Weakly supervised camouflaged object detection (WS-COD) aims to address the critical task of identifying visually assimilated objects concealed within heterogeneous backgrounds under sparse supervisory signals. However, current WS-COD frameworks suffer from compromised structural integrity, stemming from cross-hierarchical feature discrepancy and constrained cross-level information flow, which induces structural misalignment and context fragmentation in multi-granularity feature fusion. To overcome the limitation, we propose a novel SAM-guided Resolution Iteration Learning Network (SAM-RNet) that synergizes foundation model priors with multi-resolution feature refinement. Our technical contributions are threefold: (1) We utilize the Segment Anything Model (SAM) to produce high-quality masks, effectively mitigating supervision insufficiency through large-scale visual knowledge distillation. (2) We design a resolution iteration mechanism where high-resolution features progressively refine low-resolution counterparts through an Interactive Refinement Module (IRM) - a dual-branch architecture enabling hierarchical feature interaction and enhancement through branch collaboration and attention mechanism, complemented by an iterative feedback loss to enforce multi-scale feature learning. (3) We develop a Decoder with cross-layer fusion operations, enabling the aggregation of features from object and background contexts for precise object segmentation. Finally, extensive experiments demonstrate that SAM-RNet is superior to existing WS-COD methods across three COD datasets, achieving average improvements of 4.37%, 4.60%, 7.00%, and 24.06% in$S_{\alpha }$,$E_{\phi }$,$F_{\beta }^{\omega }$, and$M$, respectively.

Camouflaged Object Detection(COD) aims to segment objects with a similar appearance to the background. There are some problems in existing algorithms, such as blurred edges and incomplete detection. To address the above issues, we propose a novel COD framework termed CFNet . Our network consists of the Contradiction Area Detection Module(CADM) and Feature Aggregation Module(FAM). In the CADM, we propose an improved receptive field mechanism, which utilizes max pooling operation and convolution block to highlight the contradictory areas and refine the edge of the hidden object. Besides, the FAM is designed to connect two adjacent layers via attention and anti-attention strategy, leading to cross-layer feature enhancement and information fusion. More specifically, the self-attention mechanism helps supplement semantic information, and the anti-attention mechanism contributes to removing redundant information. Extensive experiments conducted on the four public COD datasets show the comparable performance of the proposed CFNet with SOTAs, and the ablation experiments demonstrate the effectiveness of the proposed modules.

In this article, we provide a comprehensive study of a new task called collaborative camouflaged object detection (CoCOD), which aims to simultaneously detect camouflaged objects with the same properties from a group of relevant images. To this end, we meticulously construct the first large-scale dataset, termed CoCOD8K, which consists of 8528 high-quality and elaborately selected images with object mask annotations, covering five superclasses and 70 subclasses. The dataset spans a wide range of natural and artificial camouflage scenes with diverse object appearances and backgrounds, making it a very challenging dataset for CoCOD. Besides, we propose the first baseline model for CoCOD, named bilateral-branch network (BBNet), which explores and aggregates co-camouflaged cues within a single image and between images within a group, respectively, for accurate camouflaged object detection (COD) in given images. This is implemented by an interimage collaborative feature exploration (CFE) module, an intraimage object feature search (OFS) module, and a local-global refinement (LGR) module. We benchmark 18 state-of-the-art (SOTA) models, including 12 COD algorithms and six CoSOD algorithms, on the proposed CoCOD8K dataset under five widely used evaluation metrics. Extensive experiments demonstrate the effectiveness of the proposed method and the significantly superior performance compared to other competitors. We hope that our proposed dataset and model will boost growth in the COD community. The dataset, model, and results will be available at: https://github.com/zc199823/BBNet-CoCOD.

