Camouflaged object detection, which aims to detect/segment the object(s) that blend in with their surrounding, remains challenging for deep models due to the intrinsic similarities between foreground objects and background surroundings. Ideally, an effective model should be capable of finding valuable clues from the given scene and integrating them into a joint learning framework to co-enhance the representation. Inspired by this observation, we propose a novel Mutual Graph Learning (MGL) model by shifting the conventional perspective of mutual learning from regular grids to graph domain. Specifically, an image is decoupled by MGL into two task-specific feature maps — one for finding the rough location of the target and the other for capturing its accurate boundary details. Then, the mutual benefits can be fully exploited by reasoning their high-order relations through graphs recurrently. It should be noted that our method is different from most mutual learning models that model all between-task interactions with the use of a shared function. To increase information interactions, MGL is built with typed functions for dealing with different complementary relations. To overcome the accuracy loss caused by interpolation to higher resolution and the computational redundancy resulting from recurrent learning, the S-MGL is equipped with a multi-source attention contextual recovery module, called R-MGL_v2, which uses the pixel feature information iteratively. Experiments on challenging datasets, including CHAMELEON, CAMO, COD10K, and NC4K demonstrate the effectiveness of our MGL with superior performance to existing state-of-the-art methods. The code can be found at https://github.com/fanyang587/MGL.

Cross-modal retrieval aims to retrieve relevant content of different modalities by giving a query of another modality. The biggest difficulty is how to bridge the heterogeneous gap between different modalities. The commonly-used methods tend to focus on exploiting individual image-text pair and mining the relations of cross-modality data thereof, but ignore the role of multi-sample correlation. Moreover, more global, structural inter-pair knowledge contained by the training dataset will be under-used. To fully exploit graph-structured semantics and mine the semantic information in the dataset for learning discriminative representations, we propose Weighted Graph-structured Semantics Constraint Network (WGSCN), a unified, graph-based, semantic-constrained learning framework,in which GCN is used to mine comprehensive relation information from cross modality data. Our main inspiration is to design a novel two-branch GCN-based Cross-modal Semantic Encoding (GCSE) module to produce semantic embeddings with the both modality-specific and modality-shared correlation. Moreover, a GAN-based dual learning approach is used to further improve the discriminability and model the joint distribution across different modalities. Our proposed GDL uses semantic embeddings as supervisory signal to make the common representation semantically discriminative while adversarial learning and dual learning are used to make the common representation modality-invariant. Through comparative experiments on five commonly used cross-modal datasets, we have shown the superior retrieval accuracy of our WGSCN.

We study and address the multi-view crowd counting (MVCC) problem which poses more realistic challenges than single-view crowd counting for better facilitating crowd management / public safety systems. Its major challenge lies in how to fully distill and aggregate useful, complementary information among multiple camera views to create powerful ground-plane representations for wide-area crowd analysis. In this paper, we present a graph-based, multi-view learning model called Co-Communication Graph Convolutional Network (CoCo-GCN) to jointly investigate intra-view contextual dependencies and inter-view complementary relations. More specifically, CoCo-GCN builds a view-agnostic graph interaction space for each camera view to conduct efficient contextual reasoning, and extends the intra-view reasoning by using a novel Graph Communication Layer (GCL) to also take between-graph (cross-view), complementary information into account. Moreover, CoCo-GCN uses a new Co-Memory Layer (CoML) to jointly coarsen the graphs and close the ‘representational gap’ among them for further exploiting the compositional nature of graphs and learning more consistent representations. Finally, these jointly learned features of multiple views can be easily fused to create ground-plane representations for wide-area crowd counting. Experiments show that the proposed CoCo-GCN achieves state-of-the-art results on three MVCC datasets, i.e., PETS2009, DukeMTMC, and City Street, significantly improving the scene-level accuracy over previous models.

Scene parsing, dictionary learning, representation learning