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.

Depth data with a predominance of discriminative power in location is advantageous for accurate salient object detection (SOD). Existing RGBD SOD methods have focused on how to properly use depth information for complementary fusion with RGB data, having achieved great success. In this work, we attempt a far more ambitious use of the depth information by injecting the depth maps into the encoder in a single-stream model. Specifically, we propose a depth injection framework (DIF) equipped with an Injection Scheme (IS) and a Depth Injection Module (DIM). The proposed IS enhances the semantic representation of the RGB features in the encoder by directly injecting depth maps into the high-level encoder blocks, while helping our model maintain computational convenience. Our proposed DIM acts as a bridge between the depth maps and the hierarchical RGB features of the encoder and helps the information of two modalities complement and guide each other, contributing to a great fusion effect. Experimental results demonstrate that our proposed method can achieve state-of-the-art performance on six RGBD datasets. Moreover, our method can achieve excellent performance on RGBT SOD and our DIM can be easily applied to single-stream SOD models and the transformer architecture, proving a powerful generalization ability.

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.

In this work, we propose a novel depth-induced multi-scale recurrent attention network for RGB-D saliency detection, named as DMRA. It achieves dramatic performance especially in complex scenarios. There are four main contributions of our network that are experimentally demonstrated to have significant practical merits. First, we design an effective depth refinement block using residual connections to fully extract and fuse cross-modal complementary cues from RGB and depth streams. Second, depth cues with abundant spatial information are innovatively combined with multi-scale contextual features for accurately locating salient objects. Third, a novel recurrent attention module inspired by Internal Generative Mechanism of human brain is designed to generate more accurate saliency results via comprehensively learning the internal semantic relation of the fused feature and progressively optimizing local details with memory-oriented scene understanding. Finally, a cascaded hierarchical feature fusion strategy is designed to promote efficient information interaction of multi-level contextual features and further improve the contextual representability of model. In addition, we introduce a new real-life RGB-D saliency dataset containing a variety of complex scenarios that has been widely used as a benchmark dataset in recent RGB-D saliency detection research. Extensive empirical experiments demonstrate that our method can accurately identify salient objects and achieve appealing performance against 18 state-of-the-art RGB-D saliency models on nine benchmark datasets.

Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD). This naturally leads to the incorporation of depth information in addition to the conventional RGB image as input, known as RGB-D SOD or depth-aware SOD. Meanwhile, this emerging line of research has been considerably hindered by the noise and ambiguity that prevail in raw depth images. To address the aforementioned issues, we propose a Depth Calibration and Fusion (DCF) framework that contains two novel components: 1) a learning strategy to calibrate the latent bias in the original depth maps towards boosting the SOD performance; 2) a simple yet effective cross reference module to fuse features from both RGB and depth modalities. Extensive empirical experiments demonstrate that the proposed approach achieves superior performance against 27 state-of-the-art methods. Moreover, our depth calibration strategy alone can work as a preprocessing step; empirically it results in noticeable improvements when being applied to existing cutting-edge RGB-D SOD models. Source code is available at https://github.com/jiwei0921/DCF.

The ability to capture inter-frame dynamics has been critical to the development of video salient object detection (VSOD). While many works have achieved great success in this field, a deeper insight into its dynamic nature should be developed. In this work, we aim to answer the following questions: How can a model adjust itself to dynamic variations as well as perceive fine differences in the real-world environment; How are the temporal dynamics well introduced into spatial information over time? To this end, we propose a dynamic context-sensitive filtering network (DCFNet) equipped with a dynamic context-sensitive filtering module (DCFM) and an effective bidirectional dynamic fusion strategy. The proposed DCFM sheds new light on dynamic filter generation by extracting location-related affinities between consecutive frames. Our bidirectional dynamic fusion strategy encourages the interaction of spatial and temporal information in a dynamic manner. Experimental results demonstrate that our proposed method can achieve state-of-the-art performance on most VSOD datasets while ensuring a real-time speed of 28 fps. The source code is publicly available at https://github.com/OIPLab-DUT/DCFNet.

Multi-scale features fusion plays a critical role in salient object detection. Most of existing methods have achieved remarkable performance by exploiting various multi-scale features fusion strategies. However, an elegant fusion framework requires expert knowledge and experience, heavily relying on laborious trial and error. In this paper, we propose a multi-scale features fusion framework based on Neural Architecture Search (NAS), named Auto-MSFNet. First, we design a novel search cell, named FusionCell to automatically decide multi-scale features aggregation. Rather than searching one repeatable cell stacked, we allow different FusionCells to flexibly integrate multi-level features. Simultaneously, considering features generated from CNNs are naturally spatial and channel-wise, we propose a new search space for efficiently focusing on the most relevant information. The search space mitigates incomplete object structures or over-predicted foreground regions caused by progressive fusion. Second, we propose a progressive polishing loss to further obtain exquisite boundaries by penalizing misalignment of salient object boundaries. Extensive experiments on five benchmark datasets demonstrate the effectiveness of the proposed method and achieve state-of-the-art performance on four evaluation metrics. The code and results of our method are available at https://github.com/OIPLab-DUT/Auto-MSFNet.