The predator has the ability to quickly respond to the misjudged decision and hunt the camouflaged target by analyzing its movement. Those decision compensation and movement analysis for hunting are closely tied to temporal and spatial information. This can be mirrored in the video camouflaged object detection (VCOD) task where the captured temporal information may be misjudged as well as the spatial information tends to be inaccurate in complex scenes. Thus, two key factors should be considered in the VCOD task: How can a model cope with the misjudged temporal information; How can spatial features interact with the temporal information to understand dynamic scenes? To this end, we propose a predator-mimicking network (PMNet) equipped with a selective temporal alignment module (STAM) and a temporal-spatial feedback module (T-SFM). The STAM is designed to alleviate the influence of the misjudged motion trajectory by adopting our adaptive selection mechanism from a novel perspective. In T-SFM, the temporal information works as the self-knowledge to provide assistance and interact with spatial features, enabling the model to effectively detect the camouflaged object. Experimental results demonstrate that our method achieves state-of-the-art performance on VCOD benchmarks. Furthermore, our model can be generalized in the video salient object detection (VSOD) task and also outperforms existing state-of-the-art methods. The source code will be publicly available athttps://github.com/LiuTingWed/CriDiff.

The ability to deal with intra and inter-modality features has been critical to the development of RGB-D salient object detection. While many works have advanced in leaps and bounds in this field, most existing methods have not taken their way down into the inherent differences between the RGB and depth data due to widely adopted conventional convolution in which fixed parameter kernels are applied during inference. To promote intra and inter-modality interaction conditioned on various scenarios, as RGB and depth data are processed independently and later fused interactively, we develop a new insight and a better model. In this paper, we introduce a criss-cross dynamic filter network by decoupling dynamic convolution. First, we propose a Model-specific Dynamic Enhanced Module (MDEM) that dynamically enhances the intra-modality features with global context guidance. Second, we propose a Scene-aware Dynamic Fusion Module (SDFM) to realize dynamic feature selection between two modalities. As a result, our model achieves accurate predictions of salient objects. Extensive experiments demonstrate that our method achieves competitive performance over 28 state-of-the-art RGB-D methods on 7 public datasets.

Most methods for RGB-D salient object detection (SOD) utilize the same fusion strategy to explore the cross-modal complementary information at each level. However, this may ignore different feature contributions from two modalities on different levels towards prediction. In this paper, we propose a novel top-down multi-level fusion structure where different fusion strategies are utilized to effectively explore the low-level and high-level features. This is achieved by designing the interweave fusion module (IFM) to effectively integrate the global information and designing the gated select fusion module (GSFM) to discriminatively select useful local information by filtering out the unnecessary one from RGB and depth data. Moreover, we propose an adaptive fusion module (AFM) to reintegrate the fused cross-modal features of each level to predict a more accurate result. Comprehensive experiments on 7 challenging benchmark datasets demonstrate that our method achieves the competitive performance over 14 state-of-the-art RGB-D alternative methods.