CVPR 2026 论文列表

Bamboo slips are essential media for recording ancient East Asian civilizations, but excavated slips often suffer severe deformation due to dehydration and stress effects, creating substantial challenges for restoration. Traditional manual restoration is time-consuming and risks damage, while existing generative models struggle with the complex non-linear deformations in bamboo materials.We propose a novel framework for inverse restoration of deformed bamboo slips that provides a progressive physical deformation modeling with stepwise inverse displacement prediction. Our approach establishes a computable mathematical model of deformation based on wood fiber microstructure and stress-diffusion coupling effects, enabling the forward process to simulate physically plausible deformation trajectories as a deterministic, physics-driven progressive evolution. The inverse process transforms from predicting abstract noise to learning physically meaningful inverse displacement fields that progressively restore deformations.Experimental results show substantial gains in restoration fidelity while preserving delicate textual features, enabling the reliable correction of complex non-linear deformations that defeat traditional techniques. By integrating physical insights into bamboo material behavior with progressive restoration modeling, this work establishes a new paradigm for digital archaeological restoration--one that holds significant potential to transform how deformed cultural relics are reconstructed and studied. Code is available at https://github.com/VillanelleQQ/PGDR-BambooSlips

Accurate focus quality assessment (FQA) in fluorescence microscopy is challenging due to stain-dependent optical variations that induce heterogeneous focus behavior across images. Existing methods, however, treat focus quality as a stain-agnostic problem, assuming a shared global ordering. We formulate stain-aware FQA for fluorescence microscopy, showing that focus-rank relationships vary substantially across stains due to stain-dependent imaging characteristics and invalidate this assumption. To support this formulation, we introduce FluoMix, the first dataset for stain-aware FQA spanning multiple tissues, fluorescent stains, and focus levels. We further propose FluoCLIP, a two-stage vision-language framework that grounds stain semantics and enables stain-conditioned ordinal reasoning for focus prediction, effectively decoupling stain representation from ordinal structure. By explicitly modeling stain-dependent focus behavior, FluoCLIP consistently outperforms both conventional FQA methods and recent vision-language baselines, demonstrating strong generalization across diverse fluorescence microscopy conditions. Code and dataset are publicly available at https://fluoclip.github.io/.

Recent Vision-Language-Action (VLA) models map visual-textual inputs to robotic actions via end-to-end architectures, yet this approach entangles visual understanding with task-specific actions. This leads to an exhaustive collection of full operational sequences and parameter redundancy across tasks, while generic third-person camera setups require fine-tuning for different hardware due to implicit hand-eye assumptions. We argue that decoupling how robots see from how robots act is a missing primitive in VLA systems. We present EgoRoC, a plug-and-play egocentric alignment head that precedes any task policy and exposes only a thin 6-DoF pose interface. EgoRoC establishes task-agnostic viewpoint consistency from a wrist-mounted (first-person) camera and then alternates alignment with manipulation, while a diffusion-based online hand-eye module corrects the action in the end-effector frame for hardware-agnostic deployment. Trained once from static wrist-target image pairs with relative poses, rather than full manipulation trajectories, EgoRoC leaves downstream VLAs unchanged. By turning egocentric alignment into a reusable capability, EgoRoC reduces training redundancy, strengthens zero-shot cross-scene transfer, and scales across VLA backbones without manual calibration. Across simulation and real settings, attaching EgoRoC consistently boosts success rates, especially on long-horizon and out-of-distribution tasks, and improves data efficiency during fine-tuning.

Recent advances in text-to-image (T2I) generation have greatly improved visual quality, yet producing images that appear visually authentic to real-world photography remains challenging. This is partly due to biases in existing evaluation paradigms: human ratings and preference-trained metrics often favor visually vivid images with exaggerated saturation and contrast, which make generations often too vivid to be real even when prompted for realistic-style images.To address this issue, we present Color Fidelity Dataset (CFD) and Color Fidelity Metric (CFM) for objective evaluation of color fidelity in realistic-style generations. CFD contains over 1.3M real and synthetic images with ordered levels of color realism, while CFM employs a multimodal encoder to learn perceptual color fidelity. In addition, we propose a training-free Color Fidelity Refinement (CFR) that adaptively modulates spatial-temporal guidance scale in generation, thereby enhancing color authenticity.Together, CFD supports CFM for assessment, whose learned attention further guides CFR to refine T2I fidelity, forming a progressive framework for assessing and improving color fidelity in realistic-style T2I generation. The dataset and code are available at https://github.com/ZhengyaoFang/CFM.

