CVPR 2026 Papers

Low-rank tensor representation (LRTR) is an effective tool for compactly modeling high-order data. While nonlinear LRTR models can better capture real-world nonlinear dependencies, most existing methods rely on fixed local mappings of multilayer perceptrons (MLPs) or convolutional neural networks (CNNs), limiting their ability to model complex global dependencies. To overcome this limitation, we construct a novel paradigm called Self-Attention Driven Tensor Representation (SADTR), which is the first framework that models nonlinearity from the perspective of self-attention. Specifically, we design a factor self-representation mechanism to establish dynamic global mapping, thereby adaptively capturing both local and non-local nonlinear dependencies in the factor space. Moreover, we introduce an implicit sparse representation to impose sparsity constraint while avoiding additional optimization problems. As a result, the proposed SADTR can achieve a more accurate low-rank representation. In theory, we provide a detailed analysis to demonstrate the recoverability of SADTR. To validate the effectiveness of SADTR, we apply it to three representative high-order data recovery tasks. Experimental results demonstrate that SADTR consistently outperforms existing state-of-the-art LRTR methods.

Self-supervised real-world image denoising remains a fundamental challenge, arising from the antagonistic trade-off between decorrelating spatially structured noise and preserving high-frequency details. Existing blind-spot network (BSN) methods rely on pixel-shuffle downsampling (PD) to decorrelate noise, but aggressive downsampling fragments fine structures, while milder downsampling fails to remove correlated noise. To address this, we introduce Next-Scale Prediction (NSP), a novel self-supervised paradigm that decouples noise decorrelation from detail preservation. NSP constructs cross-scale training pairs, where BSN takes low-resolution, fully decorrelated sub-images as input to predict high-resolution targets that retain fine details. As a by-product, NSP naturally supports super-resolution of noisy images without retraining or modification. Extensive experiments demonstrate that NSP achieves state-of-the-art self-supervised denoising performance on real-world benchmarks, significantly alleviating the long-standing conflict between noise decorrelation and detail preservation. The code is available at https://github.com/XLearning-SCU/2026-CVPR-NSP.

Monocular depth estimation serves as a core technique in endoscopic applications such as 3D reconstruction and localization. However, most existing methods focus primarily on in-domain depth estimation, which limits their robustness and prevents them from delivering impressive cross-domain performance, due to variations in depth distributions, illumination conditions, and texture patterns. In this work, we propose Depth Any Endoscopy (DAE), a novel self-supervised framework for generalizable depth estimation in monocular endoscopy. To specify, we develop a dual-level Mixture-of-Experts (MoE) adaptation paradigm that effectively tailors Vision Foundation Models to diverse endoscopic procedures, such as laparoscopy and colonoscopy, accounting for the challenges posed by varying environments. Internally, we integrate LoRA and Adapter modules within the MoE architecture, allowing the model to flexibly adapt to the characteristics of input data. Externally, a mixture of domain-specific experts provides customized guidance to enhance the training stability. In addition, we introduce a learnable gradient harmonization mechanism to dynamically balance the optimization between the depth and pose networks, along with a semantic distribution calibration module that strengthens the semantic consistency of depth predictions. Extensive experiments demonstrate that the proposed DAE achieves state-of-the-art performance in both zero-shot and in-domain depth estimation scenarios. The source code is publicly available at https://github.com/ShuweiShao/DAE.

Recent studies have made notable progress in video representation learning by transferring image-pretrained models to video tasks, typically with complex temporal modules and video fine-tuning. However, fine-tuning heavy modules may compromise inter-video semantic separability, i.e., the essential ability to distinguish objects across videos. While reducing the tunable parameters hinders their intra-video temporal consistency, which is required for stable representations of the same object within a video. This dilemma indicates a potential trade-off between the intra-video temporal consistency and inter-video semantic separability during image-to-video transfer. To this end, we propose the Consistency-Separability Trade-off Transfer Learning (Co-Settle) framework, which applies a lightweight projection layer on top of the frozen image-pretrained encoder to adjust representation space with a temporal cycle consistency objective and a semantic separability constraint. We further provide a theoretical support showing that the optimized projection yields a better trade-off between the two properties under appropriate conditions. Experiments on eight image-pretrained models demonstrate consistent improvements across multiple levels of video tasks with only five epochs of self-supervised training. The code is available at https://github.com/yafeng19/Co-Settle.

