CVPR 2026 Papers

Most existing underwater instance segmentation approaches are constrained by close-vocabulary prediction, limiting their ability to recognize novel marine categories. To support evaluation, we introduce **MARIS** (_Marine Open-Vocabulary Instance Segmentation_), the first large-scale fine-grained benchmark for underwater Open-Vocabulary (OV) Instance segmentation (UOVIS), featuring a limited set of seen categories and diverse unseen categories. Although OV instance segmentation has shown promise on natural images, our analysis reveals that transfer to underwater scenes suffers from severe visual degradation (e.g., color attenuation) and semantic misalignment caused by lack underwater class definitions. To address these issues, we propose a unified framework with two complementary components. The Geometric Prior Enhancement Module (**GPEM**) leverages stable part-level and structural cues to maintain object consistency under degraded visual conditions. The Semantic Alignment Injection Mechanism (**SAIM**) enriches language embeddings with domain-specific priors, mitigating semantic ambiguity and improving recognition of unseen categories. Experiments show that our framework consistently outperforms existing OV baselines both In-Domain and Cross-Domain setting on MARIS, establishing a strong foundation for future underwater perception research. The code of this paper can be found in Github.

Machine unlearning (MU) addresses privacy risks in pretrained models. The main goal of MU is to remove the influence of designated data while preserving the utility of retained knowledge. Achieving this goal requires preserving semantic relations among retained instances, which existing studies often overlook. We observe that without such preservation, models suffer from progressive structural collapse, undermining both the deletion-retention balance. In this work, we propose a novel structure-faithful framework that introduces stakes, i.e., semantic anchors that serve as reference points to maintain the knowledge structure. By leveraging these anchors, our framework captures and stabilizes the semantic organization of knowledge. Specifically, we instantiate the anchors from language-driven attribute descriptions encoded by a semantic encoder (e.g., CLIP). We enforce preservation of the knowledge structure via structure-aware alignment and regularization: the former aligns the organization of retained knowledge before and after unlearning around anchors, while the latter regulates updates to structure-critical parameters. Results from image classification, retrieval, and face recognition show average gains of 32.9%, 22.5%, and 19.3% in performance, balancing the deletion-retention trade-off and enhancing generalization.

Dense scenes containing numerous tiny objects pose a fundamental challenge for segmentation models, where small localization errors can significantly degrade downstream measurements. We present Structure-Aware Representation Distillation (SARD), a teacher-compatible framework that transfers structural knowledge from a large teacher to a compact student via feature-space alignment rather than mask imitation. SARD builds a structure-importance map by integrating boundary salience, geometric complexity, and local feature variation, and uses it to guide a unified representation loss that combines feature consistency with distribution alignment. This encourages the student to allocate capacity to geometrically informative regions while preserving global context. Experiments on Cityscapes, ADE20K, and a challenging rock fragmentation benchmark (RockFrag) show that SARD consistently improves both mIoU and boundary IoU over strong distillation baselines; on RockFrag, SARD improves a Swin-T student over CWD by +4.3 mIoU and +6.7 bIoU. A ResNet-50 student distilled from a Swin-L teacher achieves up to 7.7 times parameter reduction and 9 times higher throughput than the teacher, with no additional inference overhead beyond the student network, demonstrating that structure-aware representation distillation is effective and efficient for tiny-dense segmentation. Code is available at: https://github.com/liuuuuuuxuesong/SARD.

Accurate 3D tooth segmentation is fundamental for digital dentistry, orthodontic analysis, and clinical simulation. Intraoral scan (IOS) models often suffer from incomplete or unreliable texture information, making it difficult to delineate fine boundaries between teeth and gingiva, while 2D intraoral images provide rich semantic and chromatic information that can complement 3D geometry. Thus, we propose a novel Photo-guided 3D Model Tooth Segmentation framework, PMTSeg, that enhances 3D tooth segmentation by integrating texture cues from intraoral photos. Our framework introduces three key components: a Camera Alignment Module (CAM) for accurate image-model registration, a Feature Filtering Gate (FFG) for adaptive multi-view feature selection, and a Consistent Feature Learning (CFL) mechanism for learning texture-geometry correspondence. Our method supports arbitrary numbers and views of intraoral photos. Experiments show significant improvements in distinguishing adjacent teeth and tooth-gingiva boundaries, demonstrating that intraoral photographs serve as an efficient, semantically rich supplement to 3D scans for precise dental segmentation.

