Recent camouflaged object detection (COD) approaches have been proposed to accurately segment objects blended into surroundings. The most challenging and critical issue in COD is to find out the lines of demarcation between objects and background in the camouflage environment. Because of the similarity between the target object and the background, these lines are difficult to be found accurately. However, these are easy to be observed in different frequency components of the image. To this end, in this paper we rethink COD from the perspective of frequency components and propose a Frequency Representation Integration Network to mine informative cues from them. Specifically, we obtain high-frequency components from the original image by Laplacian pyramid-like decomposition, and then respectively send the image to a transformer-based encoder and frequency components to a tailored CNN-based Residual Frequency Array Encoder. Besides, we utilize the multi-head self-attention in transformer encoder to capture low-frequency signals, which can effectively parse the overall contextual information of camouflage scenes. We also design a Frequency Representation Reasoning Module, which progressively eliminates discrepancies between differentiated frequency representations and integrates them by modeling their point-wise relations. Moreover, to further bridge different frequency representations, we introduce the image reconstruction task to implicitly guide their integration. Sufficient experiments on three widely-used COD benchmark datasets demonstrate that our method surpasses existing state-of-the-art methods by a large margin.

Image-based salient object detection (ISOD) in$360^{\circ }$scenarios is significant for understanding and applying panoramic information. However, research on$360^{\circ }$ISOD has not been widely explored due to the lack of large, complex, high-resolution, and well-labeled datasets. Towards this end, we construct a large scale$360^{\circ }$ISOD dataset with object-level pixel-wise annotation on equirectangular projection (ERP), which contains rich panoramic scenes with not less than 2K resolution and is the largest dataset for$360^{\circ }$ISOD by far to our best knowledge. By observing the data, we find current methods face three significant challenges in panoramic scenarios: diverse distortion degrees, discontinuous edge effects and changeable object scales. Inspired by humans' observing process, we propose a view-aware salient object detection method based on a Sample Adaptive View Transformer (SAVT) module with two sub-modules to mitigate these issues. Specifically, the sub-module View Transformer (VT) contains three transform branches based on different kinds of transformations to learn various features under different views and heighten the model's feature toleration of distortion, edge effects and object scales. Moreover, the sub-module Sample Adaptive Fusion (SAF) is to adjust the weights of different transform branches based on various sample features and make transformed enhanced features fuse more appropriately. The benchmark results of 20 state-of-the-art ISOD methods reveal the constructed dataset is very challenging. Moreover, exhaustive experiments verify the proposed approach is practical and outperforms the state-of-the-art methods.