Underwater images often exhibit dominant blue-green hues due to wavelength-dependent light attenuation. While existing enhancement methods have achieved promising performance, they typically overlook the subjective nature of visual preferences. To address this gap, we propose SDUIE, a level-aware Semi-supervised Diffusion framework for Underwater Image Enhancement that enables dual control through both quantitative and textual inputs. SDUIE-Quant allows continuous, numerical adjustment of enhancement levels via low-rank adaptation weight merging within a dual-branch diffusion model. This model comprises a supervised branch trained on synthetic underwater-terrestrial pairs and a self-supervised branch designed to preserve the natural hues of real-world underwater scenes. Building on this, SDUIE-Text introduces intuitive, language-guided control by aligning semantic prompts with visual enhancement effects, leveraging the learned fusion weights. This dual-modality design offers both precise control and flexible, user-preferred enhancement. Experimental results demonstrate that SDUIE achieves state-of-the-art results while better preserving the aesthetic qualities often missed by conventional methods. The source code is in https://github.com/Xiaofeng-life/SDUIE.

Recent advances in Gaussian Splatting (GS) have significantly improved 3D scene reconstruction and novel view synthesis. However, most existing methods typically assume that training inputs are captured under stable and achromatic lighting conditions . In contrast, scenes recorded under temporally varying color light, as in "disco lights" commonly seen in events, performances, and decorative settings, introduce severe ambiguities in both scene photometry and geometry. We propose Disco-GS, a framework that leverages GS for reconstructing the 3D scene while simultaneously recovering the underlying canonical appearance from videos captured under dynamic lighting conditions. Disco-GS estimates the effective per-pixel transient light, which, when applied to the canonical image, results in the observed color image of the scene, thereby enabling self-supervised learning. Disco-GS is an end-to-end framework that does not rely on any prior knowledge, such as color values, ambient lighting conditions, or scene properties. It effectively handles both global and spatially localized transient color variations. It also enables controllable brightness manipulation of the canonical scene, facilitating applications such as simulating low-light and well-lit scene conditions. To the best of our knowledge, Disco-GS is the first method to simultaneously perform 3D scene reconstruction and canonical appearance recovery from inputs captured under artificially varying, disco-style colored light. To enable quantitative and qualitative evaluations, we also introduce the Disco dataset, a collection of 25 videos of real-world scenes exhibiting diverse and random color variations. Extensive experiments demonstrate the robustness and fidelity of Disco-GS. The dataset is available at: https://github.com/akumar005/Disco-GS.

Multimodal large language models (MLLMs) have achieved remarkable success on diverse visual-language tasks. However, fixed-resolution models face challenges in perceiving fine-grained visual details, particularly due to *distracted attention* and *blurry vision*. To address these issues, we propose **SLoFo**, a training-free and self-guided inference framework that mimics the human "**S**can-**Lo**cate-**Fo**cus" process. SLoFo first adopts a dual-branch mechanism to identify critical image regions: the Semantic branch constructs a gradient-based semantic relevance map, and the Structure branch estimates visual token uniqueness offering complementary and robust evidence. By combining both branches, SLoFo perceives and explicitly crop critical regions. During inference, with additional cropped sub-image, SLoFo applies a progressive visual token pruning strategy to improve attention focus on key areas while reducing computational overhead. Experiments on detail-sensitive and general-purpose benchmarks show that SLoFo consistently improves accuracy (+4.79% on TextVQA, +2.58% on GQA) and robustness (+4.60% on POPE-MSCOCO adversarial) without training or external modules.

Weakly supervised video anomaly detection (WS-VAD) aims to localize frame-level anomalies using only video-level labels. This task is typically formulated within a multiple instance learning (MIL) paradigm, where each video is treated as a bag of snippets, achieving robust performance without requiring additional information.However, existing methods often struggle with noisy supervision signals. Normal snippets within abnormal bags are frequently misclassified as anomalies due to inaccurate anomaly scores. These misclassified instances act as noisy samples, introducing false supervision that hinders the learning of true anomaly patterns.In this work, we introduce D^ 2 MIL, a Denoising-Debiasing framework within the Multiple Instance Learning paradigm designed to suppress noise and improve anomaly discrimination. Our approach integrates two key components:(1) Denoising Module: We introduce a dynamic drop rate to adaptively filter out suspected noisy samples during training, based on the observation that noisy samples incur higher training losses. (2) Debiasing Module: We leverage a vision-language model to re-evaluate the discarded samples. This recovers potentially valuable abnormal instances that were mistakenly removed, as they are similar to noisy samples but difficult for the model to recognize. D^ 2 MIL is a general purpose denoising strategy that can be integrated into any MIL-based method. Our extensive experiments on the three benchmark datasets (ShanghaiTech, UCF-Crime, and MSAD) demonstrate that D^ 2 MIL is compatible with diverse MIL frameworks and consistently enhances their performance.

