Cascade Fusion and Correlation Enhancement for Knowledge Distillation.

Knowledge distillation (KD) improves the performance of a compact student network by transferring learned knowledge from a cumbersome teacher network. In the existing approaches, the multiscale feature knowledge is transferred via densely connected paths, which increases the optimization difficulty. Moreover, correlations among the labels are neglected despite their capability to enhance the intraclass similarity of samples.

Dual-Structural Bipartite Graph Learning for Multiview Clustering.

Bipartite graph (BiG) has been proven to be efficient in handling massive multiview data for clustering. However, how to regulate the structural information of view-specific anchors and view-shared BiG is still open and needs to be further studied. Hence, a novel dual-structural BiG learning (DsBiGL) method is proposed in the article.

Toward higher-frequency fringe projection profilometry with fewer patterns.

Phase unwrapping is crucial in fringe projection profilometry (FPP) 3D measurement. However, achieving efficient and robust phase unwrapping remains a challenge, particularly when dealing with high-frequency fringes to achieve high accuracy. Existing methods rely on heavy fringe projections, inevitably sacrificing measurement efficiency.

Efficient Swin Transformer for Remote Sensing Image Super-Resolution.

Remote sensing super-resolution (SR) technique, which aims to generate high-resolution image with rich spatial details from its low-resolution counterpart, play a vital role in many applications. Recently, more and more studies attempt to explore the application of Transformer in remote sensing field. However, they suffer from the high computational burden and memory consumption for remote sensing super-resolution.

Spectral Super-Resolution in Frequency Domain.

Spectral super-resolution aims to reconstruct a hyperspectral image (HSI) from its corresponding RGB image, which has drawn much more attention in remote sensing field. Recent advances in the application of deep learning models for spectral super-resolution have demonstrated great potential. However, these methods only work in spectral-spatial domain while rarely explore the potential property in the frequency domain.

Multistage Spatial-Spectral Fusion Network for Spectral Super-Resolution.

Spectral super-resolution (SSR) aims to restore a hyperspectral image (HSI) from a single RGB image, in which deep learning has shown impressive performance. However, the majority of the existing deep-learning-based SSR methods inadequately address the modeling of spatial-spectral features in HSI. That is to say, they only sufficiently capture either the spatial correlations or the spectral self-similarity, which results in a loss of discriminative spatial-spectral features and hence limits the fidelity of the reconstructed HSI.

Denoiser Learning for Infrared and Visible Image Fusion.

Infrared image (IR) and visible image (VI) fusion creates fusion images that contain richer information and gain improved visual effects. Existing methods generally use the operators of manual design, such as intensity and gradient operators, to mine the image information. However, it is hard for them to achieve a complete and accurate description of information, which limits the image fusion performance.

Unified and Real-Time Image Geo-Localization via Fine-Grained Overlap Estimation.

Image geo-localization aims to locate a query image from source platform (e.g., drones, street vehicle) by matching it with Geo-tagged reference images from the target platforms (e.

Towards Visual-Prompt Temporal Answer Grounding in Instructional Video.

Temporal answer grounding in instructional video (TAGV) is a new task naturally derived from temporal sentence grounding in general video (TSGV). Given an untrimmed instructional video and a text question, this task aims at locating the frame span from the video that can semantically answer the question, i.e.

Changes in the state of matter of KCIO to improve thermal and combustion properties of Al/MoO nanothermite.

To improve the thermal and combustion properties of nanothermites, a design theory of changing the state of matter and structural state of the reactants during reaction was proposed. The Al/MoO/KClO (Kp) nanothermite was prepared and the Al/MoO nanothermite was used as a control. SEM and XRD were used to characterize the nanothermites; DSC was used to test thermal properties; and constant volume and open combustion tests were performed to examine their combustion performance.

AdaptiveClick: Click-Aware Transformer With Adaptive Focal Loss for Interactive Image Segmentation.

Interactive image segmentation (IIS) has emerged as a promising technique for decreasing annotation time. Substantial progress has been made in pre- and post-processing for IIS, but the critical issue of interaction ambiguity, notably hindering segmentation quality, has been under-researched. To address this, we introduce ADAPTIVE CLICK - a click-aware transformer incorporating an adaptive focal loss (AFL) that tackles annotation inconsistencies with tools for mask- and pixel-level ambiguity resolution.

Spectral Super-Resolution via Deep Low-Rank Tensor Representation.

