Recently, view-based approaches, which recognize a 3D object through its projected 2-D images, have been extensively studied and have achieved considerable success in 3D object recognition. Nevertheless, most of them use a pooling operation to aggregate viewwise features, which usually leads to the visual information loss. To tackle this problem, we propose a novel layer called capsule attention layer (CAL) by using attention mechanism to fuse the features expressed by capsules. In detail, instead of dynamic routing algorithm, we use an attention module to transmit information from the lower level capsules to higher level capsules, which obviously improves the speed of capsule networks. In particular, the view pooling layer of multiview convolutional neural network (MVCNN) becomes a special case of our CAL when the trainable weights are chosen on some certain values. Furthermore, based on CAL, we propose a capsule attention convolutional neural network (CACNN) for 3D object recognition. Extensive experimental results on three benchmark datasets demonstrate the efficiency of our CACNN and show that it outperforms many state-of-the-art methods.

Lane detection is a critical and challenging task in autonomous driving, particularly in real-world scenarios where traffic lanes can be slender, lengthy, and often obscured by other vehicles, complicating detection efforts. Existing anchor-based methods typically rely on prior lane anchors to extract features and subsequently refine the location and shape of lanes. While these methods achieve high performance, manually setting prior anchors is cumbersome, and ensuring sufficient coverage across diverse datasets often requires a large amount of dense anchors. Furthermore, the use of Non-Maximum Suppression (NMS) to eliminate redundant predictions complicates real-world deployment and may under-perform in complex scenarios. In this paper, we propose Polar R-CNN, an end-to-end anchor-based method for lane detection. By incorporating both local and global polar coordinate systems, Polar R-CNN facilitates flexible anchor proposals and significantly reduces the number of anchors required without compromising performance. Additionally, we introduce a triplet head with heuristic structure that supports NMS-free paradigm, enhancing deployment efficiency and performance in scenarios with dense lanes. Our method achieves competitive results on five popular lane detection benchmarks—Tusimple, CULane, LLAMAS, CurveLanes, and DL-Rail—while maintaining a lightweight design and straightforward structure. Our source code is available at https://github.com/ShqWW/PolarRCNN

In the field of remote sensing (RS) imaging, the occurrence of adverse meteorological conditions or sensor malfunctions can lead to missing data, posing a substantial impediment. Low-rank tensor decomposition has emerged as a promising strategy for resolving this issue, as it enables the integration of diverse data priors within a unified framework. Although various decomposition techniques, such as Tucker decomposition and tensor ring decomposition, have been developed based on multi-linear products, they may not adequately capture the complex structure of RS imagery. Therefore, there is a need for tensor decompositions that incorporate nonlinear operations. To alleviate this challenge, a multi-nonlinear product is defined, which enables the construction of a nonlinear Tucker decomposition model. To enhance the model’s capability, a stacked Tucker decomposition model is formulated, by representing a tensor as the product of a core tensor and a collection of factor matrices along each mode, utilizing the multi-nonlinear product, which potentially regulates the distribution of singular values, thereby achieving a more accurate characterization of textures. The proposed model, integrated with total variation regularization, is subsequently applied to the task of RS imagery inpainting. Extensive experimental results demonstrate the superiority of the proposed model over state-of-the-art methods across various tasks. This validates its effectiveness and adaptability in mitigating the challenges associated with RS imagery inpainting. The code is available at https://github.com/shuangxu96/STDTV.

Based on deep learning, various pan-sharpening models have achieved excellent results. However, most of them adopt simple addition or concatenation operations to merge the information of low spatial resolution multi-spectral (LRMS) images and panchromatic (PAN) images, which may cause a loss of detailed information. To tackle this issue, inspired by capsule networks, we propose a plug-and-play layer named modified dynamic routing layer (MDRL), which modifies the information transmission mode of capsules to effectively fuse LRMS images and PAN images. Concretely, the lower-level capsules are generated by applying transform operation to the features of LRMS images and PAN images, which preserve the spatial location information. Then, the dynamic routing algorithm is modified to adaptively select the lower-level capsules to generate the higher-level capsule features to represent the fusion of LRMS images and PAN images, which can effectively avoid the loss of detailed information. In addition, the previous addition and concatenation operations are illustrated as special cases of our MDRL. Based on MIPSM with addition operations and DRPNN with concatenation operations, two modified dynamic routing models named MDR–MIPSM and MDR–DRPNN are further proposed for pan-sharpening. Extensive experimental results demonstrate that the proposed method can achieve remarkable spectral and spatial quality.

Pansharpening, a critical task in remote sensing, aims to fuse panchromatic (PAN) images with low-resolution multispectral (LRMS) images to generate high-resolution multispectral (HRMS) outputs. Existing deep learning methods primarily focus on pixel-level spatial and spectral features, often overlooking the deeper, text-level semantic information that can enhance fusion. To address this limitation, we introduce a novel Text-modulated Multiscale Guidance Transformer (TMGformer). Specifically, we first establish a self-contained workflow to generate text features, eliminating reliance on external Application Programming Interfaces (APIs). Subsequently, we develop a Text-modulated Visual Feature Refinement (TVFR) module that transforms these text features into spectrally compatible and semantically consistent guidance. This guidance is then injected into our Asymmetric Multiscale Fusion (AMF) module to guide the fusion process at multiple scales, effectively addressing the vast variance in the scale of terrestrial objects described in their corresponding texts. Furthermore, we introduce a hybrid text-visual loss function to provide comprehensive supervision. Extensive experiments demonstrate that TMGformer achieves state-of-the-art performance. Notably, on the GaoFen-2 dataset, our method reduces the ERGAS by 5.11% and the D_s by 16.44% compared to the second-best competitor. To facilitate future research, we release a new vision-language dataset containing textual descriptions and corresponding text features. The code and dataset are available at: https://github.com/PeterZhaoXJTU/TMGformer.