State space models (SSM) with selection mechanisms and hardware-aware architectures, namely Mamba, have recently shown significant potential in long-sequence modeling. Since the complexity of transformers’ self-attention mechanism is quadratic with image size, as well as increasing computational demands, researchers are currently exploring how to adapt Mamba for computer vision tasks. This paper is the first comprehensive survey that aims to provide an in-depth analysis of Mamba models within the domain of computer vision. It begins by exploring the foundational concepts contributing to Mamba’s success, including the SSM framework, selection mechanisms, and hardware-aware design. Then, we review these vision Mamba models by categorizing them into foundational models and those enhanced with techniques including convolution, recurrence, and attention to improve their sophistication. Furthermore, we investigate the widespread applications of Mamba in vision tasks, which include their use as a backbone in various levels of vision processing. This encompasses general visual tasks, medical visual tasks (e.g., 2D/3D segmentation, classification, image registration, etc.), and remote sensing visual tasks. In particular, we introduce general visual tasks from two levels: high/mid-level vision (e.g., object detection, segmentation, video classification, etc.) and low-level vision (e.g., image super-resolution, image restoration, visual generation, etc.). We hope this endeavor will spark additional interest within the community to address current challenges and further apply Mamba models in computer vision.

Recently, infrared small target detection (ISTD) has made significant progress, thanks to the development of basic models. Specifically, the models combining CNNs with transformers can successfully extract both local and global features. However, the disadvantage of the transformer is also inherited, i.e., the quadratic computational complexity to sequence length. Inspired by the recent basic model with linear complexity for long-distance modeling, Mamba, we explore the potential of this state space model for ISTD task in terms of effectiveness and efficiency in the paper. However, directly applying Mamba achieves suboptimal performances due to the insufficient harnessing of local features, which are imperative for detecting small targets. Instead, we tailor a nested structure, Mamba-in-Mamba (MiM-ISTD), for efficient ISTD. It consists of Outer and Inner Mamba blocks to adeptly capture both global and local features. Specifically, we treat the local patches as "visual sentences" and use the Outer Mamba to explore the global information. We then decompose each visual sentence into sub-patches as "visual words" and use the Inner Mamba to further explore the local information among words in the visual sentence with negligible computational costs. By aggregating the visual word and visual sentence features, our MiM-ISTD can effectively explore both global and local information. Experiments on NUAA-SIRST and IRSTD-1k show the superior accuracy and efficiency of our method. Specifically, MiM-ISTD is 8× faster than the SOTA method and reduces GPU memory usage by 62.2% when testing on 2048 × 2048 images, overcoming the computation and memory constraints on high-resolution infrared images.

Infrared small target detection (ISTD) is critical to national security and has been extensively applied in military areas. ISTD aims to segment small target pixels from background. Most ISTD networks focus on designing feature extraction blocks or feature fusion modules, but rarely describe the ISTD process from the feature map evolution perspective. In the ISTD process, the network attention gradually shifts towards target areas. We abstract this process as the directional movement of feature map pixels to target areas through convolution, pooling and interactions with surrounding pixels, which can be analogous to the movement of thermal particles constrained by surrounding variables and particles. In light of this analogy, we propose Thermal Conduction-Inspired Transformer (TCI-Former) based on the theoretical principles of thermal conduction. According to thermal conduction differential equation in heat dynamics, we derive the pixel movement differential equation (PMDE) in the image domain and further develop two modules: Thermal Conduction-Inspired Attention (TCIA) and Thermal Conduction Boundary Module (TCBM). TCIA incorporates finite difference method with PMDE to reach a numerical approximation so that target body features can be extracted. To further remove errors in boundary areas, TCBM is designed and supervised by boundary masks to refine target body features with fine boundary details. Experiments on IRSTD-1k and NUAA-SIRST demonstrate the superiority of our method.

Medical image segmentation has made significant strides with the development of basic models. Specifically, models that combine CNNs with transformers can successfully extract both local and global features. However, these models inherit the transformer’s quadratic computational complexity, limiting their efficiency. Inspired by the recent Receptance Weighted Key Value (RWKV) model, which achieves linear complexity for long-distance modeling, we explore its potential for medical image segmentation. While directly applying vision-RWKV yields suboptimal results due to insufficient local feature exploration and disrupted spatial continuity, we propose a novel nested structure, Zigzag RWKV-in-RWKV (Zig-RiR), to address these issues. It consists of Outer and Inner RWKV blocks to adeptly capture both global and local features without disrupting spatial continuity. We treat local patches as ”visual sentences” and use the Outer Zig-RWKV to explore global information. Then, we decompose each sentence into subpatches (”visual words”) and use the Inner Zig-RWKV to further explore local information among words, at negligible computational cost. We also introduce a Zigzag-WKV attention mechanism to ensure spatial continuity during token scanning. By aggregating visual word and sentence features, our Zig-RiR can effectively explore both global and local information while preserving spatial continuity. Experiments on four medical image segmentation datasets of both 2D and 3D modalities demonstrate the superior accuracy and efficiency of our method, outperforming the state-of-the-art method 14.4 times in speed and reducing GPU memory usage by 89.5% when testing on 1024 × 1024 high-resolution medical images. Our code is available at https://github.com/txchen-USTC/Zig-RiR.

How to effectively interact audio with vision has garnered considerable interest within the multi-modality research field. Recently, a novel audio-visual video segmentation (AVS) task has been proposed, aiming to segment the sounding objects in video frames under the guidance of audio cues. However, most existing AVS methods are hindered by a modality imbalance where the visual features tend to dominate those of the audio modality, due to a unidirectional and insufficient integration of audio cues. This imbalance skews the feature representation towards the visual aspect, impeding the learning of joint audio-visual representations and potentially causing segmentation inaccuracies. To address this issue, we propose AVSAC. Our approach features a Bidirectional Audio-Visual Decoder (BAVD) with integrated bidirectional bridges, enhancing audio cues and fostering continuous interplay between audio and visual modalities. This bidirectional interaction narrows the modality imbalance, facilitating more effective learning of integrated audio-visual representations. Additionally, we present a strategy for audio-visual frame-wise synchrony as fine-grained guidance of BAVD. This strategy enhances the share of auditory components in visual features, contributing to a more balanced audio-visual representation learning. Extensive experiments show that our method has state-of-the-art performance on several AVS public benchmarks.

Medical image segmentation is of great significance to disease diagnosis and treatment planning. Despite multiple progresses, most present methods (1) pay insufficient attention to suppressing background noise disturbance that impacts segmentation accuracy and (2) are not efficient enough, especially when the images are of large resolutions. To address the two challenges, we turn to a traditional de-noising method and a new efficient network structure and propose BSBP-RWKV for accurate and efficient medical image segmentation. Specifically, we combine the advantages of Perona-Malik Diffusion (PMD) in noise suppression without losing boundary details and RWKV in its efficient structure, and devise the DWT-PMD RWKV Block across one of our encoder branches to preserve boundary details of lesion areas while suppressing background noise disturbance in an efficient structure. Then we feed the de-noised lesion boundary cues to our proposed Multi-Step Runge-Kutta convolutional Block to supplement the cues with more local details. We also propose a novel loss function for shape refinement that can align the shape of predicted lesion areas with GT masks in both spatial and frequency domains. Experiments on ISIC 2016 and Kvasir-SEG show the superior accuracy and efficiency of our BSBP-RWKV. Specifically, BSBP-RWKV reduces complexity of 5.8 times compared with the SOTA while also cutting down GPU memory usage by over 62.7% for each 1024 \times 1024 image during inference.