Unified segmentation aims to consolidate multiple vision tasks into a single model, yet faces two core challenges: learning robust homogeneous features (e.g., shared low- and mid-level cues) to enable cross-domain knowledge transfer, while disentangling heterogeneous features (e.g., task-specific semantic objectives) to avoid negative transfer and preserve task independence. To address these challenges, we propose the Homo- and Heterogeneous Network (H2Net), a unified framework that jointly models shared homogeneous representations and task-specific heterogeneous features. Specifically, H2Net incorporates a Cross-Modal Structure Enhancement Module (CSEM), which integrates auxiliary depth priors via joint frequency–spatial cross-modal attention to strengthen task-agnostic structural representations. In addition, a Task Adapter Pool (TAP) is introduced to model task-specific heterogeneous features by assigning dedicated adapters to individual tasks, enabling task-aware feature modulation and semantic disentanglement within a shared backbone. Extensive experiments on benchmarks spanning eight tasks demonstrate the effectiveness of the proposed approach and its superior performance. Code and results will be available at https://h2net-ijcai26.github.io.
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Multi-modality MRI lesion segmentation leverages complementary contrasts across modalities to better characterize lesion morphology and tissue alterations, thereby improving lesion delineation and reducing misdiagnosis. Existing correlation-driven methods estimate fusion weights by converting cross-modality correlation scores into adaptive fusion weights. However, in multi-center settings, domain shift changes cross-modal correlation patterns, which breaks the learned correlation-to-weight mapping and leads to modality-weight bias that amplifies center-specific shortcuts. To address this issue, we propose an Intervention-based Weight Calibration Network (IWCNet) that models domain shift as a controllable embedding and leverages explicit interventions to mitigate its impact on fusion weight estimation. IWCNet constructs original and intervened branches by performing interventions on the domain embedding, measuring inter-branch prediction effects to calibrate fusion weights. IWCNet further introduces Operator-level Domain Modulation (OLDM) to construct structure-consistent domain-variant feature pairs via operator-level domain intervention, and Effect-Driven Consistency Calibration (EDCC) to jointly leverage intervention effects and cross-branch prediction consistency to calibrate weights. Extensive experiments on multi-center multi-modality MRI datasets demonstrate that IWCNet substantially improves segmentation accuracy and robustness under domain shift.
Estimating the 6D pose of novel objects without CAD models or video sequences remains a challenging problem. Recent works explore text-driven approaches to address this challenge in an open-vocabulary manner. However, these methods typically treat text embeddings as static priors, which lack the flexibility to adapt to specific visual scenes, thereby limiting reliable cross-view correspondences. In this paper, we propose a framework that modulates static textual embeddings into adaptable semantic guidance for RGB-D open-vocabulary 6D pose estimation. Specifically, we introduce a Text Feature Modulation (TFM) module that uses learnable queries to transform fixed text embeddings into adaptive semantic representations. These representations are explicitly calibrated by a Foreground-Grounded Semantic Alignment (FGSA) strategy, which suppresses background distractions via contrastive learning. Furthermore, we design a Semantic-Anchored Cross-view Fusion (SACF) module that aggregates image features from the reference and query views using modulated semantics as an invariant anchor. Finally, we predict object segmentation and establish semantic-aware correspondences, leveraging modulated textual semantics to filter mismatches for robust 6D pose estimation. Extensive experiments on standard benchmarks demonstrate that our method achieves superior performance.
Transformer-based architectures have demonstrated strong performance in 3D point cloud understanding, yet many existing methods generate attention weights mainly from semantic feature similarity. In deep networks, feature-centric attention may become less selective as point features are progressively smoothed, a behavior associated with feature homogenization and rank collapse, which can weaken the structural discrimination of local aggregation.We propose PointGP, a geometry-primed framework that uses rectified local geometric topology as the primary cue for attention generation. PointGP introduces a Semantic-Guided Manifold Rectifier to predict feature-conditioned local coordinate offsets, and a Dual-Stream Geometric Kernel to compute attention logits from both raw and rectified geometric cues. By reducing reliance on explicit query-key feature matching while implicitly incorporating semantic guidance through geometric rectification, PointGP provides an effective and efficient mechanism for local point aggregation.Experiments across five benchmarks covering classification, part segmentation, and indoor scene segmentation show that PointGP achieves competitive accuracy with strong parameter and computational efficiency compared with representative strong baselines.
