OCCO: LVM-guided Infrared and Visible Image Fusion Framework based on Object-aware and Contextual COntrastive Learning

Image fusion is a crucial technique in the field of computer vision, and its goal is to generate high-quality fused images and improve the performance of downstream tasks. However, existing fusion methods struggle to balance these two factors. Achieving high quality in fused images may result in lower performance in downstream visual tasks, and vice versa. To address this drawback, a novel LVM (large vision model)-guided fusion framework with Object-aware and Contextual COntrastive learning is proposed, termed as OCCO. The pre-trained LVM is utilized to provide semantic guidance, allowing the network to focus solely on fusion tasks while emphasizing learning salient semantic features in form of contrastive learning. Additionally, a novel feature interaction fusion network is also designed to resolve information conflicts in fusion images caused by modality differences. By learning the distinction between positive samples and negative samples in the latent feature space (contextual space), the integrity of target information in fused image is improved, thereby benefiting downstream performance. Finally, compared with eight state-of-the-art methods on four datasets, the effectiveness of the proposed method is validated, and exceptional performance is also demonstrated on downstream visual task.

Learning a Unified Degradation-aware Representation Model for Multi-modal Image Fusion

All-in-One Degradation-Aware Fusion Models (ADFMs), a class of multi-modal image fusion models, address complex scenes by mitigating degradations from source images and generating high-quality fused images. Mainstream ADFMs often rely on highly synthetic multi-modal multi-quality images for supervision, limiting their effectiveness in cross-modal and rare degradation scenarios. The inherent relationship among these multi-modal, multi-quality images of the same scene provides explicit supervision for training, but also raises above problems. To address these limitations, we present LURE, a Learning-driven Unified Representation model for infrared and visible Image Fusion, which is degradation-aware. LURE decouples multi-modal multi-quality data at the data level and recouples this relationship in a unified latent feature space (ULFS) by proposing a novel unified loss. This decoupling circumvents data-level limitations of prior models and allows leveraging real-world restoration datasets for training high-quality degradation-aware models, sidestepping above issues. To enhance text-image interaction, we refine image-text interaction and residual structures via Text-Guided Attention (TGA) and an inner residual structure. These enhances text's spatial perception of images and preserve more visual details. Experiments show our method outperforms state-of-the-art (SOTA) methods across general fusion, degradation-aware fusion, and downstream tasks. The code will be publicly available.

Learning Structure-Supporting Dependencies via Keypoint Interactive Transformer for General Mammal Pose Estimation

General mammal pose estimation is an important and challenging task in computer vision, which is essential for understanding mammal behaviour in real-world applications. However, existing studies are at their preliminary research stage, which focus on addressing the problem for only a few specific mammal species. In principle, from specific to general mammal pose estimation, the biggest issue is how to address the huge appearance and pose variances for different species. We argue that given appearance context, instance-level prior and the structural relation among keypoints can serve as complementary evidence. To this end, we propose a Keypoint Interactive Transformer (KIT) to learn instance-level structure-supporting dependencies for general mammal pose estimation. Specifically, our KITPose consists of two coupled components. The first component is to extract keypoint features and generate body part prompts. The features are supervised by a dedicated generalised heatmap regression loss (GHRL). Instead of introducing external visual/text prompts, we devise keypoints clustering to generate body part biases, aligning them with image context to generate corresponding instance-level prompts. Second, we propose a novel interactive transformer that takes feature slices as input tokens without performing spatial splitting. In addition, to enhance the capability of the KIT model, we design an adaptive weight strategy to address the imbalance issue among different keypoints.

