In this work, we investigate the problem of learning knowledge from the massive community-contributed images with rich weakly-supervised context information, which can benefit multiple image understanding tasks simultaneously, such as social image tag refinement and assignment, content-based image retrieval, tag-based image retrieval and tag expansion. Towards this end, we propose a Deep Collaborative Embedding (DCE) model to uncover a unified latent space for images and tags. The proposed method incorporates the end-to-end learning and collaborative factor analysis in one unified framework for the optimal compatibility of representation learning and latent space discovery. A nonnegative and discrete refined tagging matrix is learned to guide the end-to-end learning. To collaboratively explore the rich context information of social images, the proposed method integrates the weakly-supervised image-tag correlation, image correlation and tag correlation simultaneously and seamlessly. The proposed model is also extended to embed new tags in the uncovered space. To verify the effectiveness of the proposed method, extensive experiments are conducted on two widely-used social image benchmarks for multiple social image understanding tasks. The encouraging performance of the proposed method over the state-of-the-art approaches demonstrates its superiority.

Contextual information has been shown to be powerful for semantic segmentation. This work proposes a novel Context-based Tandem Network (CTNet) by interactively exploring the spatial contextual information and the channel contextual information, which can discover the semantic context for semantic segmentation. Specifically, the Spatial Contextual Module (SCM) is leveraged to uncover the spatial contextual dependency between pixels by exploring the correlation between pixels and categories. Meanwhile, the Channel Contextual Module (CCM) is introduced to learn the semantic features including the semantic feature maps and class-specific features by modeling the long-term semantic dependence between channels. The learned semantic features are utilized as the prior knowledge to guide the learning of SCM, which can make SCM obtain more accurate long-range spatial dependency. Finally, to further improve the performance of the learned representations for semantic segmentation, the results of the two context modules are adaptively integrated to achieve better results. Extensive experiments are conducted on four widely-used datasets, i.e., PASCAL-Context, Cityscapes, ADE20K and PASCAL VOC2012. The results demonstrate the superior performance of the proposed CTNet by comparison with several state-of-the-art methods. The source code and models are available at https://github.com/syp2ysy/CTNet .

RGB-Thermal Salient Object Detection (RGB-T SOD) aims to pinpoint prominent objects within aligned pairs of visible and thermal infrared images. A key challenge lies in bridging the inherent disparities between RGB and Thermal modalities for effective saliency map prediction. Traditional encoder-decoder architectures, while designed for cross-modality feature interactions, may not have adequately considered the robustness against noise originating from defective modalities, thereby leading to suboptimal performance in complex scenarios. Inspired by hierarchical human visual systems, we propose the ConTriNet, a robust Confluent Triple-Flow Network employing a “Divide-and-Conquer” strategy. This framework utilizes a unified encoder with specialized decoders, each addressing different subtasks of exploring modality-specific and modality-complementary information for RGB-T SOD, thereby enhancing the final saliency map prediction. Specifically, ConTriNet comprises three flows: two modality-specific flows explore cues from RGB and Thermal modalities, and a third modality-complementary flow integrates cues from both modalities. ConTriNet presents several notable advantages. It incorporates a Modality-induced Feature Modulator (MFM) in the modality-shared union encoder to minimize inter-modality discrepancies and mitigate the impact of defective samples. Additionally, a foundational Residual Atrous Spatial Pyramid Module (RASPM) in the separated flows enlarges the receptive field, allowing for the capture of multi-scale contextual information. Furthermore, a Modality-aware Dynamic Aggregation Module (MDAM) in the modality-complementary flow dynamically aggregates saliency-related cues from both modality-specific flows. Leveraging the proposed parallel triple-flow framework, we further refine saliency maps derived from different flows through a flow-cooperative fusion strategy , yielding a high-quality, full-resolution saliency map for the final prediction. To evaluate the robustness and stability of our approach, we collect a comprehensive RGB-T SOD benchmark, VT-IMAG , covering various real-world challenging scenarios. Extensive experiments on public benchmarks and our VT-IMAG dataset demonstrate that ConTriNet consistently outperforms state-of-the-art competitors in both common and challenging scenarios, even when dealing with incomplete modality data. The code and VT-IMAG will be available at: https://cser-tang-hao.github.io/contrinet.html .

Dimensionality reduction has attracted increasing attention, because high-dimensional data have arisen naturally in numerous domains in recent years. As one popular dimensionality reduction method, nonnegative matrix factorization (NMF), whose goal is to learn parts-based representations, has been widely studied and applied to various applications. In contrast to the previous approaches, this paper proposes a novel semi supervised NMF learning framework, called robust structured NMF, that learns a robust discriminative representation by leveraging the block-diagonal structure and the l _2,p -norm (especially when 0 <; p 1) loss function. Specifically, the problems of noise and outliers are well addressed by the l _2,p -norm (0 <; p 1) loss function, while the discriminative representations of both the labeled and unlabeled data are simultaneously learned by explicitly exploring the block-diagonal structure. The proposed problem is formulated as an optimization problem with a welldefined objective function solved by the proposed iterative algorithm. The convergence of the proposed optimization algorithm is analyzed both theoretically and empirically. In addition, we also discuss the relationships between the proposed method and some previous methods. Extensive experiments on both the synthetic and real-world data sets are conducted, and the experimental results demonstrate the effectiveness of the proposed method in comparison to the state-of-the-art methods.

The number of images associated with weakly supervised user-provided tags has increased dramatically in recent years. User-provided tags are incomplete, subjective and noisy. In this paper, we focus on the problem of social image understanding, i.e., tag refinement, tag assignment, and image retrieval. Different from previous work, we propose a novel weakly supervised deep matrix factorization algorithm, which uncovers the latent image representations and tag representations embedded in the latent subspace by collaboratively exploring the weakly supervised tagging information, the visual structure, and the semantic structure. Due to the well-known semantic gap, the hidden representations of images are learned by a hierarchical model, which are progressively transformed from the visual feature space. It can naturally embed new images into the subspace using the learned deep architecture. The semantic and visual structures are jointly incorporated to learn a semantic subspace without overfitting the noisy, incomplete, or subjective tags. Besides, to remove the noisy or redundant visual features, a sparse model is imposed on the transformation matrix of the first layer in the deep architecture. Finally, a unified optimization problem with a well-defined objective function is developed to formulate the proposed problem and solved by a gradient descent procedure with curvilinear search. Extensive experiments on real-world social image databases are conducted on the tasks of image understanding: image tag refinement, assignment, and retrieval. Encouraging results are achieved with comparison with the state-of-the-art algorithms, which demonstrates the effectiveness of the proposed method.