Masked autoencoder have demonstrated excellent performance in point cloud representation learning and received widespread attention. However, due to the unstructured nature of point clouds, existing methods struggle to effectively model both local geometric information and global topological features, while generally neglecting positional information, resulting in insufficient model representational capacity. To address these challenges, we propose a Position-Assigned Graph Masked Autoencoder (PA-GMAE) framework for point cloud representation learning. Specifically, we first explicitly transform the unstructured point clouds into a graph structure and assigns it positional information, leveraging the structured properties of graph to mine the intrinsic correlations among points. During the encoding stage, global contextual are dynamically injected into node features, endowing the model with capabilities of global awareness. In the decoding stage, collaborative work across multiple decoding tasks fully captures the global topology and positional dependencies of the point cloud, enhancing the quality of learned representations. Extensive experiments are conducted on three authoritative benchmark datasets, namely ScanObjectNN, ModelNet40, and ShapeNetPart, across typical downstream tasks including point cloud classification and part segmentation, the results demonstrate the effectiveness and superiority of the proposed framework.
Visual grounding aims to localize target objects based on natural language descriptions, and the core challenge lies in the cross-modal gap, which is partly caused by the significant differences in semantic structure between language and vision. Existing methods typically rely on holistic sentence-level semantic representations to modulate visual features, while overlooking the inherent structure of textual prompts. In this work, we propose a Syntactic Structure-guided Visual Grounding framework, referred to as SSVG. Specifically, to inject syntactic priors into unstructured visual representations, we design a semantic structure-based feature refinement module to adaptively modulate subject-centric and contextual visual features. To perform cross-modal alignment, we further incorporate a visual semantic consistency verification module, which leverages a subject-aware contrastive learning strategy to constrain and verify the semantic correspondence between the visual prediction and subject-level textual representation, thereby enhancing the model's robustness against semantically similar distractors. Extensive experiments demonstrate that our approach consistently outperforms state-of-the-art methods, and detailed analyses verify the effectiveness of each component.
Intracranial aneurysms are life-threatening and require accurate, timely detection. Traditional manual diagnosis by radiologists can be subjective, leading to misdiagnoses, while existing deep learning approaches struggle with small aneurysms or cases complicated by surrounding tissues. In this paper, we present the Multi-View Refinement Network (MVRNet), a framework that delivers clinically meaningful performance gains on the most challenging intracranial aneurysm cases. It directly addresses real-world diagnostic difficulties, particularly for small, obscured, or ambiguously bounded lesions that conventional methods often miss. In particular, we propose a feature enhancement technique to obtain boundary-clear CTA, an informative supplement to the bone-free CTA, to further reduce the impact of noise and enhance the robustness of IA recognition. Moreover, we develop a multi-view encoder with 2D slicing in different directions to mitigate tissue occlusion effects, coupled with a progressively refined decoder that iteratively corrects uncertain predictions, ensuring precise localization and segmentation. Experimental results indicate that MVRNet improves the F1-score by 24% and the IoU by 16% in comparison with the state-of-the-art methods. Remarkably, previous methods struggled with challenging cases, while our method still achieves satisfactory results, demonstrating a >2× improvement over prior arts on challenging test sets. Our code has been released in https://github.com/TheResearchWorks/MVRNet.
Recently, diffusion models have been applied to reformulate the regressive portrait matting task as a gradual denoising-driven generative process, alleviating the inherent limitations of discriminative models via iterative error correction. However, due to the lack of high-quality ground-truth mattes, existing generative methods for portrait matting still suffer from severe detail degradation, especially in structurally delicate hair strands and pixels with ambiguous colors and context. To address these challenges, we propose OrienDiffusion, a diffusion-based framework tailored for fine-grained portrait matting. Specifically, we construct a Hair Orientation Field to conditionally guide the denoising process, constraining the generation toward authentic structural consistency in hair regions. Furthermore, instead of relying on overly encoded deep features or ambiguous original color cues as existing methods do, we develop a Pixel Color-anchored Local Structure Embedding approach, which models alpha matte transitions as local structural variations, achieving accurate alpha estimation in pixels with ambiguous colors or context. Extensive experiments on multiple portrait matting datasets demonstrate that OrienDiffusion achieves state-of-the-art performance in these matting challenging regions. Code is available at https://github.com/xtz0001/OrienDiffusion.