Continual Test-Time Adaptation (CTTA) for semantic segmentation is vital for deploying vision models in dynamic environments with persistent domain shifts. Existing methods often degrade over time as self-supervised updates amplify early prediction errors. We attribute this fragility to a geometric limitation: Euclidean feature spaces, with polynomial volume growth, lead to distorted semantic representations and crowded, unstable decision boundaries. We propose HyperProtoSeg, a hyperbolic prototypical segmentation network that learns geometrically optimal class prototypes in the Poincare ball. Leveraging the exponential expansion of hyperbolic space, it enforces large and uniform inter-class margins with low distortion, yielding well separated and curvature-stable embeddings. For robust online adaptation, we introduce Hyperbolic Boundary Consistency Adaptation (HBCA), which partitions pixels by cross-view consistency into confident "core" and uncertain "boundary" sets. HBCA applies geodesic distance minimization for confident regions and a novel Hyperbolic Directional Consistency Loss for uncertain ones, preventing error amplification. Experiments on challenging synthetic- to-real benchmarks (Cityscapes - ACDC, IDD - IDD-AW, SHIFT) show that HyperProtoSeg + HBCA achieves an average improvement of (1.94%,4.02%,1.24%) over state-of-the-art CTTA methods under severe structural shifts.

We introduce Consistent Instance Field, a continuous and probabilistic spatio-temporal representation for dynamic scene understanding.Unlike prior methods that rely on discrete tracking or view-dependent features, our approach disentangles visibility from persistent object identity by modeling each space-time point with an occupancy probability and a conditional instance distribution. To realize this, we introduce a novel instance-embedded representation based on deformable 3D Gaussians, which jointly encode radiance and semantic information and are learned directly from input RGB images and instance masks through differentiable rasterization.Furthermore, we introduce new mechanisms to calibrate per-Gaussian identities and resample Gaussians toward semantically active regions, ensuring consistent instance representations across space and time. Experiments on HyperNeRF and Neu3D datasets demonstrate that our method significantly outperforms state-of-the-art methods on novel-view panoptic segmentation and open-vocabulary 4D querying tasks.

Language-guided 3D segmentation is crucial for linking 3D perception with semantic understanding, yet it remains vulnerable to the sparse and occluded views common in real-world RGB-D data. To overcome this, we present a real-time framework that leverages 3D Gaussian Splatting (3DGS) to build a semantically continuous and differentiable embedding field from partial observations. Our approach integrates two key components: a Dirichlet Process (DP) for the adaptive discovery of novel object categories, and a gradient low-rank mechanism that enhances class separability by reducing feature redundancy. This combination enables robust open-vocabulary segmentation guided directly by text prompts. Extensive experiments on challenging benchmarks demonstrate that our method achieves strong performance, exhibiting superior accuracy, robustness to partial inputs, and a powerful capacity for novel class discovery.Our code and models are available at https://github.com/Tap12345/LangGS.

Diffusion models (DMs) can generate anatomically realistic medical images, offering a compelling route to improving generalization through synthetic augmentation. Yet high visual realism does not necessarily translate into improved downstream utility. This work addresses two key questions in diffusion-driven augmentation. First, what should be synthesized? We show that synthetic adversariality, namely the expected empirical loss induced by synthetic samples, is a key driver of generalization. More importantly, only native adversariality, arising from hard examples supported by the diffusion model distribution, yields consistent gains, whereas artificial adversariality induced by attack-based perturbations is detrimental. Second, how should such samples be synthesized? We propose the Adversariality Miner, a lightweight module that optimizes the initial noise to mine natively adversarial samples without modifying or retraining the diffusion model. Extensive experiments across diverse diffusion backbones and medical benchmarks confirm the effectiveness of our approach, establishing a principled path toward diffusion-driven generalization.

