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Automatically identifying objects similar to the surroundings is a complex and difficult task in real-world scenarios. In addition to the high intrinsic similarity between camouflaged objects and their backgrounds, these objects are usually diverse in scale and blurred in appearance. And the deceptive nature of the camouflaged objects introduces lots of noise into the features and generates inaccurate segmentation map extracted by deep learning model. We tackle these problems by proposing a novel dynamic interactive refinement network (DIRNet), which aims to make the features exploit effective details and semantics together as well as discard interference information. Specifically, we utilize bilateral interaction module (BIM) to interact with foreground and background information to conduct contextual exploration, which can capture more meaningful details and refine the confusion. Additionally, in the purpose of retaining the appropriate information and erasing noise, we design an adjacent aggregation interaction module (AAIM) to integrate the adjacent multi-level features with attention coefficients for each layer. The final results are obtained through the dynamic refinement of the BIM and AAIM. Extensive quantitative and qualitative experiments on four public benchmark datasets demonstrate that our proposed DIRNet is an effective COD framework and outperforms 14 state-of-the-art models. [ABSTRACT FROM AUTHOR]

Most of the cutting-edge 3D convolutional-based video saliency prediction models adopt a fully convolutional encoder-decoder architecture, which provides a good spatiotemporal representation of salient regions. However, the spatiotemporal cues of the saliency models will be continuously diluted during the decoding process, leading to a decreased ability to locate salient regions. To address this limitation, we propose a simple and effective gated fusion network (GFNet) to conduct video saliency prediction. Specifically, GFNet is built on a fully 3D convolutional encoder-decoder architecture and consists of a key component named the gated fusion (GF) module, which acts as a message screening unit between each level encoder and decoder features. In the GF module, the gate can be obtained via the combination of features from the previous decoder block and the current encoder block, which is employed to weight the encoder features. In this way, GFNet can control the message passing between each level encoder and decoder block. Extensive experimental results on four video saliency datasets show that our method achieves comparable performance against state-of-the-art models. The code is available at https://github.com/wusonghe/GFNet. [ABSTRACT FROM AUTHOR]

Bokeh effect is usually used to highlight major contents in an image. Limited by the small sensors, cameras on smartphones are less sensitive to the depth information and cannot directly produce bokeh effect as pleasant as digital single lens reflex cameras. To address this problem, a depth-guided deep filtering network, called DDFN, is proposed in this study. Specifically, the focused region detection block is designed to detect the salient areas, and the depth estimated block is introduced to estimate depth maps from full-focus images. Further, combining depth maps and focused features, an adaptive rendering block is proposed to synthesize bokeh effect with adaptive cross 1-D filters. Both quantitative and qualitative evaluations on the public datasets demonstrate that the proposed model performs favorably against state-of-the-art methods in terms of rendering effects and has lower computational cost, e.g., 24.07 dB PSNR on EBB! dataset and 0.45 s inference times for a 512 × 768 image on a Snapdragon 865 mobile processor. [ABSTRACT FROM AUTHOR]