Unsupervised Domain Adaptation for Semantic Segmentation (UDA-SS) has seen significant progress in recent years. Existing UDA-SS approaches mostly adopt a pseudo-labeling schema to adapt model in the target domain, but they often overlook the inherent long-tailed data distribution in segmentation. We find that such scarce tail samples can lead to representation collapse for tail classes and further hinder the quality of pseudo-labels. To address these challenges, we propose MarCon, a framework that mitigates the long-tailed problem from both feature representation and pseudo-labeling perspectives. Specifically, to explicitly learn a Maximum-Margin Distribution, we derive a reformulated pixel-level contrastive learning objective by modeling feature distributions with the von Mises-Fisher (vMF) distribution. It enforces strict margins to enhance intra-class compactness and inter-class separability, preventing tail classes from being overwhelmed by head classes. Furthermore, to mitigate label noise, we introduce a Reliability-aware Filter (RaF) based on the vMF-derived metrics, which performs adaptive class-wise pixel reliability assessment to identify unreliable pixels and attenuate their contribution to model training, thereby mitigating confirmation bias. Extensive experiments on GTA → Cityscapes and SYNTHIA → Cityscapes demonstrate that MarCon consistently outperforms current leading methods across various transformer-based architectures.
The Segment Anything Model (SAM) provides a new paradigm for visual perception tasks through large-scale pre-training and interactive prompts. However, the quality of mask generation is limited by the difficulty of aligning the optimization direction between the backbone and the prompt, which is especially significant in non-natural domain images with low contrast and indistinct semantic edge structure features, especially in SAR images. Therefore, this paper proposes a coupling optimization mechanism that integrates the originally independent prompt generation process with the backbone into a closed loop through the Point Impact Decomposition (PID) module to guide the iterative optimization of the prompt. Specifically, this paper introduces the concept of PID in the decoder to quantify the contribution of prompt points to the masks, constructing an interpretable reward variable to drive the reinforcement learning prompt optimizer. This optimizer designs a reward function based on hypothesis testing ideas, achieving iterative updates of prompt points, and providing feedback on the effects of mask generation based on prompt points. Experiments show that this mechanism can provide a prompt that fits image features; its performance exceeds the existing baseline, which effectively improves the generalization ability of SAM on SAR images.Code is available at https://github.com/Fm336/PID-SAM.
Coarse-grained time series (CGTS) are critical for business and macroeconomic analysis. However, CGTS are typically updated infrequently and contain few observations, so model-centric training on raw data is prone to overfitting and degraded forecast accuracy. To address this, we propose SI-CVAE, a scale-invariant conditional generative model built around variable-length subsequences, and adopt a data-centric "train on synthetic, test on real" paradigm to enhance downstream forecasting. Specifically, we first introduce a variable-length subsequence clustering-and-matching algorithm to capture cross-scale recurring patterns within a series. We then design a frequency-domain conditional VAE with a frequency-domain linear decoder to enable controllable, arbitrary-length sequence synthesis while enforcing scale-invariance constraints. Finally, we develop a time-domain reconstruction strategy with subsequence-conditioned fusion to ensure temporal continuity and spectral consistency across concatenated segments. We conduct extensive experiments on six ship-sales datasets and four U.S. macroeconomic indicators. Results show that replacing or augmenting the original training set with SI-CVAE–generated data yields consistent accuracy gains across multiple forecasting baselines, and that SI-CVAE attains higher synthetic-data quality than state-of-the-art generators on standard metrics.
Unsupervised 3D semantic segmentation is vital for label-free open-world perception. However, current methods typically struggle with two core limitations: fixed category assumptions that restrict adaptability in complex scenes, and geometric incompleteness caused by irregular point sampling, which degrade the integrity of local geometric descriptors for fine-grained or long-tail objects. To address these, we propose DyG-Seg, a Dynamic Geometric-Semantic Alignment framework. First, targeting the challenge of incomplete geometry, the Geometric Manifold Rectification (GMR) leverages a diffusion model to reconstruct integral object geometry and recover the underlying manifold topology, thereby enhancing feature discriminability. Utilizing this geometric prior, the Dynamic Prototype Contrastive Clustering (DPCC) applies non-parametric Bayesian inference to automatically estimate the optimal number of clusters and generate pseudo-labels. Finally, our Iterative Optimization with Dynamic Constraints integrates Dynamic Class Balancing (DCB) to mitigate long-tail bias. Capitalizing on the recovered manifold topology, we enforce Spatial Geometric Consistency to rectify structural incompleteness and ensure spatially coherent semantic predictions. Extensive experiments on ScanNet and S3DIS demonstrate that DyG-Seg discovers latent semantic structures and significantly outperforms state-of-the-art unsupervised methods.