Open-vocabulary 3D semantic segmentation aims to segment arbitrary categories beyond the training set. Existing methods predominantly rely on distilling knowledge from 2D open-vocabulary models. However, aligning 3D features to the 2D representation space restricts intrinsic 3D geometric learning and inherits errors from 2D predictions. To address these limitations, we propose GeoGuide, a novel framework that leverages pretrained 3D models to integrate hierarchical geometry-semantic consistency for open-vocabulary 3D segmentation. Specifically, we introduce an Uncertainty-based Superpoint Distillation module to fuse geometric and semantic features for estimating per-point uncertainty, adaptively weighting 2D features within superpoints to suppress noise while preserving discriminative information to enhance local semantic consistency. Furthermore, our Instance-level Mask Reconstruction module leverages geometric priors to enforce semantic consistency within instances by reconstructing complete instance masks. Additionally, our Inter-Instance Relation Consistency module aligns geometric and semantic similarity matrices to calibrate cross-instance consistency for same-category objects, mitigating viewpoint-induced semantic drift. Extensive experiments on ScanNet v2, Matterport3D, and nuScenes demonstrate the superior performance of GeoGuide.

The applicability of current lesion segmentation models for chest X-rays (CXRs) has been limited both by a small number of target labels and the reliance on complex, expert-level text inputs, creating a barrier to practical use. To address these limitations, we introduce instruction-guided lesion segmentation (ILS), a medical-domain adaptation of referring image segmentation (RIS) designed to segment diverse lesion types based on simple, user-friendly instructions. Under this task, we construct MIMIC-ILS, the first large-scale instruction-answer dataset for CXR lesion segmentation, using our fully automated multimodal pipeline that generates annotations from CXR images and their corresponding reports. MIMIC-ILS contains 1.1M instruction-answer pairs derived from 192K images and 91K unique segmentation masks, covering seven major lesion types. To empirically demonstrate its utility, we present ROSALIA, a LISA model fine-tuned on the MIMIC-ILS dataset. ROSALIA can segment diverse lesions and provide textual explanations in response to user instructions. The model achieves high accuracy in our newly proposed task, highlighting the effectiveness of our pipeline and the value of MIMIC-ILS as a foundational resource for pixel-level CXR lesion grounding. The dataset and model are available at https://github.com/checkoneee/ROSALIA.

Humans perceive actions through key transitions that structure actions across multiple abstraction levels, whereas machines, relying on visual features, tend to over-segment. This highlights the difficulty of enabling hierarchical reasoning in video understanding. Interestingly, we observe that lower-level visual and high-level action latent variables evolve at different rates, with low-level visual variables changing rapidly, while high-level action variables evolve more slowly, making them easier to identify. Building on this insight, we propose the Hierarchical Action Learning (HAL) model for weakly-supervised action segmentation. Our approach introduces a hierarchical causal data generation process, where high-level latent action govern the dynamics of low-level visual features. To model these varying timescales effectively, we introduce deterministic processes to align these latent variables over time. The HAL model employs a hierarchical pyramid transformer to capture both visual features and latent variables, and a sparse transition constraint is applied to enforce the slower dynamics of high-level action variables. This mechanism enhances the identification of these latent variables over time. Under mild assumptions, we prove that these latent action variables are strictly identifiable. Experimental results on several benchmarks show that the HAL model significantly outperforms existing methods for weakly-supervised action segmentation, confirming its practical effectiveness in real-world applications.

Effectively grounding complex language to pixels in remote sensing (RS) images is a critical challenge for applications like disaster response and environmental monitoring. Current models can parse simple, single-target commands but fail when presented with complex geospatial scenarios, e.g., segmenting objects at various granularities, executing multi-target instructions, and interpreting implicit user intent. To drive progress against these failures, we present LaSeRS, the first large-scale dataset built for comprehensive training and evaluation across four critical dimensions of language-guided segmentation: hierarchical granularity, target multiplicity, reasoning requirements, and linguistic variability. By capturing these dimensions, LaSeRS moves beyond simple commands, providing a benchmark for complex geospatial reasoning. This addresses a critical gap: existing datasets oversimplify, leading to sensitivity-prone real-world models. We also propose SegEarth-R2, an MLLM architecture designed for comprehensive language-guided segmentation in RS, which directly confronts these challenges. The model's effectiveness stems from two key improvements: (1) a spatial attention supervision mechanism specifically handles the localization of small objects and their components, and (2) a flexible and efficient segmentation query mechanism that handles both single-target and multi-target scenarios. Experimental results demonstrate that our SegEarth-R2 achieves outstanding performance on LaSeRS and other benchmarks, establishing a powerful baseline for the next generation of geospatial segmentation. All data and code will be released.

