CVPR 2026 Papers

Vision-language models (VLMs) achieve strong multimodal performance, yet how computation is organized across populations of neurons remains poorly understood. In this work, we study VLMs through the lens of neural topology, representing each layer as a within-layer correlation graph derived from neuron-neuron co-activations. This view allows us to ask whether population-level structure is behaviorally meaningful, how it changes across modalities and depth, and whether it identifies causally influential internal components under intervention. We show that correlation topology carries recoverable behavioral signal; moreover, cross-modal structure progressively consolidates with depth around a compact set of recurrent hub neurons, whose targeted perturbation materially alters model output. Neural topology thus emerges as a meaningful intermediate scale for VLM interpretability: richer than local attribution, more tractable than full circuit recovery, and empirically tied to multimodal behavior. Code is publicly available at https://github.com/he-h/vlm-graph-probing.

Due to the heterogeneity of faces and edges in B-rep, conventional graph-based representations is incapable of establishing a unified formulation for faces and edges, thereby constraining the capabilities of B-rep generative models. We propose a B-rep Variational Graph Auto Encoding (BrepVGAE), the first variational graph autoencoder framework capable of holistically encoding and decoding boundary representations of B-rep models.Firstly, we novelly represent both geometry faces and edges as nodes in a graph representation. We then design a sparse graph autoencoder to aggregate the complete B-rep structure into a compact global latent vector. We then construct a decoder that employs set-based generation, which uses bilinear layers to reconstruct adjacency relationships, i.e., topology, with a single latent vector. Afterwards, the same decoder generates node features for all faces and edges through learnable query vectors and cross-attention mechanisms. Finally, a two-stage training strategy ensures effective coupling of geometry and topology throughout. Comprehensive experiments demonstrate that BrepVGAE significantly outperforms existing methods in reconstruction accuracy, topological validity, and generative diversity. This validates the feasibility and efficacy of decoding complete CAD geometric-topological distributions from a unified latent representation, while also offering novel insights for CAD part retrieval and feature recognition domains.

Inverse problems in imaging are ill-posed, leading to infinitely many solutions consistent with the measurements due to the non-trivial null-space of the sensing matrix. Common image priors promote solutions on the general image manifold, such as sparsity, smoothness, or score function. However, as these priors do not constrain the null-space component, they can bias the reconstruction. Thus, we aim to incorporate meaningful null-space information in the reconstruction framework. Inspired by smooth image representation on graphs, we propose Graph-Smooth Null-Space Representation (GSNR), a mechanism that imposes structure only into the invisible component. Particularly, given a graph Laplacian, we construct a null-restricted Laplacian that encodes similarity between neighboring pixels in the null-space signal, and we design a low-dimensional projection matrix from the p-smoothest spectral graph modes (lowest graph frequencies). This approach has strong theoretical and practical implications: i) improved convergence via a null-only graph regularizer, ii) better coverage, how much null-space variance is captured by p modes, and iii) high predictability, how well these modes can be inferred from the measurements. GSNR is incorporated into well-known inverse problem solvers, e.g., PnP, DIP, and diffusion solvers, in four scenarios: image deblurring, compressed sensing, demosaicing, and image super-resolution, providing consistent improvement of up to 4.3 dB over baseline formulations and up to 1 dB compared with end-to-end learned models in terms of PSNR.

Federated Graph Learning (FGL) offers a privacy-preserving paradigm for collaborative training on graph data, yet significant topological heterogeneity poses a critical threat to generalization fairness, often yielding a global model dominated by a subset of clients. This introduces two critical issues: at the global level, aggregation bias disproportionately amplifies the influence of dominant clients, while at the local level, blind optimization results in inefficient and inequitable training processes. To address these challenges, we propose FedSST, an adaptive fairness framework. FedSST introduces a fair, structure-based signal to quantify client contributions, which in turn guides fair aggregation and adaptive local training. Extensive experiments across diverse cross-domain and cross-dataset settings demonstrate that FedSST enhances generalization fairness and overall model performance, outperforming various state-of-the-art methods.

State-of-the-art whole-body pose estimators often lack robustness, producing anatomically implausible predictions in challenging scenes. We posit this failure stems from spurious correlations learned from visual context, a problem we formalize using a Structural Causal Model (SCM). The SCM identifies visual context as a confounder that creates a non-causal backdoor path, corrupting the model's reasoning. We introduce the Causal Intervention Graph Pose (CIGPose) framework to address this by approximating the true causal effect between visual evidence and pose. The core of CIGPose is a novel Causal Intervention Module: it first identifies confounded keypoint representations via predictive uncertainty and then replaces them with learned, context-invariant canonical embeddings. These deconfounded embeddings are processed by a hierarchical graph neural network that reasons over the human skeleton at both local and global semantic levels to enforce anatomical plausibility. Extensive experiments show CIGPose achieves a new state-of-the-art on COCO-WholeBody. Notably, our CIGPose-x model achieves 67.0% AP, surpassing prior methods that rely on extra training data. With the additional UBody dataset, CIGPose-x is further boosted to 67.5% AP, demonstrating superior robustness and data efficiency.