Discovering shapelets -- i.e., discriminative temporal patterns within time series -- has been widely studied to address the inherent complexity of time-series classification (TSC) and to make model decision-making processes more transparent. However, existing methods primarily focus on population-level shapelets optimized across the entire dataset, which leads to two fundamental limitations: (i) population-level patterns often misalign with instance-specific features, resulting in suboptimal performance and potentially misleading interpretations, and (ii) most methods treat shapelets as independent entities, overlooking important temporal dependencies and interactions among multiple patterns. To address these limitations, we propose INSHAPE, an interpretable TSC framework that discovers variable-length, discriminative temporal patterns specific to each time series. INSHAPE identifies these patterns as non-overlapping segments and models their temporal dependencies, thereby providing clear instance-level interpretations while achieving strong predictive performance. Furthermore, INSHAPE bridges local and global interpretability through a bottom-up approach, aggregating instance-level shapelets into prototypical (population-level) shapelets. Extensive experiments on 128 UCR and 30 UEA benchmark datasets show that INSHAPE consistently outperforms state-of-the-art shapelet-based methods while providing more intuitive and interpretable insights.
Domain Generalization (DG) for medical image segmentation is both highly challenging and critically important. However, existing medical DG methods largely overlook the issue of Catastrophic Forgetting (CF): Models often sacrifice their ability to retain source-domain knowledge while pursuing cross-domain robustness. This can directly threaten diagnostic safety in already-deployed clinical scenarios. To address this, we investigate data augmentation strategies and catastrophic forgetting for medical image DG segmentation. First, we propose a structure-guided style diffusion augmentation method. Constrained by anatomical structure consistency in the frequency domain, this method performs cross-domain diffusion on the amplitude spectrum, generating samples with more diverse and broader style coverage to better support domain generalization. Then, we design a collaborative learning network with a dual-branch interactive architecture (CoDG-Net), together with a novel learning bias-guided strategy that adaptively regulates knowledge transfer at both the layer level and the task level, thereby effectively mitigating catastrophic forgetting on the source domain. Experiments and ablation studies on single-source and multi-source medical DG benchmark datasets demonstrate that CoDG-Net not only outperforms existing state-of-the-art methods in target-domain segmentation performance, but also achieves a lower forgetting rate on the source-domain data. The code is available at: https://github.com/wangprocess/CoDG-Net.
Trajectory generation is a pivotal technique for mitigating data sparsity, but existing methods struggle to simultaneously achieve strict road network alignment and capture realistic movement characteristics. To bridge this gap, we propose RNTrajGen, a two-stage fine-grained trajectory generation framework constrained by road networks. Specifically, we first develop a Road Network Knowledge-Enhanced Encoder (RNKEE) to provide semantically rich representations for trajectory generation. By leveraging graph attention networks, RNKEE encodes static prior knowledge of the road network while integrating self-supervised learning to extract latent movement patterns from real-world trajectories. Subsequently, RNTrajGen follows a two-stage generation strategy: it first generates a sequence of road segments to ensure strict topological alignment, and then infers the moving ratios of trajectory points along road segments to capture fine-grained movement characteristics. Extensive experiments on two real-world datasets demonstrate that RNTrajGen significantly outperforms state-of-the-art baselines, and the generated trajectories exhibit high utility in downstream prediction tasks. Our code is available at https://github.com/jkzh986/RNTrajGen.
Camouflaged Object Detection (COD) aims to segment objects that are hidden within complex backgrounds. Due to the low visual contrast of camouflaged objects, annotations are costly, motivating unsupervised COD (UCOD) to eliminate labeling expenses. Most UCOD methods follow the “MLLMs + other foundation models + SAM” paradigm, which relies on spatial interactions that are unreliable in camouflaged scenarios, leading to fundamental performance bottlenecks. In this paper, we propose a novel UCOD framework that leverages SAM3 through category-level interaction with MLLMs, bypassing unreliable spatial prompts. To address SAM3’s sensitivity to category granularity, we introduce Fine-grained Category Query, guiding MLLMs to generate full-granularity category chains for robust category prompting. To mitigate suboptimal segmentation caused by high camouflage and background confusion, we propose Semantic–Geometric Dual Confirmation, which jointly validates segmentation masks from semantic and spatial perspectives. Furthermore, we introduce Semantic–Geometric Reasoning Injection, which injects critical semantic and geometric cues into MLLMs to refine category reasoning and progressively correct segmentation errors under extreme camouflage or MLLM hallucinations. Extensive experiments show that our method significantly outperforms existing UCOD approaches and achieves performance comparable to weakly supervised COD.