High-quality segmentations are critical in vision tasks where boundary accuracy is important (e.g., medical diagnostics, quality control, etc.). Recently, promptable vision models have emerged as effective backbones for segmentation refinement frameworks. However, their performance not only hinges on prompt quality, they also must overcome noisy input masks and semantically ambiguous outputs from promptable models. Existing prompt-based refiners rely on fixed prompt rules, making them brittle to changing failure modes and new tasks or domains. We propose PromptMoE, a model-agnostic MoE-driven prompting refiner effective in segmentation refinement across tasks and domains. PromptMoE features three collaborative modules to refine an initial mask: our MoE-based Image-Informed Prompting framework (IIP) takes an image and coarse mask and produces a set of expert score maps to guide prompt generation, the Dynamic Expert Selector (DES) activates only the most relevant experts and fuses their maps to avoid dense evaluation and signal dilution, and the Prompt-Placement Explorer (PPE) explores the fused guidance map to place high-confidence spatially diverse point prompts. Across five benchmark datasets (BIG, VOC, DAVIS585, ECSSD, MSRA-B), PromptMoE achieves statistically significant gains over SOTA methods CascadePSP, SegRefiner, and SAMRefiner on semantic, instance, and salient tasks, with mean improvements of +6.24 IoU / +8.99 BIoU.

We introduce GeoSAM2, a prompt-controllable framework for 3D part segmentation that casts the task as multi-view 2D mask prediction. Given a textureless object, we render normal and point maps from predefined viewpoints and accept simple 2D prompts--clicks or boxes--to guide part selection. These prompts are processed by a shared SAM2 backbone augmented with LoRA and residual geometry fusion, enabling view-specific reasoning while preserving pretrained priors. The predicted masks are back-projected to the object, aggregated across views.Our method enables fine-grained, part-specific control without requiring text prompts, per-shape optimization, or full 3D labels. In contrast to global clustering or scale-based methods, prompts are explicit, spatially grounded, and interpretable. We achieve state-of-the-art class-agnostic performance on PartObjaverse-Tiny and PartNetE, outperforming both slow optimization-based pipelines and fast but coarse feedforward approaches. Our results highlight a new paradigm: aligning the paradigm of 3D segmentation with SAM2, leveraging interactive 2D inputs to unlock controllability and precision in object-level part understanding.

Semi-supervised learning addresses label scarcity and high annotation costs in medical image segmentation by exploiting the latent information in unlabeled data to enhance model performance. Traditional discriminative segmentation relies on segmentation masks, neglecting feature-level distribution constraints. This limits robust semantic representation learning and adaptive modeling of unlabeled data in scenarios with few labels. To address these limitations, we propose SemiGDA, a novel Generative Dual-distribution Alignment framework for semi-supervised medical image segmentation. Our SemiGDA overcomes the reliance of discriminative methods on large labeled datasets by aligning feature and semantic distributions to boost semantic learning and scene adaptability. Specifically, we propose a Dual-distribution Alignment Module (DAM), which employs two structurally distinct encoders to model image and mask feature distributions. It enforces their alignment in the latent space via distributional constraints, establishing structured feature consistency. Moreover, we design a Consistency-Driven Skip Adapter (CDSA) strategy, which introduces dual skip adapters (Image and Mask) to fuse multi-scale features via skip connections. Using a consistency loss, CDSA enhances cross-branch semantic alignment and reinforces fine-grained semantic consistency. Experimental results on diverse medical datasets show that our method outperforms other state-of-the-art semi-supervised segmentation methods. Code is released at: https://github.com/taozh2017/SemiGDA.