Estimating robot pose from a monocular RGB image is a challenge in robotics and computer vision. Existing methods typically build networks on top of 2D visual backbones and depend heavily on labeled data for training, which is often scarce in real-world scenarios, causing a sim-to-real gap. Moreover, these approaches reduce the 3D-based problem to 2D domain, neglecting the 3D priors. To address these, we propose Robot Topological Alignment Graph (RoboTAG), which incorporates a 3D branch to inject 3D priors while enabling co-evolution of the 2D and 3D representations, alleviating the reliance on labels. Specifically, the RoboTAG consists of a 3D branch and a 2D branch, where nodes represent the states of the camera and robot system, and edges capture the dependencies between these variables or denote alignments between them. Closed loops are then defined in the graph, on which a consistency supervision across branches can be applied. Experimental results demonstrate that our method is effective across robot types, suggesting new possibilities of alleviating the data bottleneck in robotics.

Visual navigation requires the robot to reach a specified goal such as an image, based on a sequence of first-person visual observations. While recent learning-based approaches have made significant progress, they often focus on improving policy heads or decision strategies while relying on simplistic feature encoders and temporal pooling to represent visual input. This leads to the loss of fine-grained spatial and temporal structure, ultimately limiting accurate action prediction and progress estimation. In this paper, we propose a unified spatio-temporal representation framework that enhances visual encoding for robotic navigation. Our approach extracts features from both image sequences and goal observations, and fuses them using the designed spatio-temporal fusion module. This module performs spatial graph reasoning within each frame and models temporal dynamics using a hybrid temporal shift module combined with multi-resolution difference-aware convolution. Experimental results demonstrate that our approach consistently improves navigation performance and offers a generalizable visual backbone for goal-conditioned control. Code is available at https://github.com/hren20/STRNet.

Efficient image compression relies on modeling both local and global redundancy. Most state-of-the-art (SOTA) learned image compression (LIC) methods are based on CNNs or Transformers, which are inherently rigid. Standard CNN kernels and window-based attention mechanisms impose fixed receptive fields and static connectivity patterns, which potentially couple non-redundant pixels simply due to their proximity in Euclidean space. This rigidity limits the model's ability to adaptively capture spatially varying redundancy across the image, particularly at the global level. To overcome these limitations, we propose a content-adaptive image compression framework based on Graph Neural Networks (GNNs). Specifically, our approach constructs dual-scale graphs that enable flexible, data-driven receptive fields. Furthermore, we introduce adaptive connectivity by dynamically adjusting the number of neighbors for each node based on local content complexity. These innovations empower our Graph-based Learned Image Compression (GLIC) model to effectively model diverse redundancy patterns across images, leading to more efficient and adaptive compression. Experiments demonstrate that GLIC achieves state-of-the-art performance, achieving BD-rate reductions of 19.29%, 21.69%, and 18.71% relative to VTM-9.1 on Kodak, Tecnick, and CLIC, respectively. Code will be released at https://github.com/UnoC-727/GLIC.

Few-shot learning has attracted extensive attention, with metric-based approaches such as Prototypical Networks establishing strong baselines. These methods construct class prototypes from support samples and classify query samples via distance metrics, but their performance is highly sensitive to label noise. To tackle this challenge, we propose a novel Graph Attention Prototypical Network (GAPNet) for robust few-shot classification. GAPNet first extracts local and global features via a classic CNN backbone and a group attention broad learning module, respectively. To mitigate the impact of label noise, the intra-class and inter-class relationships between support and query samples are explicitly modeled via a pseudo-label guided graph constructor, and then processed by an edge-aware graph attention module to capture topological correlations. Furthermore, an adaptive noise-robust prototype generator is introduced to dynamically suppress the contributions of noisy samples, substantially improving the reliability of class prototypes. Extensive experiments demonstrate the effectiveness and robustness of GAPNet to label noise. Compared to state-of-the-art approaches, GAPNet improves accuracy in the 5-way 5-shot setting by 3% ~ 8% on three general image benchmarks and one fine-grained classification dataset.

