Fraesormer: Learning Adaptive Sparse Transformer for Efficient Food Recognition

In recent years, Transformer has witnessed significant progress in food recognition. However, most existing approaches still face two critical challenges in lightweight food recognition: (1) the quadratic complexity and redundant feature representation from interactions with irrelevant tokens; (2) static feature recognition and single-scale representation, which overlook the unstructured, non-fixed nature of food images and the need for multi-scale features. To address these, we propose an adaptive and efficient sparse Transformer architecture (Fraesormer) with two core designs: Adaptive Top-k Sparse Partial Attention (ATK-SPA) and Hierarchical Scale-Sensitive Feature Gating Network (HSSFGN). ATK-SPA uses a learnable Gated Dynamic Top-K Operator (GDTKO) to retain critical attention scores, filtering low query-key matches that hinder feature aggregation. It also introduces a partial channel mechanism to reduce redundancy and promote expert information flow, enabling local-global collaborative modeling. HSSFGN employs gating mechanism to achieve multi-scale feature representation, enhancing contextual semantic information. Extensive experiments show that Fraesormer outperforms state-of-the-art methods. code is available at https://zs1314.github.io/Fraesormer.

Learning Dual-Domain Multi-Scale Representations for Single Image Deraining

Existing image deraining methods typically rely on single-input, single-output, and single-scale architectures, which overlook the joint multi-scale information between external and internal features. Furthermore, single-domain representations are often too restrictive, limiting their ability to handle the complexities of real-world rain scenarios. To address these challenges, we propose a novel Dual-Domain Multi-Scale Representation Network (DMSR). The key idea is to exploit joint multi-scale representations from both external and internal domains in parallel while leveraging the strengths of both spatial and frequency domains to capture more comprehensive properties. Specifically, our method consists of two main components: the Multi-Scale Progressive Spatial Refinement Module (MPSRM) and the Frequency Domain Scale Mixer (FDSM). The MPSRM enables the interaction and coupling of multi-scale expert information within the internal domain using a hierarchical modulation and fusion strategy. The FDSM extracts multi-scale local information in the spatial domain, while also modeling global dependencies in the frequency domain. Extensive experiments show that our model achieves state-of-the-art performance across six benchmark datasets.

Dual-domain Modulation Network for Lightweight Image Super-Resolution

Lightweight image super-resolution (SR) aims to reconstruct high-resolution images from low-resolution images with limited computational costs. We find existing frequency-based SR methods cannot balance the reconstruction of overall structures and high-frequency parts. Meanwhile, these methods are inefficient for handling frequency features and unsuitable for lightweight SR. In this paper, we show introducing both wavelet and Fourier information allows our model to consider both high-frequency features and overall SR structure reconstruction while reducing costs. Specifically, we propose a dual-domain modulation network that utilize wavelet-domain modulation self-Transformer (WMT) plus Fourier supervision to modulate frequency features in addition to spatial domain modulation. Compared to existing frequency-based SR modules, our WMT is more suitable for frequency learning in lightweight SR. Experimental results show that our method achieves a comparable PSNR of SRFormer and MambaIR while with less than 50% and 60% of their FLOPs and achieving inference speeds 15.4x and 5.4x faster, respectively, demonstrating the effectiveness of our method on SR quality and lightweight. Codes will be released upon acceptance.

