360{\deg} salient object detection (SOD) aims to accurately segment salient regions across a full field of view. However, equirectangular projection (ERP) introduces severe spatial distortion when mapping the spherical domain onto a planar representation. Existing methods mainly focus on compensating projection distortion while overlooking the interaction between panoramic geometry and salient object structure during feature perception and prediction refinement. To this end, we propose SphereSOD, an ERP-native framework that couples panoramic geometry with evolving salient structures. Specifically, spherical geometry governs feature sampling and spatial weighting, while coarse-grained saliency and contour prediction influence context aggregation during the progressive decoding process. SphereSOD first initializes deformable sampling based on spherical projection geometry and then employs bounded, content-adaptive offsets, yielding features that are better aligned with the underlying panoramic geometry. Subsequently, the decoder performs structure-guided context aggregation and progressive refinement to recover complete salient regions and accurate boundaries. Extensive experiments on three public 360{\deg} SOD benchmarks demonstrate state-of-the-art performance and a favorable accuracy-efficiency trade-off, supporting structurepreserving inference directly in ERP space as a promising alternative to projection-heavy panoramic pipelines.
Jun-Song Zhang, Zhi-Jie Shen, Shuai Zheng et al.· 0 citations
Federated Graph Learning (FGL) aims to maximize the benefits of each graph owner, which is a common form of distributed graph learning under privacy-preserving conditions. As the landscape of local clients becomes increasingly diverse in terms of both model architectures and topological complexities, graph heterogeneity turns out to be one of the significant challenges to efficient collaboration among clients. Beyond existing paradigms, we delve a fresh insight into revisiting FGL as a semantic condensed graph alignment problem in this work. From this perspective, HashFGL is proposed for heterogeneous FGL through aligning condensed graphs via hashing in a symbiotic space. Specifically, the core of HashFGL lies in that it introduces a cross-client symbiotic space to facilitate effective collaboration. Within this space, an efficient hash-based semantic encoding strategy is proposed to model each local client while balancing coordinated resilience and semantic consistency. Furthermore, we derive an elaborate graph condenser based on the above strategy, which condenses original graphs with semantics and structure-preserving property, to maintain the effectiveness of condensed graph alignment for FGL. Formal theoretical analysis further reveals that HashFGL can effectively alleviate the problem of graph heterogeneity. Experimental results on three large-scale graphs, employing standard partitioning strategies and a pioneering, more realistic partitioning that we introduced, demonstrate the efficacy and scalability of HashFGL.
Yeyu Yan, Shuai Zheng, Zhenfeng Zhu et al.· IEEE Transactions on Pattern...· 0 citations
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