Masked visual modeling is a self-supervised learning task that does not use visual annotations. It aims to learn discriminative representations via a mask-reconstruction task. A single mask ratio in reconstruction may fail to capture complex semantics, which motivates dynamic masking strategies. In this work, we propose Progressive Mask Distillation (PMD), which utilizes dynamic mask ratios to facilitate progressive semantic learning from easy to hard. PMD integrates three key components: a progressive student distiller, a difficulty-aware region enhancer, and a cross-layer feature aligner. First, to capture dynamic visual semantics, we design a progressive student distiller that trains multiple student models with progressively increasing mask ratios. The early-phase student (with a low mask ratio) learns easy, low-level semantics from more visible tokens. This learned knowledge then guides the next-phase student (with a higher mask ratio) to capture hard, high-level semantics from fewer visible tokens. This progressive distillation mechanism enhances detail reconstruction at a high mask ratio. Second, to alleviate insufficient learning of semantic regions, we design a difficulty-aware region enhancer. We first smooth the region reconstruction loss to reduce large fluctuations across training epochs. The smoothed loss is then used to learn region-level weights, prioritizing accurate learning of regions with large reconstruction losses. Third, to further bridge the semantic gap across network layers, we design cross-layer feature alignment. This module aligns features across shallow, middle, and deep encoder layers, ensuring that shallow-layer features incorporate semantic information from deeper layers. Extensive experiments demonstrate that our method achieves state-of-the-art performance on the Something-Something V2, Kinetics-400, UCF-101, and HMDB-51 datasets.

Continual self-supervised learning (CSSL) in medical imaging trains a foundation model sequentially, alleviating the need for collecting multi-modal images for joint training and offering promising improvements in downstream performance while preserving data privacy. However, most existing methods still rely on replaying data from previous stages to prevent catastrophic forgetting, which compromises privacy and limits their applicability in real-world scenarios where data transfer across sites is often restricted. In this work, we propose InvCoSS, an inversion-driven continual self-supervised learning framework for medical multi-modal image pre-training. Specifically, after training on a previous task, InvCoSS inverts the pre-trained self-supervised model to generate synthetic images that approximate the original training distribution. These synthetic images are then combined with data from the new task for joint optimization, which effectively mitigates catastrophic forgetting while strictly adhering to the constraint of no access to previous real data. Furthermore, to improve the fidelity of synthetic images, we introduce a novel InvUNet with a multi-scale fusion architecture to restore both high- and low-frequency components of the inverted images. To enhance diversity and prevent mode collapse, we design a repulsive representation-learning mechanism that encourages a diverse feature space for synthetic images without class guidance. Extensive experiments across nine downstream tasks validate the effectiveness of InvCoSS, achieving performance comparable to or even superior to prior data-replay methods while significantly reducing storage requirements and eliminating data privacy constraints.

Denoising-based diffusion transformers, despite their strong generation performance, suffer from inefficient training convergence. Existing methods addressing this issue, such as REPA (relying on external representation encoders) or SRA (requiring dual-model setups), inevitably incur heavy computational overhead during training due to external dependencies. To tackle these challenges, this paper proposes SRA 2, a lightweight intrinsic self-representation alignment framework for efficient diffusion training. SRA 2 leverages off-the-shelf pre-trained Variational Autoencoder (VAE) features: their reconstruction property ensures inherent encoding of visual priors like rich texture details, structural patterns, and basic semantic information. Specifically, SRA 2 aligns the intermediate latent features of diffusion transformers with VAE features via a lightweight projection layer, supervised by a feature alignment loss. This design accelerates training without extra representation encoders or dual-model maintenance, resulting in a simple yet effective pipeline. Extensive experiments demonstrate that SRA 2 improves both generation quality and training convergence speed compared to vanilla diffusion transformers, matches or outperforms state-of-the-art acceleration methods, and incurs merely 4% extra GFLOPs with zero additional cost for external guidance models.

