Infrared small target detection is challenging due to limited target size and low signal-to-noise ratio. Unlike common targets, infrared small targets contain a higher proportion of edge pixels and exhibit blurred boundaries due to diffraction and quantization artifacts, making boundaries uniquely valuable cues for target perception. However, existing methods often emphasize holistic modeling while underutilizing such informative boundary cues. Motivated by this observation, we propose a Dual-Path Edge-Guided Frequency-Aware Network (DEFANet), which enables edge-target collaborative modeling for enhanced feature representation. DEFANet features a dual-path design, consisting of a main branch for holistic target modeling and an edge branch for boundary transition perception. To facilitate interaction and enhance representation in both branches, we introduce two core modules: Frequency-Aware Dual Enhancement Module (FADE) and Edge-Guided Integration Module (EGI). FADE employs a Frequency-Decoupled Attention Enhancement Mechanism to enhance both branches in the frequency domain, strengthening holistic modeling in the main branch and boundary representation in the edge branch. EGI leverages a Dual-Path Group-Wise Guidance Mechanism to integrate enhanced edge features into the main branch, improving boundary perception. Extensive experiments on four public infrared small target datasets, MDvsFA, LAFT, SIRST, and SIATD, demonstrate that DEFANet achieves SOTA performance. Ablation studies further validate the effectiveness of DEFANet and the soundness of its design motivation.

Cross-view geo-localization (CVGL) determines the geographic location of a query image by matching it with the most similar GPS-tagged satellite images. Existing methods have improved cross-view feature consistency through complex network architectures but still face challenges under significant illumination variations. Inspired by cognitive psychology, we observe that humans can selectively ignore color distractions and focus on structural information under certain circumstances. Therefore, we propose an SPNet, leveraging sketches to enhance structural feature consistency and achieve illumination-robust CVGL. SPNet employs Sketch Proxy Training, which introduces both RGB and sketch images during training. By leveraging the structural consistency provided by sketches under color variations, it mitigates the network’s over-reliance on color cues and achieves robustness to illumination changes. To further integrate multi-source information, we design a Recurrent Progressive Channel Attention (RPCA) module that progressively selects and reweights channel features, effectively combining the semantic cues of RGB images with the structural information of sketches. In addition, we introduce a High-Pass Cross-Layer Connection (HPCL) to transmit high-frequency information across feature layers, emphasizing edges and details to reinforce structural modeling and cross-layer feature consistency. Extensive experiments demonstrate that our SPNet achieves superior performance on several CVGL datasets, including University-1652, SUES-200, CVUSA, and CVACT, and ranks first on the public leaderboard of the University-160k-WX dataset released by UAVM 2024, further validating its robustness and generalization capability.

Infrared small-target detection (ISTD) presents significant challenges due to the blurred edges and low signal-to-noise ratio (SNR) of the targets, which often results in the loss of critical target features during convolution and downsampling. While researchers have explored complex architectures to preserve these features, such designs often come at the cost of increased computational cost. Our analysis reveals that the curse of dimensionality (COD) is a fundamental issue in ISTD. Due to the sparse information content of infrared small targets, they struggle to effectively utilize high-dimensional feature spaces, leading to a significant feature loss phenomenon. To address this, we propose the CODNet, which integrates spatial–channel shuffle (SCS) attention, dynamic gated channel attention (DGCA), and DCT-based frequency attention (DFA) in the UNet framework. SCS enhances feature representation through spatial attention and channel shuffling, making the feature space more compact and informative. DGCA performs selective channel compression to improve the feature SNR while reducing redundancy and sparsity. DFA incorporates frequency-domain features to supplement spatial representations, alleviating feature sparsity and improving information utilization. Experimental results demonstrate that the CODNet achieves state-of-the-art performance on four public datasets while maintaining relatively high operation efficiency.

