Camouflaged Object Detection (COD) is pivotal for segmenting objects that seamlessly blend into their surroundings. While prior endeavors demonstrate impressive performance through training on predefined labels, they heavily rely on labor-intensive data annotation and struggle to adapt to open-world scenarios. In this light, we propose RA-COD, a training-free paradigm that enables COD by retrieving the most similar samples from the prototype repository. The efficacy of RA-COD hinges on 1) capturing the nuanced resemblance between objects and their environments and 2) excelling in dense prediction tasks. To achieve (1), the crux lies in ensuring diversity and discriminability within the prototype repository. In this context, we propose GenPro, an automated pipeline for crafting Generative Prototypes. GenPro integrates a range of foundation models, including the Diffusion Model, Vision-Language Model, Segment Anything Model (SAM), and DINOv2, in a complementary manner that synergistically generates diverse and distinguishable prototype samples. To achieve (2), we propose C2F to retrieve camouflaged objects in a Coarse-to-Fine regime. We commence with pixel-level retrieval in the feature space, which generates a coarse mask that effectively captures class discrimination and object localization. Further refinement is achieved by extracting bounding boxes from this coarse mask to prompt SAM in generating mask proposals for region-level retrieval. Evaluations on four benchmarks showcase that RA-COD achieves state-of-the-art performance compared to existing training-free methods.

Camouflaged Object Detection (COD) seeks to distinguish objects from their highly similar backgrounds. Existing work has essentially focused on isolating camouflaged objects from the environment, demonstrating ever-improving performance but at the cost of extensive annotations and complex optimizations. In this paper, we diverge from this paradigm and shift the lens to isolating the salient environment from the camouflaged object. We introduce EASE, an Environment-Aware unSupErvised COD framework that identifies the environment by referencing an environment prototype library and detects camouflaged objects by inverting the retrieved environmental features. Specifically, our approach (DiffPro) uses large multimodal models, diffusion models, and vision-foundation models to construct the environment prototype library. To retrieve environments from the library and refrain from confusing foreground and background, we incorporate three retrieval schemes: Kernel Density Estimation-based Adaptive Threshold (KDE-AT), Global-to-Local pixel-level retrieval (G2L), and Self-Retrieval (SR). Our experiments demonstrate significant improvements over current unsupervised methods, with EASE achieving an average gain of over 10% on the COD10K dataset. When integrated with SAM, EASE surpasses prompt-based segmentation approaches and performs competitively with state-of-the-art fully-supervised methods. Code is available at https://github.com/xiaohainku/EASE.

Camouflaged Object Detection (COD) aims to segment objects resembling their environment. To address the challenges of extensive annotations and complex optimizations in supervised learning, recent prompt-based segmentation methods excavate insightful prompts from Large Vision-Language Models (LVLMs) and refine them using various foundation models. These are subsequently fed into the Segment Anything Model (SAM) for segmentation. However, due to the hallucinations of LVLMs and insufficient image-prompt interactions during the refinement stage, these prompts often struggle to capture well-established class differentiation and localization of camouflaged objects, resulting in performance degradation. To provide SAM with more informative prompts, we present UpGen, a pipeline that prompts SAM with generative prompts without requiring training, marking a novel integration of generative models with LVLMs. Specifically, we propose the Multi-Student-Single-Teacher (MSST) knowledge integration framework to alleviate hallucinations of LVLMs. This framework integrates insights from multiple sources to enhance the classification of camouflaged objects. To enhance interactions during the prompt refinement stage, we are the first to leverage generative models on real camouflage images to produce SAM-style prompts without fine-tuning. By capitalizing on the unique learning mechanism and structure of generative models, we effectively enable image-prompt interactions and generate highly informative prompts for SAM. Our extensive experiments demonstrate that UpGen outperforms weakly-supervised models and its SAM-based counterparts. We also integrate our framework into existing weakly-supervised methods to generate pseudo-labels, resulting in consistent performance gains. Moreover, with minor adjustments, UpGen shows promising results in open-vocabulary COD, referring COD, salient object detection, marine animal segmentation, and transparent object segmentation.

