perspective of matching the appropriate field of view. We propose a matching-recognition-refinement network (MRR-Net), which consists of two key modules, i.e., the visual field matching and recognition module (VFMRM) and the stepwise refinement module (SWRM). In the VFMRM, various feature receptive fields are used to match candidate areas of camouflaged objects of different sizes and shapes and adaptively activate and recognize the approximate area of the real camouflaged object. The SWRM then uses the features extracted by the backbone to gradually refine the camouflaged region obtained by VFMRM, thus yielding the complete camouflaged object. In addition, a more efficient deep supervision method is exploited, making the features from the backbone input into the SWRM more critical and not redundant. Extensive experimental results demonstrate that our MRR-Net runs in real-time (82.6 frames/s) and significantly outperforms 30 state-of-the-art models on three challenging datasets under three standard metrics. Furthermore, MRR-Net is applied to four downstream tasks of camouflaged object segmentation (COS), and the results validate its practical application value. Our code is publicly available at: https://github.com/XinyuYanTJU/MRR-Net.

Few-shot image classification(FSIC) aims to recognize novel classes given few labeled images from base classes. Recent works have achieved promising classification performance, especially for metric-learning methods, where a measure at only image feature level is usually used. In this paper, we argue that measure at such a level may not be effective enough to generalize from base to novel classes when using only a few images. Instead, a multi-level descriptor of an image is taken for consideration in this paper. We propose a multi-level correlation network (MLCN) for FSIC to tackle this problem by effectively capturing local information. Concretely, we present the self-correlation module and cross-correlation module to learn the semantic correspondence relation of local information based on learned representations. Moreover, we propose a pattern-correlation module to capture the pattern of fine-grained images and find relevant structural patterns between base classes and novel classes. Extensive experiments and analysis show the effectiveness of our proposed method on four widely-used FSIC benchmarks.

Recent deep learning based salient object detection methods with attention mechanisms have made great success. However, existing attention mechanisms can be generally separated into two categories. One part chooses to calculate weights indiscriminately, which yields computational redundancy. While one part focuses randomly on a small part of the images, such as hard attention, resulting in incorrectness owing to insufficiently targeted selection of a subset of tokens. To alleviate these problems, we design a Curiosity-driven Network (CNet) and a Curiosity-driven Learning Algorithm (CLA) based on fragment attention (FA) mechanism newly defined in this paper. FA imitates the process of cognition perception driven by human curiosity, and divides the degree of curiosity into three levels, i.e. curious, a little curious and not curious. These three levels correspond to five saliency degrees, including salient and non-salient, likewise salient and likewise non-salient, completely uncertain. With more knowledge gained by the network, CLA transforms the curiosity degree of each pixel to yield enhanced detail-enriched saliency maps. In order to extract more context-aware information of potential salient objects and make a better foundation for CLA, a high-level feature extraction module (HFEM) is further proposed. Based on the much better high-level features extracted by HFEM, FA can classify the curiosity degree for each pixel more reasonably and accurately. Extensive experiments on five popular datasets clearly demonstrate that our method outperforms the state-of-the-art approaches without any pre-processing operations or post-processing operations.

The pandemic of COVID-19 has become a global crisis in public health, which has led to a massive number of deaths and severe economic degradation. To suppress the spread of COVID-19, accurate diagnosis at an early stage is crucial. As the popularly used real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test can be lengthy and inaccurate, chest screening with radiography imaging is still preferred. However, due to limited image data and the difficulty of the early-stage diagnosis, existing models suffer from ineffective feature extraction and poor network convergence and optimisation. To tackle these issues, a segmentation-based COVID-19 classification network, namely SC2Net, is proposed for effective detection of the COVID-19 from chest x-ray (CXR) images. The SC2Net consists of two subnets: a COVID-19 lung segmentation network (CLSeg), and a spatial attention network (SANet). In order to supress the interference from the background, the CLSeg is first applied to segment the lung region from the CXR. The segmented lung region is then fed to the SANet for classification and diagnosis of the COVID-19. As a shallow yet effective classifier, SANet takes the ResNet-18 as the feature extractor and enhances high-level feature via the proposed spatial attention module. For performance evaluation, the COVIDGR 1.0 dataset is used, which is a high-quality dataset with various severity levels of the COVID-19. Experimental results have shown that, our SC2Net has an average accuracy of 84.23% and an average F1 score of 81.31% in detection of COVID-19, outperforming several state-of-the-art approaches.

Existing saliency detection techniques are sensitive to image quality and perform poorly on degraded images. In this paper, we systematically analyze the current status of the research on detecting salient objects from degraded images and then propose a new multi-type self-attention network, namely MSANet, for degraded saliency detection. The main contributions include: 1) Applying attention transfer learning to promote semantic detail perception and internal feature mining of the target network on degraded images; 2) Developing a multi-type self-attention mechanism to achieve the weight recalculation of multi-scale features. By computing global and local attention scores, we obtain the weighted features of different scales, effectively suppress the interference of noise and redundant information, and achieve a more complete boundary extraction. The proposed MSANet converts low-quality inputs to high-quality saliency maps directly in an end-to-end fashion. Experiments on seven widely-used datasets show that our approach produces good performance on both clear and degraded images.