Learning dense correspondences across deformable 3D shapes remains a long-standing challenge due to structural variability, non-isometric deformation, and inconsistent topology. Existing methods typically trade off generalization, geometric fidelity, and efficiency. We address this by proposing SGSoft, a unified intrinsic pipeline that (i) constructs a geodesic correspondence field on a canonical template, (ii) learns multimodal dense descriptors guided by pretrained semantic priors with this geodesic correspondence field supervision, (iii) retrieves dense correspondences in a single feed-forward pass via nearest-neighbor search in descriptor space. This formulation enables stable and topology-invariant supervision under large pose variation, structural differences, and remeshing. SGSoft achieves state-of-the-art inter-category generalization while offering the best accuracy-efficiency trade-off among prior methods. It also achieves near real-time inference without pre-alignment, pairwise optimization, or post-refinement. Learned descriptors can be transferred effectively to downstream tasks such as semantic segmentation and deformation transfer, establishing a scalable and deployment-ready paradigm for dense 3D correspondence.

WiFi sensing offers passive and privacy-preserving perception that complements vision-based sensing, but its performance degrades sharply under domain shifts caused by changes in environment, subjects, or hardware. This challenge is exacerbated in real-world deployments where source data are unavailable, motivating test-time adaptation (TTA) as a practical solution for self-calibration using only unlabeled target samples. We introduce WiTTA-Bench, the first comprehensive benchmark for WiFi TTA, covering 20 representative methods, two adaptation protocols (OTTA and TTDA), and three major physics-induced shifts in WiFi: cross-environment, cross-subject, and cross-device. Furthermore, we contribute a new dataset featuring paired recordings from heterogeneous devices to bridge the cross-device gap. Extensive experiments reveal three key insights unique to WiFi sensing: (i) WiFi domain shifts exhibit a physics-induced hierarchy; (ii) OTTA and TTDA are complementary; (iii) OTTA is generally more robust to hyperparameters, while TTDA is more sensitive due to recursive self-training. WiTTA-Bench establishes the first systematic foundation for adaptive, robust, and deployable WiFi sensing under realistic wireless conditions.

Fine-tuning large-scale Vision-Language Models (VLMs) is crucial for specialized tasks, but their performance is often undermined by the label noise prevalent in real-world datasets. Traditional approaches to learning with noisy labels typically rely on a self-referential loop, using a model's own predictions to correct errors. While recent VLM-specific methods have begun to leverage cross-modal information to aid in noise detection, we explore an alternative direction: using the text modality not just to identify noise, but to establish a source of ground truth that is fully independent of the training data's potentially corrupt labels. To this end, we propose Text-ANchored Guided Optimization (TANGO), a framework centered on "semantic anchors"--a set of pure, immutable reference points generated from diverse text descriptions. Building upon these anchors, our approach reframes two key aspects of learning with noisy labels. First, we replace the conventional linear classifier with a parameter-free Text-Anchored Classifier, making predictions a direct, weighted consensus of the clean anchors. Second, we introduce an Anchor-Guided Refinement mechanism that validates each sample's given label against this external ground truth, providing a more robust signal for sample selection and label correction. Extensive experiments demonstrate that this approach achieves competitive and often state-of-the-art performance. Our code will be publicly available.