Zinc-based alloys are indispensable emerging absorbable metallic biomaterials, and their macroscopic performance is governed by microstructural characteristics. Intermediate phases—key microstructural constituents—are pivotal in regulating mechanical and functional properties. However, intermediate phase segmentation in zinc alloy microstructures faces formidable challenges: scarce annotated datasets, low contrast, difficulty detecting small targets, and heterogeneous morphologies. To this end, we construct IPSM-Bench, the largest high-quality dataset for zinc-alloy intermediate phase segmentation. Furthermore, we propose SCoP-SAM, a new Spatial Context Prior-guided SAM method that leverages the gradient structure and grayscale properties of intermediate phases to capture spatial context priors and incorporates them into the entire SAM encoding-decoding process, improving segmentation performance. Based on the proposed IPSM-Bench, we establish a new benchmark for intermediate phase segmentation to systematically evaluate state-of-the-art (SOTA) methods and advance research on zinc alloy microstructure analysis. Extensive experiments on IPSM-Bench and additional public alloy benchmarks demonstrate that our SCoP-SAM not only achieves SOTA performance for zinc-alloy intermediate phase segmentation but also generalizes remarkably well to other alloy scenarios.
Medical image segmentation is still challenging, especially in semi-supervised scenarios where limited annotations are expected to support both accurate boundary delineation and coherent anatomical structures. We propose LR-GCF, a Local-Rotation-Driven Global Consistency Framework that couples strong local geometric perturbations with global consistency regularization in a dual-view architecture. Given an image, LR-GCF transforms it to different variants through patch-wise jigsaw-rotated perturbations. Such variants are decoded using parallel Swin Transformer decoders and perform consistency regularization by virtue of cross pseudo supervision. A rotation prediction head is designed to predict the rotation of each local patch. This makes locally discriminative representations robust to rotations and helps the model recognize global anatomical structure under strong perturbations. To further stabilize global modeling, we introduce a rotation-aligned attention consistency loss that encourages the inter-patch correlation across different variants to be as similar as possible. Moreover, we design a Position Encoding Duplicating scheme to propagate positional encodings from the low-resolution token map to high-resolution token maps, which helps alleviate the misalignment problem in rotation-aligned attention consistency. Extensive experiments on multiple medical image segmentation benchmarks demonstrate that LR-GCF consistently achieves superior performance under limited supervision, by jointly optimizing local detail extraction and stable global anatomical modeling. Source code is available at https://github.com/shuaiaihang/LR-GCF.
Point cloud completion seeks to recover geometrically consistent shapes from partial or sparse 3D observations. Although recent methods have achieved reasonable global shape reconstruction, they often rely on Euclidean proximity and overlook the intrinsic nonlinear geometric structure of point clouds, resulting in suboptimal geometric consistency and semantic ambiguity. In this paper, we present a manifold-aware point cloud completion framework that explicitly incorporates nonlinear geometry information throughout the feature learning pipeline. Our approach introduces two key modules: a Geodesic Distance Approximator (GDA), which estimates geodesic distances between points to capture the latent manifold topology, and a Manifold-Aware Feature Extractor (MAFE), which utilizes geodesic-based k-NN groupings and a geodesic-relational attention mechanism to guide the hierarchical feature extraction process. By integrating geodesic-aware relational attention, our method promotes semantic coherence and structural fidelity in the reconstructed point clouds. Extensive experiments on benchmark datasets demonstrate that our approach consistently outperforms state-of-the-art methods in reconstruction quality. The code has been available online https://anonymous.4open.science/r/Manifold-Aware-Point-Cloud-Completion--F380/README.md.
Microscopic road-network weights represent fine-grained, time-varying traffic conditions obtained from individual vehicles. An example is travel speeds associated with road segments as vehicles traverse them. These weights support tasks including traffic microsimulation and vehicle routing with reliability guarantees. We study the problem of time-varying microscopic weight completion. During a time slot, the available weights typically cover only some road segments. Weight completion recovers distributions for the weights of every road segment at the current time slot. This problem involves two challenges: (i) contending with two layers of sparsity, where weights are missing at both the network layer (many road segments lack weights) and the segment layer (a segment may have insufficient weights to enable accurate distribution estimation); and (ii) achieving a weight distribution representation that is closed-form and can capture complex conditions flexibly, including heavy tails and multiple clusters. To address these challenges, we propose DiSGMM that combines sparsity-aware embeddings with spatiotemporal modeling to leverage sparse known weights alongside learned segment properties and long-range correlations for distribution estimation. DiSGMM represents distributions of microscopic weights as learnable Gaussian mixture models, providing closed-form distributions capable of capturing complex conditions flexibly. Experiments on two real-world datasets show that DiSGMM can outperform state-of-the-art methods.