SCASeg: Strip Cross-Attention for Efficient Semantic Segmentation

The Vision Transformer (ViT) has achieved notable success in computer vision, with its variants extensively validated across various downstream tasks, including semantic segmentation. However, designed as general-purpose visual encoders, ViT backbones often overlook the specific needs of task decoders, revealing opportunities to design decoders tailored to efficient semantic segmentation. This paper proposes Strip Cross-Attention (SCASeg), an innovative decoder head explicitly designed for semantic segmentation. Instead of relying on the simple conventional skip connections, we employ lateral connections between the encoder and decoder stages, using encoder features as Queries for the cross-attention modules. Additionally, we introduce a Cross-Layer Block that blends hierarchical feature maps from different encoder and decoder stages to create a unified representation for Keys and Values. To further boost computational efficiency, SCASeg compresses queries and keys into strip-like patterns to optimize memory usage and inference speed over the traditional vanilla cross-attention. Moreover, the Cross-Layer Block incorporates the local perceptual strengths of convolution, enabling SCASeg to capture both global and local context dependencies across multiple layers. This approach facilitates effective feature interaction at different scales, improving the overall performance. Experiments show that the adaptable decoder of SCASeg produces competitive performance across different setups, surpassing leading segmentation architectures on all benchmark datasets, including ADE20K, Cityscapes, COCO-Stuff 164k, and Pascal VOC2012, even under varying computational limitations.

Efficient Semantic Segmentation via Lightweight Multiple-Information Interaction Network

Recently, the integration of the local modeling capabilities of Convolutional Neural Networks (CNNs) with the global dependency strengths of Transformers has created a sensation in the semantic segmentation community. However, substantial computational workloads and high hardware memory demands remain major obstacles to their further application in real-time scenarios. In this work, we propose a lightweight multiple-information interaction network for real-time semantic segmentation, called LMIINet, which effectively combines CNNs and Transformers while reducing redundant computations and memory footprint. It features Lightweight Feature Interaction Bottleneck (LFIB) modules comprising efficient convolutions that enhance context integration. Additionally, improvements are made to the Flatten Transformer by enhancing local and global feature interaction to capture detailed semantic information. The incorporation of a combination coefficient learning scheme in both LFIB and Transformer blocks facilitates improved feature interaction. Extensive experiments demonstrate that LMIINet excels in balancing accuracy and efficiency. With only 0.72M parameters and 11.74G FLOPs, LMIINet achieves 72.0% mIoU at 100 FPS on the Cityscapes test set and 69.94% mIoU at 160 FPS on the CamVid test dataset using a single RTX2080Ti GPU.

HazyDet: Open-source Benchmark for Drone-view Object Detection with Depth-cues in Hazy Scenes

Drone-based object detection in adverse weather conditions is crucial for enhancing drones' environmental perception, yet it remains largely unexplored due to the lack of relevant benchmarks. To bridge this gap, we introduce HazyDet, a large-scale dataset tailored for drone-based object detection in hazy scenes. It encompasses 383,000 real-world instances, collected from both naturally hazy environments and normal scenes with synthetically imposed haze effects to simulate adverse weather conditions. By observing the significant variations in object scale and clarity under different depth and haze conditions, we designed a Depth Conditioned Detector (DeCoDet) to incorporate this prior knowledge. DeCoDet features a Multi-scale Depth-aware Detection Head that seamlessly integrates depth perception, with the resulting depth cues harnessed by a dynamic Depth Condition Kernel module. Furthermore, we propose a Scale Invariant Refurbishment Loss to facilitate the learning of robust depth cues from pseudo-labels. Extensive evaluations on the HazyDet dataset demonstrate the flexibility and effectiveness of our method, yielding significant performance improvements. Our dataset and toolkit are available at https://github.com/GrokCV/HazyDet.

ReviveDiff: A Universal Diffusion Model for Restoring Images in Adverse Weather Conditions