Coded Aperture Snapshot Spectral Imaging (CASSI) is an emerging hyperspectral image (HSI) acquisition technique for downstream semantic segmentation. Due to the ill-posedness nature of CASSI systems, typical solutions are compelled to conduct a two-stage reconstruction-then-segmentation pipeline, namely viewing them as two separate tasks. However, we observe that such two tasks are interrelated and mutually reinforcing for representation learning, and thus separating them limits the overall accuracy and efficiency. To this end, we propose the first Cooperative Reconstruction-Segmentation Deep Unfolding Network (CRSDUN) to solve the reconstruction and segmentation tasks in parallel. To make the two mutually reinforcing, we introduce the Cross-Aggregated Super-Token Attention (CASTA) mechanism to enhance the representation interactions between HSI reconstruction and semantic segmentation. Extensive experiments on both synthetic and real-world HSI reconstruction-segmentation datasets demonstrate that our method achieves state-of-the-art in both spectral reconstruction and semantic segmentation. The code is available at \href https://github.com/zjhe02/CRSDUN https://github.com/zjhe02/CRSDUN .

Transformer-based approaches have recently become the dominant paradigm for 3D instance segmentation. These methods typically employ a multi-layer decoder that iteratively refines a set of learnable queries into instance mask predictions. However, we observe that multiple queries often target the same instance simultaneously, leading to fragmented masks for a single object. We define this phenomenon as inter-query competition, which slows convergence and limits segmentation accuracy. To address this problem, we present CompetitorFormer, a novel framework designed for Transformer-based methods. Our method mitigates inter-query competition by explicitly modeling the competitive relationships among queries. Specifically, we introduce a Query Competition Layer before each decoder stage to construct a dynamic competitive landscape, allowing each query to perceive its relative importance. In addition, the proposed Relative Relationship Encoding and Rank Cross-Attention modules enhance both self-attention and cross-attention by prioritizing dominant queries. Extensive experiments show that our approach converges faster and achieves superior performance on the ScanNetV2, ScanNet++V2, ScanNet200, and S3DIS datasets. Code is available at https://github.com/DuanchuWang/CompetitorFormer.

Foundation models for medical image segmentation struggle under out-of-distribution (OOD) shifts, often producing fragmented false positives on OOD tumors. We introduce **R^2-Seg**, a **training-free** framework for robust OOD tumor segmentation that operates via a two-stage **Reason-and-Reject** process. First, the **Reason** step employs an LLM-guided anatomical reasoning planner to localize organ anchors and generate multi-scale ROIs. Second, the **Reject** step applies two-sample statistical testing to candidates generated by a frozen foundation model (BiomedParse) within these ROIs. This statistical rejection filter retains only candidates significantly different from normal tissue, effectively suppressing false positives. Our framework requires no parameter updates, making it compatible with zero-update test-time augmentation and avoiding catastrophic forgetting. On multi-center and multi-modal tumor segmentation benchmarks, **R^2-Seg** substantially improves Dice, specificity, and sensitivity over strong baselines and the original foundation models.

Underwater instance segmentation is essential for fine-grained scene understanding. However, underwater imagery exhibits a strong domain gap from in-air vision due to severe degradation (e.g., turbidity). Consequently, despite its general segmentation ability, SAM degrades sharply underwater. In this work, we propose BiPA, which effectively adapts SAM to the underwater domain. To be concrete, we construct an underwater SAM with dual prompts and introduce a foreground-attentive injection block to enhance local foreground representation. We formulate dense prompt learning as a bilevel optimization, explicitly capturing the mutual dependency between prompt and model. To make this tractable, we design a two-stage learning strategy. The first stage adapts the dense prompt itself, updating it with Bayesian optimization to learn efficiently. The second stage fine-tunes the model parameters under the frozen optimized prompt, which finally enables effective cross-domain adaptation. Extensive experiments and analyses verify the superiority and efficiency of BiPA. The code is publicly available at https://github.com/ZeAstra/BiPA.