UASTrack: A Unified Adaptive Selection Framework with Modality-Customization in Single Object Tracking

Multi-modal tracking is essential in single-object tracking (SOT), as different sensor types contribute unique capabilities to overcome challenges caused by variations in object appearance. However, existing unified RGB-X trackers (X represents depth, event, or thermal modality) either rely on the task-specific training strategy for individual RGB-X image pairs or fail to address the critical importance of modality-adaptive perception in real-world applications. In this work, we propose UASTrack, a unified adaptive selection framework that facilitates both model and parameter unification, as well as adaptive modality discrimination across various multi-modal tracking tasks. To achieve modality-adaptive perception in joint RGB-X pairs, we design a Discriminative Auto-Selector (DAS) capable of identifying modality labels, thereby distinguishing the data distributions of auxiliary modalities. Furthermore, we propose a Task-Customized Optimization Adapter (TCOA) tailored to various modalities in the latent space. This strategy effectively filters noise redundancy and mitigates background interference based on the specific characteristics of each modality. Extensive comparisons conducted on five benchmarks including LasHeR, GTOT, RGBT234, VisEvent, and DepthTrack, covering RGB-T, RGB-E, and RGB-D tracking scenarios, demonstrate our innovative approach achieves comparative performance by introducing only additional training parameters of 1.87M and flops of 1.95G. The code will be available at https://github.com/wanghe/UASTrack.

Research on Cervical Cancer p16/Ki-67 Immunohistochemical Dual-Staining Image Recognition Algorithm Based on YOLO

The p16/Ki-67 dual staining method is a new approach for cervical cancer screening with high sensitivity and specificity. However, there are issues of mis-detection and inaccurate recognition when the YOLOv5s algorithm is directly applied to dual-stained cell images. This paper Proposes a novel cervical cancer dual-stained image recognition (DSIR-YOLO) model based on an YOLOv5. By fusing the Swin-Transformer module, GAM attention mechanism, multi-scale feature fusion, and EIoU loss function, the detection performance is significantly improved, with mAP@0.5 and mAP@0.5:0.95 reaching 92.6% and 70.5%, respectively. Compared with YOLOv5s in five-fold cross-validation, the accuracy, recall, mAP@0.5, and mAP@0.5:0.95 of the improved algorithm are increased by 2.3%, 4.1%, 4.3%, and 8.0%, respectively, with smaller variances and higher stability. Compared with other detection algorithms, DSIR-YOLO in this paper sacrifices some performance requirements to improve the network recognition effect. In addition, the influence of dataset quality on the detection results is studied. By controlling the sealing property of pixels, scale difference, unlabelled cells, and diagonal annotation, the model detection accuracy, recall, mAP@0.5, and mAP@0.5:0.95 are improved by 13.3%, 15.3%, 18.3%, and 30.5%, respectively.

SPDFusion: An Infrared and Visible Image Fusion Network Based on a Non-Euclidean Representation of Riemannian Manifolds

Euclidean representation learning methods have achieved commendable results in image fusion tasks, which can be attributed to their clear advantages in handling with linear space. However, data collected from a realistic scene usually have a non-Euclidean structure, where Euclidean metric might be limited in representing the true data relationships, degrading fusion performance. To address this issue, a novel SPD (symmetric positive definite) manifold learning framework is proposed for multi-modal image fusion, named SPDFusion, which extends the image fusion approach from the Euclidean space to the SPD manifolds. Specifically, we encode images according to the Riemannian geometry to exploit their intrinsic statistical correlations, thereby aligning with human visual perception. Actually, the SPD matrix underpins our network learning, with a cross-modal fusion strategy employed to harness modality-specific dependencies and augment complementary information. Subsequently, an attention module is designed to process the learned weight matrix, facilitating the weighting of spatial global correlation semantics via SPD matrix multiplication. Based on this, we design an end-to-end fusion network based on cross-modal manifold learning. Extensive experiments on public datasets demonstrate that our framework exhibits superior performance compared to the current state-of-the-art methods.

MMDRFuse: Distilled Mini-Model with Dynamic Refresh for Multi-Modality Image Fusion