Video salient object detection has been attracting more and more research interests recently. However, the definition of salient objects in videos has been controversial all the time, which has become a critical bottleneck in video salient object detection. Specifically, the sequential information contained in videos results in a fact that objects have a relative saliency ranking between each other rather than specific saliency. This implies that simply distinguishing objects into salient or not-salient as usual could not represent the information about saliency comprehensively. To address this issue, 1) in this paper we propose a completely new definition for the salient objects in videos---ranking salient objects, which considers relative saliency ranking assisted with eye fixation points. 2) Based on this definition, a ranking video salient object dataset(RVSOD) is built. 3) Leveraging our RVSOD, a novel neural network called Synthesized Video Saliency Network (SVSNet) is constructed to detect both traditional salient objects and human eye movements in videos. Finally, a ranking saliency module (RSM) takes the results of SVSNet as input to generate the ranking saliency maps. We hope our approach will serve as a baseline and lead to a conceptually new research in the field of video saliency.

Tongue diagnosis is an important concept in Traditional Chinese Medicine (TCM). The tongue colour and coating can aid understanding of the body's physiological mechanisms, as well as the pathology of diseases. Existing research has focused on using digital images and tongue colour classification, without considering the other visible bands of information in the tongue. In this study, a visible hyperspectral image system, with an approximate spectral range of 400–1000 nm, was used to predict the tongue colour values and the coating position in TCM, and a stacked autoencoder (SAE) predict model based on spectral–spatial feature was performed to digital the tongue colour space and the coating. The experimental results show the effectiveness of the spectral–spatial feature with SAE model in predicting the CIELAB values of L , a, and coating position, thus the authors provide a new technique for the objective and digitising development of TCM.

Data-driven saliency detection has attracted strong interest as a result of applying convolutional neural networks to the detection of eye fixations. Although a number of image-based salient object and fixation detection models have been proposed, video fixation detection still requires more exploration. Different from image analysis, motion and temporal information is a crucial factor affecting human attention when viewing video sequences. Although existing models based on local contrast and low-level features have been extensively researched, they failed to simultaneously consider interframe motion and temporal information across neighboring video frames, leading to unsatisfactory performance when handling complex scenes. To this end, we propose a novel and efficient video eye fixation detection model to improve the saliency detection performance. By simulating the memory mechanism and visual attention mechanism of human beings when watching a video, we propose a step-gained fully convolutional network by combining the memory information on the time axis with the motion information on the space axis while storing the saliency information of the current frame. The model is obtained through hierarchical training, which ensures the accuracy of the detection. Extensive experiments in comparison with 11 state-of-the-art methods are carried out, and the results show that our proposed model outperforms all 11 methods across a number of publicly available datasets.

Although extreme learning machine (ELM) has successfully been applied to a number of pattern recognition problems, only with the original ELM it can hardly yield high accuracy for the classification of hyperspectral images (HSIs) due to two main drawbacks. The first is due to the randomly generated initial weights and bias, which cannot guarantee optimal output of ELM. The second is the lack of spatial information in the classifier as the conventional ELM only utilizes spectral information for classification of HSI. To tackle these two problems, a new framework for ELM-based spectral-spatial classification of HSI is proposed, where probabilistic modeling with sparse representation and weighted composite features (WCFs) is employed to derive the optimized output weights and extract spatial features. First, ELM is represented as a concave logarithmic-likelihood function under statistical modeling using the maximum a posteriori estimator. Second, sparse representation is applied to the Laplacian prior to efficiently determine a logarithmic posterior with a unique maximum in order to solve the ill-posed problem of ELM. The variable splitting and the augmented Lagrangian are subsequently used to further reduce the computation complexity of the proposed algorithm. Third, the spatial information is extracted using the WCFs to construct the spectral-spatial classification framework. In addition, the lower bound of the proposed method is derived by a rigorous mathematical proof. Experimental results on three publicly available HSI data sets demonstrate that the proposed methodology outperforms ELM and also a number of state-of-the-art approaches.

Hyperspectral imaging (HSI) classification has become a popular research topic in recent years, and effective feature extraction is an important step before the classification task. Traditionally, spectral feature extraction techniques are applied to the HSI data cube directly. This paper presents a novel algorithm for HSI feature extraction by exploiting the curvelet-transformed domain via a relatively new spectral feature processing technique—singular spectrum analysis (SSA). Although the wavelet transform has been widely applied for HSI data analysis, the curvelet transform is employed in this paper since it is able to separate image geometric details and background noise effectively. Using the support vector machine classifier, experimental results have shown that features extracted by SSA on curvelet coefficients have better performance in terms of classification accuracy over features extracted on wavelet coefficients. Since the proposed approach mainly relies on SSA for feature extraction on the spectral dimension, it actually belongs to the spectral feature extraction category. Therefore, the proposed method has also been compared with some state-of-the-art spectral feature extraction techniques to show its efficacy. In addition, it has been proven that the proposed method is able to remove the undesirable artifacts introduced during the data acquisition process. By adding an extra spatial postprocessing step to the classified map achieved using the proposed approach, we have shown that the classification performance is comparable with several recent spectral–spatial classification methods.

A novel stroke based sparse hybrid convolutional neural networks (CNNs) method is proposed for author classification of Chinese ink-wash paintings (IWPs). As Chinese IWPs usually have many authors in several art styles, this differs from real images or western paintings and has led to a big challenge. In our work, we classify Chinese IWPs of different artists by analyzing a set of automatically extracted brushstrokes. A sparse hybrid CNNs in a deep-learning framework is then proposed to extract brushstroke features to replace the commonly used handcrafted ones such as edge, color, intensity and texture. Using 120 IWPs from six famous artists, promising results have been shown in successfully classifying authors in comparison to two other state-of-the-art approaches.

Classification of micro calcification clusters is very essential for early detection of breast cancer from mammograms. In this paper, an improved support vector machine (SVM) scheme is proposed, where optimized decision making is introduced for effective and more accurate data classification. Experimental results on the well-known DDSM database have shown that the proposed method can significantly increase the performance in terms of F1 and Az measurements for the successful classification of clustered micro calcifications.