Learning temporal transformations, that is, how visual objects evolve across frames, is a fundamental challenge in video representation learning. Frame-to-frame dynamics involve complex, non-linear, and non-local changes that go far beyond conventional spatial augmentations. We propose TimeBridge, a self-supervised method that combines the joint embedding for video representation with learning temporal transformations by reconstructing in-between frames from only the start and end frames. This formulation encourages the model to infer the temporal evolution bridging the two endpoints, rather than merely encoding static frame representations. Unlike joint-embedding methods that lack explicit transformation modelling or future-prediction objectives that rely on unconstrained extrapolation, TimeBridge learns concrete frame-to-frame dynamics by promoting temporal consistency. We realise this through cross-concatenated class tokens and lightweight decoders, which recombine features from the start and end frames to reconstruct intermediates. TimeBridge achieves new state-of-the-art performance on multiple dense video prediction benchmarks, including 73.5 J&F on DAVIS 2017 video object segmentation, 47.5 mIoU on VIP semantic part propagation.

To continuously enhance model adaptability in surgical video scene parsing, recent studies incrementally update it to progressively learn to segment an increasing number of surgical instruments over time. However, prior works constantly overlooked the potential of positive forward knowledge transfer, i.e., how past knowledge could help learn new classes, and positive backward knowledge transfer, i.e., how learning new classes could help refine past knowledge. In this paper, we propose a self-reflection hierarchical prompt framework that unlocks the power of positive forward and backward knowledge transfer in class incremental segmentation, aiming to proficiently learn new instruments, improve existing skills of regular instruments, and avoid catastrophic forgetting of old instruments. Our framework is built on a frozen, pre-trained model that adaptively appends instrument-aware prompts for new classes throughout training episodes. To enable positive forward knowledge transfer, we organize instrument prompts into a hierarchical prompt parsing tree with the instrument-shared prompt partition as the root node, n-part-shared prompt partitions as intermediate nodes and instrument-distinct prompt partitions as leaf nodes, to expose the reusable historical knowledge for new classes to simplify their learning. Conversely, to encourage positive backward knowledge transfer, we conduct self-reflection refining on existing knowledge by directed-weighted graph propagation, examining the knowledge associations recorded in the tree to improve its representativeness without causing catastrophic forgetting. Our framework is applicable to both CNN-based models and advanced transformer-based foundation models, yielding more than 5% and 11% improvements over the competing methods on two public benchmarks respectively.

Humans develop visual intelligence through perceiving and interacting with their environment--a self-supervised learning process grounded in egocentric experience. Inspired by this, we ask how can artificial systems learn stable object representations from continuous, uncurated first-person videos without relying on manual annotations. This setting poses challenges of separating, recognizing, and persistently tracking objects amid clutter, occlusion, and ego-motion. We propose EgoViT, a unified vision Transformer framework designed to learn stable object representations from unlabeled egocentric video. EgoViT bootstraps this learning process by jointly discovering and stabilizing "proto-objects" through three synergistic mechanisms: (1) Proto-object Learning, which uses intra-frame distillation to form discriminative representations; (2) Depth Regularization, which grounds these representations in geometric structure; and (3) Teacher-Filtered Temporal Consistency, which enforces identity over time. This creates a virtuous cycle where initial object hypotheses are progressively refined into stable, persistent representations. The framework is trained end-to-end on unlabeled first-person videos and exhibits robustness to geometric priors of varied origin and quality. On standard benchmarks, EgoViT achieves +8.0% CorLoc improvement in unsupervised object discovery and +4.8% mIoU improvement in semantic segmentation, demonstrating its potential to lay a foundation for robust visual abstraction in embodied intelligence.