Spectral super-resolution has attracted the attention of more researchers for obtaining hyperspectral images (HSIs) in a simpler and cheaper way. Although many convolutional neural network (CNN)-based approaches have yielded impressive results, most of them ignore the low-rank prior of HSIs resulting in huge computational and storage costs. In addition, the ability of CNN-based methods to capture the correlation of global information is limited by the receptive field.

SOSNet: Real-Time Small Object Segmentation via Hierarchical Decoding and Example Mining.

Real-time semantic segmentation plays an important role in auto vehicles. However, most real-time small object segmentation methods fail to obtain satisfactory performance on small objects, such as cars and sign symbols, since the large objects usually tend to devote more to the segmentation result. To solve this issue, we propose an efficient and effective architecture, termed small objects segmentation network (SOSNet), to improve the segmentation performance of small objects.

Click-Pixel Cognition Fusion Network With Balanced Cut for Interactive Image Segmentation.

Interactive image segmentation (IIS) has been widely used in various fields, such as medicine, industry, etc. However, some core issues, such as pixel imbalance, remain unresolved so far. Different from existing methods based on pre-processing or post-processing, we analyze the cause of pixel imbalance in depth from the two perspectives of pixel number and pixel difficulty.

A Lightweight Pixel-Level Unified Image Fusion Network.

In recent years, deep-learning-based pixel-level unified image fusion methods have received more and more attention due to their practicality and robustness. However, they usually require a complex network to achieve more effective fusion, leading to high computational cost. To achieve more efficient and accurate image fusion, a lightweight pixel-level unified image fusion (L-PUIF) network is proposed.

Pixel-Centric Context Perception Network for Camouflaged Object Detection.

Camouflaged object detection (COD) aims to identify object pixels visually embedded in the background environment. Existing deep learning methods fail to utilize the context information around different pixels adequately and efficiently. In order to solve this problem, a novel pixel-centric context perception network (PCPNet) is proposed, the core of which is to customize the personalized context of each pixel based on the automatic estimation of its surroundings.

SSTF-Unet: Spatial-Spectral Transformer-Based U-Net for High-Resolution Hyperspectral Image Acquisition.

To obtain a high-resolution hyperspectral image (HR-HSI), fusing a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI) is a prominent approach. Numerous approaches based on convolutional neural networks (CNNs) have been presented for hyperspectral image (HSI) and multispectral image (MSI) fusion. Nevertheless, these CNN-based methods may ignore the global relevant features from the input image due to the geometric limitations of convolutional kernels.

Surface defect detection competition with a bio-inspired vision sensor.

This paper reports the background and results of the Surface Defect Detection Competition with Bio-inspired Vision Sensor, as well as summarizes the champion solutions, current challenges and future directions.

Development of a Completely New PFOS Alternative with Lower Surface Tension for Minimizing the Environmental Burden.

Unlabelled: Improving the technical performance of related industrial products is an efficient strategy to reducing the application quantities and environmental burden for toxic chemicals. A novel polyfluoroalkyl surfactant potassium 1,1,2,2,3,3,4,4-octafluoro-4-(perfluorobutoxy)butane-1-sulfonate(F404) was synthesized by a commercializable route. It had a surface tension(γ) of 18.

LRAF-Net: Long-Range Attention Fusion Network for Visible-Infrared Object Detection.

Visible-infrared object detection aims to improve the detector performance by fusing the complementarity of visible and infrared images. However, most existing methods only use local intramodality information to enhance the feature representation while ignoring the efficient latent interaction of long-range dependence between different modalities, which leads to unsatisfactory detection performance under complex scenes. To solve these problems, we propose a feature-enhanced long-range attention fusion network (LRAF-Net), which improves detection performance by fusing the long-range dependence of the enhanced visible and infrared features.

Zero-Shot Hyperspectral Sharpening.

Fusing hyperspectral images (HSIs) with multispectral images (MSIs) of higher spatial resolution has become an effective way to sharpen HSIs. Recently, deep convolutional neural networks (CNNs) have achieved promising fusion performance. However, these methods often suffer from the lack of training data and limited generalization ability.

Feature Consistency-Based Prototype Network for Open-Set Hyperspectral Image Classification.

Hyperspectral image (HSI) classification methods have made great progress in recent years. However, most of these methods are rooted in the closed-set assumption that the class distribution in the training and testing stages is consistent, which cannot handle the unknown class in open-world scenes. In this work, we propose a feature consistency-based prototype network (FCPN) for open-set HSI classification, which is composed of three steps.