Most current infrared small target detection methods attempt to fuse local and global information by using single-scale inputs and creating a multi-scale feature pyramid during network feeding forwards. Further to this, our research finds that using high-resolution inputs can improve recall, while low-resolution inputs improve precision. Nevertheless, solely focusing on global or local information can result in missing target and false alarm. To address these issues, we propose the BPR-Net to balance precision and recall via a novel multi-scale attention mechanism, which combines semantic and shallow features of multi-scale inputs. We first scale the input image into multiple images with varying resolutions and feed them into the network. In the encoder, Scale Fusion Module (SFM) fuses features from corresponding images of different resolutions. In the decoder, a Channel Fusion Module (CFM) fuses useful information from multiple channels. Furthermore, a Wavelet Transform cross-layer skip Layer (WTL) is employed to enhance the interaction between decoder layers for more effective multi-scale feature fusion. Experimental results demonstrate that our approach achieves a balance between recall and precision and yields state-of-the-art performance on challenging benchmarks including Sirst, MDvsFA, and SIATD. Notably, our approach achieves an F1 score of 0.9409 on the challenging benchmark SIATD, surpassing the state-of-the-art method by 16.7%.

Developing accurate algorithms for agricultural pattern recognition from aerial imagery has become increasingly important due to the prevalence of unmanned aerial vehicles (UAVs). This letter introduces a deep encoder–decoder network for semantic land cover segmentation, where the goal is to classify six anomaly categories from multispectral aerial imagery. Since aerial imagery exhibits specific characteristics and visual challenges in this imaging domain, existing semantic segmentation models are not plug-and-play. Starting from a state-of-the-art segmentation model, we present a step-by-step analysis of key challenges and also reveal our observations in addressing these challenges. In particular, we investigate on how to exploit data prior knowledge, how to deal with sample imbalance, and how to encode global semantic and contextual information to improve segmentation. Experiments on a recent large-scale aerial land cover data set demonstrate that our method achieves compelling performance against other state-of-the-art approaches. Our results and insights can provide references for practitioners working in this field when dealing with similar segmentation problems.

Infrared small target detection plays an important role in target warning, ground monitoring, and flight guidance. Existing methods typically utilize local-contrast information of each pixel to detect infrared small targets, neglecting the interior relation between target pixels or background pixels. The mere use of the local information of one pixel, however, is not sufficient for accurate detection, which may lead to missing detection and false alarms. As a harmonious whole, information between pixels are necessary to determine if a pixel belongs to the target or the background. Motivated by the fact that pixels from targets or backgrounds are correlated with each other, we propose a coarse-to-fine interior attention-aware network (IAANet) for infrared small target detection. Specifically, a region proposal network (RPN) is first applied to obtain coarse target regions and filter out backgrounds. Then, we leverage a transformer encoder to model the attention between pixels in coarse target regions, outputting attention-aware features. Finally, predictions are obtained by feeding attention-aware features to a classification head. Extensive experiments show that our approach is capable of detecting targets precisely, of suppressing a variety of false alarm sources, and works effectively in various background environments and target appearances. We show that our IAANet outperforms the state-of-the-art methods by a large margin. Code will be made available at:https://github.com/kwwcv/iaanet.

We show that explicit modeling of composition rules benefits image cropping. Image cropping is considered a promising way to automate aesthetic composition in professional photography. Existing efforts, however, only model such professional knowledge implicitly, e.g., by ranking from comparative candidates. Inspired by the observation that natural composition traits always follow a specific rule, we propose to learn such rules in a discriminative manner, and more importantly, to incorporate learned composition clues explicitly in the model. To this end, we introduce the concept of the key composition map (KCM) to encode the composition rules. The KCM can reveal the common laws hidden behind different composition rules and can inform the cropping model of what is important in composition. With the KCM, we present a novel cropping-by-composition paradigm and instantiate a network to implement composition-aware image cropping. Extensive experiments on two benchmarks justify that our approach enables effective, interpretable, and fast image cropping.

Existing deep siamese trackers are typically built on off-the-shelf CNN models for feature learning, with the demand for huge power consumption and memory storage. This limits current deep siamese trackers to be carried on resource-constrained devices like mobile phones, given factor that such a deployment normally requires cost-effective considerations. In this work, we address this issue by presenting a novel Distilled Learning Framework(DLF) for siamese tracking, which aims at learning tracking model with efficiency and high accuracy. Specifically, we propose two simple yet effective knowledge distillation strategies, denote as point-wise distillation and pairwise distillation, which are designed for transferring knowledge from a more discriminative teacher tracker into a compact student tracker. In this way, cost-effective and high performance tracking could be achieved. Extensive experiments on several tracking benchmarks demonstrate the effectiveness of our proposed method.