In recent years, Multi-Modality Image Fusion (MMIF) has been applied to many fields, which has attracted many scholars to endeavour to improve the fusion performance. However, the prevailing focus has predominantly been on the architecture design, rather than the training strategies. As a low-level vision task, image fusion is supposed to quickly deliver output images for observation and supporting downstream tasks. Thus, superfluous computational and storage overheads should be avoided. In this work, a lightweight Distilled Mini-Model with a Dynamic Refresh strategy (MMDRFuse) is proposed to achieve this objective. To pursue model parsimony, an extremely small convolutional network with a total of 113 trainable parameters (0.44 KB) is obtained by three carefully designed supervisions. First, digestible distillation is constructed by emphasising external spatial feature consistency, delivering soft supervision with balanced details and saliency for the target network. Second, we develop a comprehensive loss to balance the pixel, gradient, and perception clues from the source images. Third, an innovative dynamic refresh training strategy is used to collaborate history parameters and current supervision during training, together with an adaptive adjust function to optimise the fusion network. Extensive experiments on several public datasets demonstrate that our method exhibits promising advantages in terms of model efficiency and complexity, with superior performance in multiple image fusion tasks and downstream pedestrian detection application. The code of this work is publicly available at https://github.com/yanglinDeng/MMDRFuse.

Understanding Matrix Function Normalizations in Covariance Pooling through the Lens of Riemannian Geometry

Global Covariance Pooling (GCP) has been demonstrated to improve the performance of Deep Neural Networks (DNNs) by exploiting second-order statistics of high-level representations. GCP typically performs classification of the covariance matrices by applying matrix function normalization, such as matrix logarithm or power, followed by a Euclidean classifier. However, covariance matrices inherently lie in a Riemannian manifold, known as the Symmetric Positive Definite (SPD) manifold. The current literature does not provide a satisfactory explanation of why Euclidean classifiers can be applied directly to Riemannian features after the normalization of the matrix power. To mitigate this gap, this paper provides a comprehensive and unified understanding of the matrix logarithm and power from a Riemannian geometry perspective. The underlying mechanism of matrix functions in GCP is interpreted from two perspectives: one based on tangent classifiers (Euclidean classifiers on the tangent space) and the other based on Riemannian classifiers. Via theoretical analysis and empirical validation through extensive experiments on fine-grained and large-scale visual classification datasets, we conclude that the working mechanism of the matrix functions should be attributed to the Riemannian classifiers they implicitly respect.

C2C: Component-to-Composition Learning for Zero-Shot Compositional Action Recognition

Compositional actions consist of dynamic (verbs) and static (objects) concepts. Humans can easily recognize unseen compositions using the learned concepts. For machines, solving such a problem requires a model to recognize unseen actions composed of previously observed verbs and objects, thus requiring, so-called, compositional generalization ability. To facilitate this research, we propose a novel Zero-Shot Compositional Action Recognition (ZS-CAR) task. For evaluating the task, we construct a new benchmark, Something-composition (Sth-com), based on the widely used Something-Something V2 dataset. We also propose a novel Component-to-Composition (C2C) learning method to solve the new ZS-CAR task. C2C includes an independent component learning module and a composition inference module. Last, we devise an enhanced training strategy to address the challenges of component variation between seen and unseen compositions and to handle the subtle balance between learning seen and unseen actions. The experimental results demonstrate that the proposed framework significantly surpasses the existing compositional generalization methods and sets a new state-of-the-art. The new Sth-com benchmark and code are available at https://github.com/RongchangLi/ZSCAR_C2C.

Product Geometries on Cholesky Manifolds with Applications to SPD Manifolds

This paper presents two new metrics on the Symmetric Positive Definite (SPD) manifold via the Cholesky manifold, i.e., the space of lower triangular matrices with positive diagonal elements. We first unveil that the existing popular Riemannian metric on the Cholesky manifold can be generally characterized as the product metric of a Euclidean metric and a Riemannian metric on the space of n-dimensional positive vectors. Based on this analysis, we propose two novel metrics on the Cholesky manifolds, i.e., Diagonal Power Euclidean Metric and Diagonal Generalized Bures-Wasserstein Metric, which are numerically stabler than the existing Cholesky metric. We also discuss the gyro structures and deformed metrics associated with our metrics. The gyro structures connect the linear and geometric properties, while the deformed metrics interpolate between our proposed metrics and the existing metric. Further, by Cholesky decomposition, the proposed deformed metrics and gyro structures are pulled back to SPD manifolds. Compared with existing Riemannian metrics on SPD manifolds, our metrics are easy to use, computationally efficient, and numerically stable.