Camouflaged object detection (COD) aims to segment concealed objects from their surroundings. However, most existing frequency-domain methods rely on simplistic fusion schemes with RGB features especially for objects with severe occlusion, varying scales, or ambiguous appearance. In this paper, we propose a frequency-guided COD network to explore how to utilize frequency-domain information to enhance the learning and presentation ability of RGB-domain features. Specifically, the frequency-aware query module is designed to selectively focus on essential RGB features by capturing and integrating long-term dependencies from frequency-domain information. Additionally, to enhance the model’s capability in identify objects of varying scales, the perception enhancement module is proposed to sufficiently integrate related and complementary features across adjacent levels. Finally, the dynamic balance module is introduced to aggregate multi-level features by adaptively balancing global contextual knowledge with local details information. Experiments on three widely-used benchmarks demonstrate the effectiveness and superiority of our network. The source code is available at https://github.com/iuueong/FPDNet.

Existing camouflaged object detection (COD) approaches primarily rely on RGB domain features to segment camouflaged objects. However, they face a major limitation: insensitivity to subtle differences in colors, textures and patterns, which contradicts the essence of COD that discerns imperceptible cues of camouflaged objects. To address this challenge, we propose a dual-domain collaborative learning network, which leverages frequency domain cues to collaborate with RGB domain features, therefore utilizing their unique and complementary strengths for effectively detecting camouflaged objects. Specifically, we propose the dual-domain feature integration (DFI) module, which aggregates cross-level RGB and frequency features to alleviate level-specific limitations, resulting in discriminative object features. Furthermore, we design the frequency-aware global localization (FGL) module, employing different frequency components to perceive global contexts. Additionally, we introduce the foreground-background separation learning (FSL) module to simultaneously capture semantic contexts and intricate details in both foreground and background. Extensive experimental results demonstrate the effectiveness of our network. Our results are available at https://github.com/ZhangQing0329/DCLNet

RGB-Thermal salient object detection (RGB-T SOD) aims to identify and segment visually prominent objects by leveraging complementary information from RGB and thermal modalities. A key challenge lies in exploiting both the uniqueness and shared characteristics of these modalities to enhance their collaboration. Existing methods often ignore the optimization of unimodal features and the level-specific modality discrepancy, leading to noisy and redundant multi-modal feature representations. To address these limitations, we propose a novel RGB-T SOD network, HEFNet, which employs hierarchical unimodal enhancement and multi-modal fusion to achieve precise segmentation. Specifically, we introduce the unimodal feature enhancement (UFE) module, which refines RGB and thermal features by incorporating complementary information from adjacent levels, thereby enhancing saliency cues and suppressing noise distractions. Additionally, the hierarchical multi-modal fusion (HMF) module is designed to generate robust cross-modal feature representation. By employing tailored refinement and fusion strategies within the UFE and HMF modules, our network fully exploits the strengths of each modality, facilitating the generation of discriminative cross-modal features. Finally, the multi-level feature integration (MFI) module is introduced to progressively aggregate features across levels to ensure accurate saliency predictions. Extensive experiments demonstrate that our method achieves state-of-the-art performance, verifying its effectiveness and superiority over existing RGB-T SOD approaches. Our results are available at https://github.com/ZhangQing0329/HEFNet

A high-quality guidance cue is critical for accurately segmenting camouflaged objects from their visually similar surroundings. In this paper, we present a novel camouflaged object detection network to achieve complete segmentation predictions with fine-grained details by exploring how to generate and utilize the high-quality guidance cue. We first propose a feature self-calibration module to suppress noise and highlight camouflaged object regions from a contextual and spatial perspective. Based on the calibrated features, the proposed boundary-aware localization (BAL) module captures the coarse position information of camouflaged objects. Furthermore, the position information as the guidance cue is further refined iteratively by the context guidance refinement (CGR) module to effectively inform the network’s learning process. Finally, the progressive feature shrinking (PFS) module integrates adjacent features in a hierarchical manner to produce the final segmentation result. Experimental results on widely-used benchmark datasets demonstrate the effectiveness and superiority of our network. Our results are available at https://github.com/ZhangQing0329/FGNet

We propose a simple but effective approach to detect salient objects by exploring both patch-level and object-level cues under the framework of absorbing Markov chain. Saliency detection is carried out in a two-stage scheme. In the first stage, we conduct random walk on absorbing Markov chain with coarsely selected background seeds in the boundary. The result is integrated with a objectness map which is generated by finding potential object candidates to boost the saliency detection for object completeness. And in the second stage, we use the refined background seeds computed by the first stage as absorbing nodes for the absorbing Markov chain to obtain the final saliency map. Experimental results on four publicly available datasets demonstrate the robustness and efficiency of our proposed approach against 8 state-of-the-art methods in terms of five performance criterions.