The F-measure is a widely used metric in multi-label classification, where multiple labels are predicted simultaneously for a single instance. The optimal prediction rule for F-measure requires estimating q^2+1 probabilities, where q is the number of labels. Existing approaches train q multinomial estimators (multi-class classifiers) to directly estimate these probabilities, followed by a matrix multiplication for making predictions. However, this method has two major drawbacks. First, the matrix multiplication incurs a time complexity of \mathcal O (q^3), which becomes computationally expensive for large q. Second, training multinomial estimators is challenging due to the sparsity of the underlying distributions, which results from the inherent imbalance in multi-label datasets and is further exacerbated by the label transformation required by the method itself. In this paper, we first demonstrate that matrix multiplication can be reformulated as a series of convolutions by exploiting a special structure in the matrix. These convolutions can then be efficiently computed using the Fast Fourier Transform (FFT), reducing the time complexity to \mathcal O (q^2\log q). To avoid multinomial label transformation, we propose an indirect sampling-then-estimation approach to estimate the required probabilities. This method trains only q binary estimators instead of multinomial ones, thereby alleviating the sparsity issue, simplifying the training process, and improving performance. We provide theoretical guarantees for the consistency of the proposed sampling-based method and demonstrate its effectiveness through extensive experiments on diverse datasets. The code is available in: https://github.com/ZixunWang/MLC-F1-Sampling.

Diffusion model (DM) based Video Super-Resolution (VSR) approaches achieve impressive perceptual quality. Diffusion model (DM) based Video Super-Resolution (VSR) approaches achieve impressive perceptual quality. However, existing DM-based VSR methods over-prioritize perceptual synthesis while neglecting fidelity gains from accurate alignment and sufficient compensation. In this paper, within the DM-based VSR pipeline, we revisit the role of alignment and compensation between adjacent video frames and reveal two crucial observations: (a) the feature domain is better suited than the pixel domain for information compensation due to its stronger spatial and temporal correlations, and (b) warping at an upscaled resolution better preserves high-frequency information, but this benefit is not necessarily monotonic. Therefore, we propose a novel Densely Guided diffusion model with Aligned Features for Video Super-Resolution (DGAF-VSR), with an Optical Guided Warping Module (OGWM) to maintain high-frequency details in the aligned features and a Feature-wise Temporal Condition Module (FTCM) to deliver dense guidance in the feature domain. Extensive experiments on synthetic and real-world datasets demonstrate that DGAF-VSR surpasses state-of-the-art methods in key aspects of VSR, including perceptual quality (35.82% DISTS reduction), fidelity (0.20 dB PSNR gain), and temporal consistency (30.37% tLPIPS reduction).

In this paper, we tackle the Aerial Vision-and-Dialog Navigation (AVDN) task in the training-free setting for resource-efficient high-altitude UAV navigation.Naively applying MLLMs leads to unreliable navigation due to weak directional grounding and the lack of explicit spatial memory.To address these issues, we propose PSC-AVDN, a training-free framework that tightly couples a three-stage Parsing-Search-Confirmation reasoning pipeline with a Structured Spatial Memory (SSM).The parsing stage uses an LLM to convert ambiguous dialogue instructions into stable geometric directional and destination cues.A Search Chain-of-Thought (S-CoT) then performs stepwise target exploration under high-altitude observations, and a Confirmation Chain-of-Thought (C-CoT) conducts fine-grained verification around candidate regions to resolve visual ambiguity.Meanwhile, SSM integrates three complementary sources of spatial cues, including multi-scale visual observation, spatial visual memory, and structured geometric memory to provide global spatial context and long-horizon consistency.Extensive experiments on ANDH and ANDH-Full show that PSC-AVDN establishes new state-of-the-art performance in the training-free setting, matching or surpassing several finetuned methods.

Vision Large Language Models (VLLMs) incur high computational costs due to their reliance on hundreds of visual tokens to represent images. While token pruning offers a promising solution for accelerating inference, this paper, however, identifies a key observation: in deeper layers (e.g., beyond the 20th), existing training-free pruning methods perform no better than random pruning. We hypothesize that this degradation is caused by "vanishing token information", where visual tokens progressively lose their salience with increasing network depth. To validate this hypothesis, we quantify a token's information content by measuring the change in the model output probabilities upon its removal. Using this proposed metric, our analysis of the information of visual tokens across layers reveals three key findings: (1) As layers deepen, the information of visual tokens gradually becomes uniform and eventually vanishes at an intermediate layer, which we term as "information horizon", beyond which the visual tokens become redundant;(2) The position of this horizon is not static; it extends deeper for visually intensive tasks, such as Optical Character Recognition (OCR), compared to more general tasks like Visual Question Answering (VQA);(3) This horizon is also strongly correlated with model capacity, as stronger VLLMs (e.g., Qwen2.5-VL) employ deeper visual tokens than weaker models (e.g., LLaVA-1.5).Based on our findings, we show that simple random pruning in deep layers efficiently balances performance and efficiency. Moreover, integrating random pruning consistently enhances existing methods. Using DivPrune with random pruning achieves state-of-the-art results, maintaining 96.9% of Qwen-2.5-VL-7B performance while pruning 50% of visual tokens. The code is available at https://github.com/YahongWang1/Information-Horizon.

