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首次观测 · 记录 #1CVF · CVPR 2026:cvf:3764b29233ddf35a4e498b8d5058d65dCVF · CVPR 2026

Mamba Learns in Context: Structure-Aware Domain Generalization for Multi-Task Point Cloud Understanding

Jiang, Jincen, Zhou, Qianyu, Li, Yuhang, Su, Kui, Wang, Meili, Chang, Jian, Zhang, Jian Jun, Lu, Xuequan

原文摘要

While recent Transformer and Mamba architectures have advanced point cloud representation learning, they are typically developed for single-task or single-domain settings. Directly applying them to multi-task domain generalization (DG) leads to degraded performance. Transformers effectively model global dependencies but suffer from quadratic attention cost and lack explicit structural ordering, whereas Mamba offers linear-time recurrence yet often depends on coordinate-driven serialization, which is sensitive to viewpoint changes and missing regions, causing structural drift and unstable sequential modeling. In this paper, we propose Structure-Aware Domain Generalization (SADG), a Mamba-based In-Context Learning framework that preserves structural hierarchy across domains and tasks. We design structure-aware serialization (SAS) that generates transformation-invariant sequences using centroid-based topology and geodesic curvature continuity. We further devise hierarchical domain-aware modeling (HDM) that stabilizes cross-domain reasoning by consolidating intra-domain structure and fusing inter-domain relations. At test time, we introduce a lightweight spectral graph alignment (SGA) that shifts target features toward source prototypes in the spectral domain without updating model parameters, ensuring structure-preserving test-time feature shifting. In addition, we introduce MP3DObject, a real-scan object dataset for multi-task DG evaluation. Comprehensive experiments demonstrate that the proposed approach improves structural fidelity and consistently outperforms state-of-the-art methods across multiple tasks including reconstruction, denoising, and registration.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:a4084d8aeca3875dab25e4ff64553925CVF · CVPR 2026

IEBGL:An Interpretability-Enhanced Brain Graph Learning Framework with LLM-Instructed Topology and Literature-Augmented Semantics

Duan, Yihang, Huang, Shuo, Zhang, Li, Wang, Meiling, Zhang, Li

原文摘要

Resting-state functional MRI (rs-fMRI) provides rich information for modeling brain connectivity in disease diagnosis. However, most existing brain graph learning methods rely solely on imaging data, leading to limited biological interpretability and poor integration of external medical knowledge. To address these challenges, we propose an Interpretability-Enhanced Brain Graph Learning (IEBGL) framework that anchors brain network modeling in large-scale medical knowledge. Our framework introduces two complementary modules: LLM-Instructed Topological Reconstruction (LITR) and Literature-Augmented Semantic Aggregation (LASA). LITR employs large language model (LLM) reasoning to refine brain connectivity and construct topological structure. LASA augments node representations by aggregating semantic information from biomedical literature, ensuring the model's interpretability and relevance to clinical disease knowledge. Finally, the framework is trained with the Graph Bi-directional Mamba Network (GBMN) for disease diagnosis. Extensive experiments on the REST-meta-MDD and ABIDE datasets, together with 35,133 depression-related and 32,617 autism-related publications, demonstrate that IEBGL outperforms state-of-the-art methods in classification performance. Further analyses show that the LITR module reveals biologically meaningful alterations in brain connectivity, while the LASA module establishes interpretable associations between these regions and disease-related biomedical literature. Together, these mechanisms help IEBGL explain abnormal brain connections and their links to disease-related knowledge.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:c96ff6a0f719e9e40177dd6aec930b9cCVF · CVPR 2026

Learning a Unified Latent Action Space from Videos with Action-centric Cycle Consistency

Chen, Guangyan, Shao, Qi, Cui, Te, Zhou, Zichen, Mao, Weixin, Yang, Luojie, Wang, Meiling, Yang, Yi 等 10 位作者

原文摘要

Video data provides a rich source beyond expensive action-labeled data for advancing robot learning. Recent approaches have demonstrated promising potential in leveraging video data by learning latent actions for policy training. The latent action tokenizer encodes latent actions between successive video frames, and the tokenizer is trained to reconstruct future frames using current frames and the encoded latent actions. However, the unique pairing of successive frames permits future frame reconstruction with little understanding of transition dynamics, hindering the learning of semantically consistent latent actions. Moreover, the tokenizer typically allocates distinct latent action subsets to individual embodiments to accommodate heterogeneous morphologies, constraining knowledge transfer. To overcome such limitations, we propose the action-centric cycle consistency, aiming to establish a unified latent action space. Our method samples latent actions from the latent action space and decodes them with video frames to generate diverse subsequent frames, then enforces cycle consistency by predicting the sampled actions from both original and generated frames. Our concise method creates a challenging task that learns corresponding latent actions from current frames and diverse generated future frames, compelling the tokenizer to develop semantically consistent action representations. Additionally, sampled latent actions can be applied to video frames from distinct embodiments, facilitating the alignment of latent actions across embodiments. Experiments demonstrate that our approach achieves a 20.1% improvement over OpenVLA on the LIBERO benchmark and increases the average length from 3.27 to 3.93 on the CALVIN benchmark. In real-world experiments, our method maintains strong performance with a 44% improvement.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:df8d67bdf9d20bf70a797e04645562a1CVF · CVPR 2026

Advancing Cancer Prognosis with Hierarchical Fusion of Genomic, Proteomic and Pathology Imaging Data from a Systems Biology Perspective

Zhou, Junjie, Xue, Bao, Wang, Meiling, Shao, Wei, Zhang, Daoqiang

原文摘要

To enhance the precision of cancer prognosis, recent research has increasingly focused on multimodal survival methods by integrating genomic data and histology images. However, current approaches overlook the fact that the proteome serves as an intermediate layer bridging genomic alterations and histopathological features while providing complementary biological information essential for survival prediction. This biological reality exposes another architectural limitation: existing integrative analysis studies fuse these heterogeneous data sources in a flat manner that fails to capture their inherent biological hierarchy. To address these limitations, we propose HFGPI, a hierarchical fusion framework that models the biological progression from genes to proteins to histology images from a systems biology perspective. Specifically, we introduce Molecular Tokenizer, a molecular encoding strategy that integrates identity embeddings with expression profiles to construct biologically informed representations for genes and proteins. We then develop Gene-Regulated Protein Fusion (GRPF), which employs graph-aware cross-attention with structure-preserving alignment to explicitly model gene-protein regulatory relationships and generate gene-regulated protein representations. Additionally, we propose Protein-Guided Hypergraph Learning (PGHL), which establishes associations between proteins and image patches, leveraging hypergraph convolution to capture higher-order protein-morphology relationships. The final features are progressively fused across hierarchical layers to achieve precise survival outcome prediction. Extensive experiments on five benchmark datasets demonstrate the superiority of HFGPI over state-of-the-art methods.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