The widespread deployment of Wireless Sensor Networks (WSN) has made network coverage optimization crucial for improving coverage rates. However, traditional methods struggle with challenges such as energy constraints and environmental uncertainties. Metaheuristic (MH) algorithms offer promising solutions. Dung Beetle Optimization (DBO) algorithm is a well-regarded MH approach, but it suffers from slow convergence and a propensity for local optima entrapment in WSN coverage optimization. To overcome these limitations, this study proposes the Enhanced Dung Beetle Optimization with Neighborhood Mutation (ENMDBO). ENMDBO incorporates three key mechanisms: (1) the Exploring Cosine Similarity Transformation (ECST) strategy, which dynamically adjusts individual similarity to balance global exploration and local exploitation, mitigating the risk of local optima; (2) the Neighborhood Solution Mutation Sharing (NSMS) mechanism, which enhances population diversity by sharing positional information among neighbors, improving search efficiency; and (3) the Tolerance Threshold Detection Mutation (TTDM) mechanism, which detects stagnation in fitness to strengthen the algorithm’s global search capabilities. Experiments on the CEC2017 benchmark suite (Dim = 30, 50, 100) show that ENMDBO achieves superior performance compared to state-of-the-art algorithms, approaching the global optimum. Finally, in WSN coverage optimization, ENMDBO achieves an 86.88% coverage rate, representing an 8.92% improvement over the original DBO, while effectively reducing redundancy. These results underscore ENMDBO’s robustness and effectiveness, establishing it as a practical and reliable solution. (Matlab codes of ENMDBO are available at https://ww2.mathworks.cn/matlabcentral/fileexchange/181820-enhanced-dung-beetle-optimization-with-neighborhood-mutation.

Recently, the prediction of drug-target interactions (DTIs) is improved by graph neural networks (GNNs). There exists some DTIs that are far away from other DTIs, which plays fundamental role in drug development. However, conventional GNNs encode long-range topological correlations with latent embeddings, restricting their performances in inferring distant DTIs. Inspired by random walk methods that directly encode the connectivity between nodes by transition probabilities, a random walk guided graph neural network (RWGNN) method is proposed, in which random walk profiles are passed through a GNN to enable it to learn distance-aware node embeddings. For an unknown DTI, enclosing subnetworks are firstly extracted. Node-level, interaction-level and network-level transition probabilities of random walks (i.e., random walk profile) are calculated on these subnetworks. Then, each graph convolutional layer aggregates node embeddings from multi-hop neighbors according to attentional weights calculated from random walk profiles. Finally, the DTI’s drug embedding, protein embedding and random walk profile are aggregated to calculate an interaction score for it. The performance in inferring distant DTIs (≥ 3 hops away from known DTIs) has been improved by RWGNN (AUC=0.957) significantly, compared with GCN (AUC=0.582) and GIN (AUC=0.724). Besides, RWGNN is powerful in inferring DTIs for cold-start drugs and target proteins. Top-10 scored DTIs of RWGNN can be verified in literatures.

Recently, with the in-depth study of biological network structures, methods such as graph neural networks and graph contrastive learning have attracted significant attention and demonstrated notable advantages in DTI prediction. Nevertheless, it remains a challenging task that predicting new DTIs by a small number of known data in heterogeneous biological networks. Graph contrastive learning as an effective method to solute this issue has been proposed recently. Although contrastive learning have shown significant advantages, the objective function heavily relies on unbiased positive and negative samples. Inspired by this issue, a heterogeneous ranking contrastive learning method (HRCL-DTI) for DTI prediction is proposed. Specifically, multiple graph encoders are employed to capture the topological relationships in heterogeneous biological networks. After that, the prediction scores are calculated in the ranking module of HRCL-DTI to select reliable positive and negative samples for the heterogeneous contrastive learning module, aiming to enhance the consistency of DTI representations. Experimental results demonstrate that HRCL-DTI outperforms existing stateof-the-art baselines on multiple datasets and it possesses strong generalization ability and practical effectiveness.