High-resolution content creation is rapidly emerging as a central challenge in both the vision and graphics communities. Images serve as the most fundamental modality for visual expression, and content generation that aligns with the user intent requires effective, controllable high-resolution image manipulation mechanisms. However, existing approaches remain limited to low-resolution settings, typically supporting only up to 1K resolution. In this work, we introduce the task of high-resolution image editing and propose a test-time optimization framework to address it. Our method performs patch-wise optimization on high-resolution source images, followed by a fine-grained detail transfer module and a novel synchronization strategy to maintain consistency across patches. Extensive experiments show that our method produces high-quality edits, facilitating high-resolution content creation.

While existing point-cloud-registration methods can well handle high-overlap scenarios of two point clouds, they often struggle with low-overlap scenarios, due to inevitable geometric/semantic ambiguities in the non-overlapping regions. In this paper, we introduce SuP, a novel framework that reformulates low-overlap registration as a high-overlap sub-cloud pairs (anchor pairs) mining problem. Central to SuP is our Dual-phase Sub-cloud Anchor Mining (DSAM) module, which first subdivides the source and target point clouds into multiple sub-clouds, followed by introducing a dual-phase weighting pipeline: 1) an efficient overlap-guided prior-weighting scheme (OPS) that leverages feature salience to identify candidate anchor pairs, and 2) a multi-scale post-weighting network (MPN) that exploits neighborhood feature consensus to further identify anchor pairs. Subsequently, final correspondences are generated through a merge-to-match module using the anchor pairs. To train DSAM, we design an alignment-aware weighting loss that uses on-the-fly alignment errors as supervision. Comprehensive experiments on the color-enhanced 3DMatch and 3DLoMatch demonstrate that SuP significantly outperforms state-of-the-art methods, achieving higher registration recall and more accurate alignment, especially under challenging low-overlap conditions.

Vertical federated learning (VFL) trains models by splitting computation across clients and a server that only exchange intermediate embeddings. Recent work shows that a server even if honest-but-curious can steal a client's bottom model by querying the system and regressing on the returned embeddings, and in response, defenses perturb or decouple the embedding channel. We show these defenses remain vulnerable. We propose VENOM, a geometry-aware stealing attack. VENOM first learns a contrastive space over server-observed embeddings, then builds a neighborhood graph and trains a surrogate bottom model to match targets and respect local geometry via a neighbor-matching loss alongside pointwise and feature-shape alignment. This strategy preserves the relational structure that defenses fail to erase, effectively recoupling the embeddings produced by multi-branch and noise-based defenses. Across six datasets, VENOM consistently outperforms standard stealing methods under no defense and multiple defenses, and remains effective with out-of-distribution (OOD) auxiliary data.

Modern microscopy routinely produces gigapixel images that contain structures across multiple spatial scales, from fine cellular morphology to broader tissue organization. Many analysis tasks require combining these scales, yet most vision models operate at a single resolution or derive multi-scale features from one view, limiting their ability to exploit the inherently multi-resolution nature of microscopy data. We introduce MuViT, a transformer architecture built to fuse true multi-resolution observations from the same underlying image. MuViT embeds all patches into a shared world-coordinate system and extends rotary positional embeddings to these coordinates, enabling attention to integrate large-scale context with high-resolution detail within a single encoder. Across synthetic benchmarks, kidney histopathology, and high-resolution mouse-brain microscopy, MuViT delivers consistent improvements over strong ViT and CNN baselines. Multi-resolution MAE pretraining further produces scale-consistent representations that enhance downstream tasks. These results demonstrate that explicit world-coordinate modelling provides a simple yet powerful mechanism for leveraging multi-resolution information in large-scale microscopy analysis. Code is available at github.com/weigertlab/muvit.