Learning-based structure-from-motion methods such as ACE-Zero have demonstrated strong performance in estimating camera poses and scene coordinates from unordered image collections without requiring ground truth supervision. However, the lack of global and multi-view consistency constraints in ACE-Zero can lead to pose drift and misalignment, particularly in complex or ambiguous scenes. In this work, we propose a hybrid framework that integrates pose graph optimization (PGO) into ACE-Zero to refine camera poses and suppress incorrect refinements. We construct pose graphs directly from ACE-Zero outputs by extracting relative pose constraints from predicted scene coordinates. Furthermore, we introduce an uncertainty-aware optimization strategy by estimating confidence scores using geometric priors, including epipolar and optical flow consistencies across views. Our approach improves the robustness and accuracy of pose estimation, demonstrating that global geometric reasoning can effectively complement learning-based inference in structure-from-motion.

While spatial transcriptomics (ST) has advanced our understanding of gene expression in tissue context, its high experimental cost limits its large-scale application. Predicting ST from pathology images is a promising, cost-effective alternative, but existing methods struggle to capture complex cross-slide spatial relationships. To address the challenge, we propose SpaHGC, a multi-modal heterogeneous graph-based model that captures both intra-slice and inter-slice spot-spot relationships from histology images. It integrates local spatial context within the target slide and cross-slide similarities computed from image embeddings extracted by a pathology foundation model. These embeddings enable inter-slice knowledge transfer, and SpaHGC further incorporates Masked Graph Contrastive Learning to enhance feature representation and transfer spatial gene expression knowledge from reference to target slides, enabling it to model complex spatial dependencies and significantly improve prediction accuracy.We conducted comprehensive benchmarking on seven matched histology-ST datasets from different platforms, tissues, and cancer subtypes. The results demonstrate that SpaHGC significantly outperforms the existing nine state-of-the-art methods across all evaluation metrics. Additionally, the predictions are significantly enriched in multiple cancer-related pathways, thereby highlighting its strong biological relevance and application potential. Code and data are available at https://github.com/wenwenmin/SpaHGC.

In this paper we focus on the viewing graph, which is used to represent cameras (as nodes) and their pairwise relationships (as edges) in the context of Structure from Motion. By analyzing this graph, it is possible to establish if the available pairwise relationships (e.g., fundamental matrices in the uncalibrated case) are theoretically enough to uniquely determine the cameras, in which case the graph is termed "solvable". Previous results considered calibrated and uncalibrated settings, whereas other camera models have not been explored in the context of viewing graph solvability: this work represents the first study under the affine camera model. We provide a characterization of the problem in terms of a linear system, from which we derive a practical method to check affine solvability. We complement this by some theoretical results providing sufficient/necessary conditions for affine solvability, in order to give further insights on the problem. Thanks to our experiments, we analyze synthetic graphs and real graphs coming from structure-from-motion datasets, where we focus on understanding the differences among different camera models (calibrated, uncalibrated and affine) in terms of solvability. In this context, we also raise an open research question and conjecture a possible answer, which is supported by empirical evidence.

The pose graph is a core component of Structure-from-Motion (SfM), where images act as nodes and edges encode relative poses. Since geometric verification is expensive, SfM pipelines restrict the pose graph to a sparse set of candidate edges, making initialization critical. Existing methods rely on image retrieval to connect each image to its k nearest neighbors, treating pairs independently and ignoring global consistency. We address this limitation through the concept of edge prioritization, ranking candidate edges by their utility for SfM. Our approach has three components: (1) a GNN trained with SfM-derived supervision to predict globally consistent edge reliability; (2) multi-minimal-spanning-tree-based pose graph construction guided by these ranks; and (3) connectivity-aware score modulation that reinforces weak regions and reduces graph diameter. This globally informed initialization yields more reliable and compact pose graphs, improving reconstruction accuracy in sparse and high-speed settings and outperforming SOTA retrieval methods on ambiguous scenes. The code and trained models are available at https://github.com/weitong8591/global_edge_prior.

We train a language model to generate LEGO(r)-brick build sequences. While prior work has been restricted to discrete, voxel-like towers, we consider a much broader set of pieces, encompassing thousands of part types with diverse connection semantics. To enable this, we first collect a large-scale dataset of over 100,000 human-designed LDraw brick objects and scenes. The complexity of our setting makes it challenging to autoregressively assemble structures that satisfy physical constraints. When predicting block pose directly, build sequences quickly become invalid after a small number of steps. Although pieces are placed in 3D space, it is the spatial relationships of the parts which define the whole. With this in mind, we design a graph-based program representation that parametrizes structure through connectivity, improving the physical grounding of generated sequences. To enable future applications, we make our dataset and models available for research purposes. https://kulits.github.io/BrickNet

Despite remarkable progress in text-to-image diffusion models, controlling the semantic and spatial relationships between interacting instances remains a fundamental challenge. Current methods that inject spatial constraints often fail to model the intrinsic functional dependencies between entities, leading to implausible interactions. In this paper, we introduce Semantic Derivative Flow (SDF), a novel graph-guided framework that structures the diffusion process within a directed acyclic interaction graph. Our core innovation is a theoretically-motivated derivative attention mechanism, which explicitly enforces the semantic representation of a predicate to be derived from its subject, and the object from the predicate, formalizing a differentiable semantic graph. This principled approach compels the generative process to adhere to the logical chain of interaction. We further integrate a global context node and a real-time regional refinement module to ground the graph in the visual domain holistically. Extensive experiments demonstrate that our model, an instantiation of SDF, establishes a new state-of-the-art in fidelity and controllability on the HICODet benchmark. We complement our empirical results with a theoretical analysis, framing our method as structured message passing on interaction graphs, which provides a rigorous justification for its efficacy and generalization benefits.

