The field of computer vision is witnessing significant advancements in camouflaged object detection and related tasks. Researchers are proposing innovative architectures and techniques to address the challenges of detecting objects that blend seamlessly with their surroundings. A notable trend is the development of specialized dual-pathway decoder architectures and attention-based mechanisms to improve feature representation and object detection. Another area of focus is the handling of occlusion, with frameworks being proposed to reconstruct occluded object features and preserve discriminative properties. The use of multi-scale recursive networks, gradient-based backpropagation, and overlapped windows cross-attention mechanisms are also being explored to enhance detection performance. Noteworthy papers in this area include: C3Net, which achieves state-of-the-art performance on camouflaged object detection benchmarks, and CountOCC, which presents a framework for open-world amodal counting that explicitly reconstructs occluded object features. MSRNet and RFMNet also demonstrate impressive results in camouflaged object detection and referring camouflaged object detection, respectively. Furthermore, JFD3 proposes a novel end-to-end framework for prior-guided infrared UAV target detection that enhances feature representation under blur conditions. These advancements have the potential to significantly impact various applications, including surveillance, robotics, and autonomous systems.