Images captured in challenging environments--such as nighttime, foggy, rainy weather, and underwater--often suffer from significant degradation, resulting in a substantial loss of visual quality. Effective restoration of these degraded images is critical for the subsequent vision tasks. While many existing approaches have successfully incorporated specific priors for individual tasks, these tailored solutions limit their applicability to other degradations. In this work, we propose a universal network architecture, dubbed "ReviveDiff", which can address a wide range of degradations and bring images back to life by enhancing and restoring their quality. Our approach is inspired by the observation that, unlike degradation caused by movement or electronic issues, quality degradation under adverse conditions primarily stems from natural media (such as fog, water, and low luminance), which generally preserves the original structures of objects. To restore the quality of such images, we leveraged the latest advancements in diffusion models and developed ReviveDiff to restore image quality from both macro and micro levels across some key factors determining image quality, such as sharpness, distortion, noise level, dynamic range, and color accuracy. We rigorously evaluated ReviveDiff on seven benchmark datasets covering five types of degrading conditions: Rainy, Underwater, Low-light, Smoke, and Nighttime Hazy. Our experimental results demonstrate that ReviveDiff outperforms the state-of-the-art methods both quantitatively and visually.

Microscopic-Mamba: Revealing the Secrets of Microscopic Images with Just 4M Parameters

In the field of medical microscopic image classification (MIC), CNN-based and Transformer-based models have been extensively studied. However, CNNs struggle with modeling long-range dependencies, limiting their ability to fully utilize semantic information in images. Conversely, Transformers are hampered by the complexity of quadratic computations. To address these challenges, we propose a model based on the Mamba architecture: Microscopic-Mamba. Specifically, we designed the Partially Selected Feed-Forward Network (PSFFN) to replace the last linear layer of the Visual State Space Module (VSSM), enhancing Mamba's local feature extraction capabilities. Additionally, we introduced the Modulation Interaction Feature Aggregation (MIFA) module to effectively modulate and dynamically aggregate global and local features. We also incorporated a parallel VSSM mechanism to improve inter-channel information interaction while reducing the number of parameters. Extensive experiments have demonstrated that our method achieves state-of-the-art performance on five public datasets. Code is available at https://github.com/zs1314/Microscopic-Mamba

OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation

Optical Coherence Tomography Angiography (OCTA) is a crucial imaging technique for visualizing retinal vasculature and diagnosing eye diseases such as diabetic retinopathy and glaucoma. However, precise segmentation of OCTA vasculature remains challenging due to the multi-scale vessel structures and noise from poor image quality and eye lesions. In this study, we proposed OCTAMamba, a novel U-shaped network based on the Mamba architecture, designed to segment vasculature in OCTA accurately. OCTAMamba integrates a Quad Stream Efficient Mining Embedding Module for local feature extraction, a Multi-Scale Dilated Asymmetric Convolution Module to capture multi-scale vasculature, and a Focused Feature Recalibration Module to filter noise and highlight target areas. Our method achieves efficient global modeling and local feature extraction while maintaining linear complexity, making it suitable for low-computation medical applications. Extensive experiments on the OCTA 3M, OCTA 6M, and ROSSA datasets demonstrated that OCTAMamba outperforms state-of-the-art methods, providing a new reference for efficient OCTA segmentation. Code is available at https://github.com/zs1314/OCTAMamba

Attention-Guided Multi-scale Interaction Network for Face Super-Resolution

Recently, CNN and Transformer hybrid networks demonstrated excellent performance in face super-resolution (FSR) tasks. Since numerous features at different scales in hybrid networks, how to fuse these multi-scale features and promote their complementarity is crucial for enhancing FSR. However, existing hybrid network-based FSR methods ignore this, only simply combining the Transformer and CNN. To address this issue, we propose an attention-guided Multi-scale interaction network (AMINet), which contains local and global feature interactions as well as encoder-decoder phases feature interactions. Specifically, we propose a Local and Global Feature Interaction Module (LGFI) to promote fusions of global features and different receptive fields' local features extracted by our Residual Depth Feature Extraction Module (RDFE). Additionally, we propose a Selective Kernel Attention Fusion Module (SKAF) to adaptively select fusions of different features within LGFI and encoder-decoder phases. Our above design allows the free flow of multi-scale features from within modules and between encoder and decoder, which can promote the complementarity of different scale features to enhance FSR. Comprehensive experiments confirm that our method consistently performs well with less computational consumption and faster inference.