Detecting subtle visual anomalies in images remains challenging, particularly when only normal samples are available a priori. Such unsupervised anomaly detection is typically solved by measuring feature similarity of a query patch to a memory of normal patches. However, similarity alone does not reveal how strongly a query patch violates the structure of the normal feature manifold. We propose a training-free Laplacian graph energy optimization formulation, named ANoCo that scores Anomaly by the cost of Non-Conformity of a query patch to align with a fixed normal manifold. For each query patch, we construct a bipartite query to normal graph weighted by cosine affinity, explicitly removing query-query and normal-normal edges to prevent evidence dilution. We formulate anomaly scoring as a convex Laplacian energy with anchored normal nodes, and solve in closed form. In particular, we do not use the optimized features themselves--the anomaly score is the magnitude of the update required to satisfy normality constraints, reframing the graph Laplacian as a non-conformity operator rather than a smoothing prior. The proposed method introduces no learnable parameters, message passing, or sampling, and has complexity comparable to a single linear solve. Across standard benchmarks, it delivers strong image-level AUROC, stable localization maps, and improved robustness over prior methods, demonstrating the effectiveness of using optimization-induced feature drift as anomaly measure.

In industrial settings, classification of 3D CAD models are critical for efficient manufacturing. However, the limited availability of annotated CAD models presents an obstacle to achieving rapid adaptation in few-shot part classification scenarios. In this paper, we propose a hybrid graph representation and a pre-training and graph prompt framework for B-rep few-shot classification. Specifically, hybrid graph representation captures comprehensive and multi-level structural information of B-rep models by constructing local topology graph, global parallel graph and regional association hypergraph. A hierarchical graph network then fuses component-level structures with topological details in the hybrid graph. Reinforcement-augmented contrastive pre-training produces robust universal representations while in-place perturbation reduces training time. Structure-aware graph prompts finally produce node-specific cues, enabling few-shot B-rep part classification without heavy fine-tuning. Experiments on the TraceParts-11and FabWave-31 datasets show that our method outperforms existing general-purpose approaches. This work provides an efficient and state-of-the-art solution for few-shot B-rep part classification.

Vision-Language Models (VLMs) have emerged as versatile solutions for zero-shot question answering (QA) across various domains. However, enabling VLMs to effectively comprehend structured graphs and perform accurate, efficient QA remains challenging. Existing approaches typically rely on a single type of graph topology representation (GTR) of graphs, such as fixed-style visual images or unified text descriptions. This "one-size-fits-all" strategy often neglects model-specific and task-specific preferences, resulting in inaccurate or overly lengthy responses to graph-related queries. To address this, we propose the DynamicGTR framework, which dynamically selects the optimal GTR for each query during inference, thereby enhancing the zero-shot graph QA capabilities of VLMs with a customizable accuracy and brevity trade-off. Extensive experiments show that DynamicGTR not only improves VLM-based graph algorithm QA performance but also successfully transfers the experience trained from synthetic graph algorithm tasks to real-world applications like link prediction and node classification, without any additional training. Additionally, DynamicGTR demonstrates strong transferability across tasks, domains, and models, suggesting its potential as a flexible solution for broad graph scenarios.

We present a general optimization-based framework for depth-preserving normal integration. Unlike existing methods that operate on surface orientations defined over regular grids, our approach introduces a unified graph-based formulation capable of integrating semi-differentiable surfaces on unstructured domains. Given a set of points uniformly sampled from a surface, we construct a directed, weighted graph that jointly parameterizes the surface geometry and pairwise point correlations. Surface depth is recovered by minimizing projected point-to-plane distances across the graph, and we attain this objective through variational inference. In our formulation, estimated surface normals serve as latent variables that encode local geometry via the posterior probabilities of a two-component Gaussian mixture, allowing depth discontinuities to be explicitly inferred from sampled triplet configurations. The unknowns are estimated in an alternating fashion, and we provide a geometric interpretation of this inference process by relating it to shape deformation. Experimental results show that the proposed method not only delivers superior performance on regularly gridded data, but also generalizes effectively to scattered points, which existing approaches do not directly support.