The purpose of co-salient object detection (CoSOD) is to detect the salient objects that co-occur in a group of relevant images. CoSOD has been significantly prospered by recent advances in convolutional neural networks (CNNs). However, it shows general limitations in modeling long-range feature dependencies, which is crucial for CoSOD. In the vision transformer, the self-attention mechanism is utilized to capture global dependencies but unfortunately destroy local spatial details, which are also essential for CoSOD. To address the above issues, we propose a dual network structure, called TCNet, which can efficiently excavate both local information and global representations for co-saliency learning via the parallel interaction of Transformers and CNNs. Specifically, it contains three critical components, i.e., the mutual consensus module (MCM), the consensus complementary module (CCM), and the group consistent progressive decoder (GCPD). MCM aims to capture the global consensus from high-level features of these two branches as a guide for the following integration of consensus cues of both branches at each level. Next, CCM is designed to effectively fuse the consensus of local information and global contexts from different levels of the two branches. Finally, GCPD is developed to maintain group feature consistency and predict accurate co-saliency maps. The proposed TCNet is evaluated on five challenging CoSOD benchmark datasets using six widely used metrics, showing that our proposed method is superior to other existing cutting-edge methods for co-salient object detection.

Camouflaged object detection (COD) is an emerging visual detection task, which aims to locate and distinguish the disguised target in complex backgrounds by imitating the human visual detection system. Recently, COD has attracted increasing attention in computer vision, and a few models of camouflaged object detection have been successfully explored. However, most existing works primarily focus on modeling camouflaged object detection over in-depth analyzing existing COD structures. To the best of our knowledge, a systematic review for COD has not been publicly reported, especially for recently proposed deep learning-based COD models. To make up this vacancy, we firstly proposed a comprehensive review on both COD models and public benchmark datasets and provide potential directions for future COD studies. Specifically, we conduct a comprehensive summary of 39 existing COD models from 1998 to 2021. And then, to facilitate subsequent research on COD, we classify the existing structures into two categories, 27 traditional handcrafted feature-based structures and 12 structures based on deep learning. In addition, we further group traditional handcrafted feature-based structures into six sub-classes based on the detection mechanism: texture, color, motion, intensity, optical flow, and multi-modal fusion. Furthermore, we take an in-depth analysis of the deep learning-based structure based on both detection motivation and detection performance and evaluate the performance of each structure. Moreover, we sum up four widely used COD datasets and describe the details of each one. Finally, we also discuss the limitations of COD and the corresponding solutions to improve detection accuracy. We still mention the relevant applications of camouflaged object detection and its future research directions to promote the development of camouflaged object detection.

Salient object detection is used as a pre-process in many computer vision tasks (such as salient object segmentation, video salient object detection, etc.). When performing salient object detection, depth information can provide clues to the location of target objects, so effective fusion of RGB and depth feature information is important. In this paper, we propose a new feature information aggregation approach, weighted group integration (WGI), to effectively integrate RGB and depth feature information. We use a dual-branch structure to slice the input RGB image and depth map separately and then merge the results separately by concatenation. As grouped features may lose global information about the target object, we also make use of the idea of residual learning, taking the features captured by the original fusion method as supplementary information to ensure both accuracy and completeness of the fused information. Experiments on five datasets show that our model performs better than typical existing approaches for four evaluation metrics.

We investigate conditional generative adversarial network (cGAN) as a solution to realize the face-to-sketch translation problems. These networks not only learn the mapping relationships between the face and responding sketch, but also generate a loss function to train the mapping relationships automatically. This makes it possible to regard the transformation problems as minimizing the loss function. In previous works, cGAN employs a single scale to resolve the above problems and lacks multi-scale information. In this work, considering that image multi-scale representation can capture image texture, structure and other important features more effectively, and we construct a three-layer pyramid model to obtain multi-scale information, and employ the proposed multiscale cGAN to train the mapping relationships. With respects to four metrics, our method outperforms previous models.

This paper proposes a fast detection model for video salient objects based on recurrent network architecture. Firstly, a multi-level attention (MLA) module is designed, which integrates multi-level feature maps in a cascaded manner. It effectively extracts the semantic information and detailed information of the intra-frame. These spatial features are input into a deeper bidirectional ConvLSTM to learn temporal dependence. Secondly, the result of the forward flow output is used as a backward input, and deeper temporal dependence is extracted. Finally, we present a spatial-temporal fused bidirectional ConvLSTM framework, which reduces the accumulated memory in the bidirectional ConvLSTM by exploiting element level fusion strategy. The experimental results show that the proposed method achieves the best detection precision on the two challenging benchmarks: ViSal and FBMS datasets, with a real-time speed of 23 fps.