In this paper, we propose a novel two-stream neural network for video saliency prediction. Unlike some traditional methods developed based on hand-crafted feature extraction and integration, our proposed method automatically learns saliency related spatiotemporal features from human fixations without any pre-processing, post-processing or manual tuning. Video frames are routed through the spatial stream network to compute static or color saliency maps for each of them. And a new two-stage temporal stream network is proposed, which is composed by a pre-trained 2D-CNN model (SF-Net) to extract saliency related features and a shallow 3D-CNN model (Te-Net) to process these features, for temporal or dynamic saliency maps. It can reduce requirement of video gaze data, improve training efficiency and achieve high performance. A fusion network is adopted to combine the outputs of both streams and generate the final saliency maps. Besides, a Convolutional Gaussian Priors (CGP) layer is proposed to learn the bias phenomenon in viewing behavior to improve the performance of video saliency prediction. The proposed method is compared with state-of-the-art saliency models on two public video saliency benchmark datasets. Results demonstrate that our model can achieve advanced performance on video saliency prediction.

In this paper, a recurrent neural network is designed for video saliency prediction considering spatial-temporal features. In our work, video frames are routed through the static network for spatial features and the dynamic network for temporal features. For the spatial-temporal feature integration, a novel select and re-weight fusion model is proposed which can learn and adjust the fusion weights based on the spatial and temporal features in different scenes automatically. Finally, an attention-aware convolutional long short term memory (ConvLSTM) network is developed to predict salient regions based on the features extracted from consecutive frames and generate the ultimate saliency map for each video frame. The proposed method is compared with state-of-the-art saliency models on five public video saliency benchmark datasets. The experimental results demonstrate that our model can achieve advanced performance on video saliency prediction.

In this paper, an attentive cross-modal fusion (ACMF) network is proposed for RGB-D salient object detection. The proposed method selectively fuses features in a cross-modal manner and uses a fusion refinement module to fuse output features from different resolutions. Our attentive cross-modal fusion network is built based on residual attention. In each level of ResNet output, both the RGB and depth features are turned into an identity map and a weighted attention map. The identity map is reweighted by the attention map of the paired modality. Moreover, the lower level features with higher resolution are adopted to refine the boundary of detected targets. The entire architecture can be trained end-to-end. The proposed ACMF is compared with state-of-the-art methods on eight recent datasets. The results demonstrate that our model can achieve advanced performance on RGB-D salient object detection.

Continuous Conditional Generative Modeling (CCGM) estimates high-dimensional data distributions, such as images, conditioned on scalar continuous variables (aka regression labels). While Continuous Conditional Generative Adversarial Networks (CcGANs) were designed for this task, their instability during adversarial learning often leads to suboptimal results. Conditional Diffusion Models (CDMs) offer a promising alternative, generating more realistic images, but their diffusion processes, label conditioning, and model fitting procedures are either not optimized for or incompatible with CCGM, making it difficult to integrate CcGANs' vicinal approach. To address these issues, we introduce Continuous Conditional Diffusion Models (CCDMs), the first CDM specifically tailored for CCGM. CCDMs address existing limitations with specially designed conditional diffusion processes, a novel hard vicinal image denoising loss, a customized label embedding method, and efficient conditional sampling procedures. Through comprehensive experiments on four datasets with resolutions ranging from $64\times 64$ to $192\times 192$, we demonstrate that CCDMs outperform state-of-the-art CCGM models, establishing a new benchmark. Ablation studies further validate the model design and implementation, highlighting that some widely used CDM implementations are ineffective for the CCGM task. Our code is publicly available at https://github.com/UBCDingXin/CCDM.