Robotic Foundation Models (RFMs) hold great promise as generalist, end-to-end systems for robot control.Yet their ability to generalize across new environments, tasks, and embodiments remains limited.We argue that a major bottleneck lies in their foundations: most RFMs are built by fine-tuning internet-pretrained Vision-Language Models (VLMs).However, these VLMs are trained on 2D image-language tasks and lack the 3D spatial reasoning inherently required for embodied control in the 3D world.Bridging this gap directly with large-scale robotic data is costly and difficult to scale.Instead, we propose to enrich easy-to-collect non-robotic image data with 3D annotations and enhance a pretrained VLM with 3D understanding capabilities.Following this strategy, we train SPEAR-VLM, a 3D-aware VLM that infers object coordinates in 3D space from a single 2D image.Building on SPEAR-VLM, we introduce our main contribution, SPEAR-1: a robotic foundation model that integrates grounded 3D perception with language-instructed embodied control.Trained on ~45M frames from 24 Open X-Embodiment datasets, SPEAR-1 outperforms or matches state-of-the-art models such as \pi_0-FAST and \pi_ 0.5 , while it uses 20xfewer robot demonstrations.This carefully-engineered training strategy unlocks new VLM capabilities and as a consequence boosts the reliability of embodied control beyond what is achievable with only robotic data.We make our model weights and 3D-annotated datasets publicly available.

Federated Multi-Label Learning is a distributed paradigm where multiple clients possess heterogeneous multi-label data and perform collaborative learning under privacy constraints without sharing raw data. However, modeling label correlations under heterogeneous distributions remains challenging. Due to client-specific label spaces and varying co-occurrence patterns, correlations learned by individual clients inevitably deviate from the global structure, a phenomenon we term label correlation drift. To address this, we propose FedHarmony, a framework that harmonizes heterogeneous label correlations across clients. It introduces consensus correlation, capturing agreement among clients and serving as a global teacher to correct biased local estimates. During aggregation, FedHarmony evaluates each client by both data size and correlation quality, assigning weights accordingly. Moreover, we develop an accelerated optimization algorithm and theoretically establish faster convergence without sacrificing accuracy. Experiments on real-world federated multi-label datasets show that FedHarmony consistently outperforms state-of-the-art methods.

Test-time alignment (TTA) aims to adapt models to specific rewards during inference. However, existing methods tend to either under-optimise or over-optimise (reward hack) the target reward function. We propose Null-Text Test-Time Alignment (Null-TTA), which aligns diffusion models by optimising the unconditional embedding in classifier-free guidance, rather than manipulating latent or noise variables. Due to the structured semantic nature of the text embedding space, this ensures alignment occurs on a semantically coherent manifold and prevents reward hacking (exploiting non-semantic noise patterns to improve the reward). Since the unconditional embedding in classifier-free guidance serves as the anchor for the model's generative distribution, Null-TTA directly steers model's generative distribution towards the target reward rather than just adjusting the samples, even without updating model parameters. Thanks to these desirable properties, we show that Null-TTA achieves state-of-the-art target test-time alignment while maintaining strong cross-reward generalisation. This establishes semantic-space optimisation as an effective and principled novel paradigm for TTA.

This paper reveals that many open-source large language models (LLMs) lack hierarchical knowledge about our visual world, unaware of even well-established biology taxonomies. This shortcoming makes LLMs a bottleneck for vision LLMs' hierarchical visual recognition (e.g., recognizing Anemone Fish but not Vertebrate). We arrive at these findings using about one million four-choice visual question answering (VQA) tasks constructed from six taxonomies and four image datasets. Interestingly, finetuning a vision LLM using our VQA tasks reaffirms LLMs' bottleneck effect because the VQA tasks improve the LLMs' hierarchical consistency in text-only tasks more than the vision LLMs'. We believe that one cannot make vision LLMs understand our visual world hierarchically until LLMs possess corresponding taxonomy knowledge.