Weakly Supervised Semantic Segmentation (WSSS) typically utilizes Class Activation Maps (CAMs) to provide the pixel-wise localization. However, CAMs tend to activate only the most discriminative regions, leading to suboptimal WSSS performance. Although existing CAM refinement methods leverage pair-wise relations in affinity to expand the activation regions, these affinities derived from Vision Transformer (ViTs) exhibit a smoothing property, neglecting crucial high-frequency relations and failing to accurately refine object boundaries. In this work, we propose the Dual Frequency-Aware framework (DFA) to address this limitation. Specifically, the Low-Frequency-Aware Alignment (LFAA) generates low-frequency-aware affinity that captures salient semantic relations to enhance object interior semantic consistency on CAMs, while the High-Frequency-Aware Rectification (HFAR) module produces high-frequency-aware affinity that models precise relations to preserve object boundary structure on CAMs. By effectively integrating these two complementary affinities, we design a novel Frequency-Guided (FG) CAM Generation based on Optimal Transport theory, which significantly omits the complex refinement process. Extensive experiments demonstrate that our DFA framework achieves state-of-the-art performance on both PASCAL VOC and MS COCO benchmarks. Code will be released.

We introduce SPECTRE, a fully transformer-based foundation model for volumetric computed tomography (CT). Our Self-Supervised & Cross-Modal Pretraining for CT Representation Extraction (SPECTRE) approach utilizes scalable 3D Vision Transformer architectures and modern self-supervised and vision-language pretraining strategies to learn general-purpose CT representations. Volumetric CT poses unique challenges, such as extreme token scaling, geometric anisotropy, and weak or noisy clinical supervision, that make standard transformer and contrastive learning recipes ineffective out of the box. The framework jointly optimizes a local transformer for high-resolution volumetric feature extraction and a global transformer for whole-scan context modeling, making large-scale 3D attention computationally tractable. Notably, SPECTRE is trained exclusively on openly available CT datasets, demonstrating that high-performing, generalizable representations can be achieved without relying on private data. Pretraining combines DINO-style self-distillation with SigLIP-based vision-language alignment using paired radiology reports, yielding features that are both geometrically consistent and clinically meaningful. Across multiple CT benchmarks, SPECTRE consistently outperforms prior CT foundation models in both zero-shot and fine-tuned settings, establishing SPECTRE as a scalable, open, and fully transformer-based foundation model for 3D medical imaging.

Reference-based color grading aims to reproduce the tonal mood and lighting of a reference while preserving color harmony and scene structure. Existing photorealistic and filter-based methods often produce unstable tone mappings --- over-shifting or inconsistently retaining colors --- leading to unnatural results. We propose CanonCGT, a two-stage framework built on a canonical pivot --- a style-neutral intermediate representation for stable color mapping. The first stage canonicalizes the input by removing intrinsic tonal bias, and the second color-grades it to match the reference style. A dual-phase training scheme, DP-CGT, combines supervised preset learning with self-supervised refinement on unpaired photographs. CanonCGT delivers photorealistic and tonally consistent results across diverse datasets, surpassing state-of-the-art methods in stability and visual fidelity. Our codes are available at \href https://github.com/Jinwon-Ko/CanonCGT https://github.com/Jinwon-Ko/CanonCGT .

Pixel-level annotations for medical image segmentation are costly and labor-intensive, often requiring expert knowledge. Bounding box labels provide a more scalable alternative but introduce strong box-shaped bias that hampers segmentation quality. We propose WeakMed, a general-purpose weakly supervised segmentation framework that removes the dependence on pixel-level masks while overcoming the structural limitations of box supervision. WeakMed introduces two lightweight, plug-and-play training components: (1) a Mask-to-Box transformation that aligns predicted masks with box annotations to reduce label mismatch and box-induced bias, and (2) a Scale Consistency loss that enforces multi-scale self-supervision to address the ambiguity and instability of weak labels. Both modules are used only during training and impose no inference overhead. Across 9 segmentation tasks, 9 datasets, and 6 imaging modalities, WeakMed consistently surpasses existing weakly supervised methods and achieves performance competitive with fully supervised baselines. These results demonstrate its practicality as a low-cost yet high-quality solution for medical image segmentation.

LiDAR perception is fundamental to robotics, enabling machines to understand their environment in 3D. A crucial task for LiDAR-based scene understanding and navigation is ground segmentation. However, existing methods are either handcrafted for specific sensor configurations or rely on costly per-point manual labels, severely limiting their generalization and scalability. To overcome this, we introduce TerraSeg, the first self-supervised, domain-agnostic model for LiDAR ground segmentation. We train TerraSeg on OmniLiDAR, a unified large-scale dataset that aggregates and standardizes data from 12 major public benchmarks. Spanning almost 22 million raw scans across 15 distinct sensor models, OmniLiDAR provides unprecedented diversity for learning a highly generalizable ground model. To supervise training without human annotations, we propose PseudoLabeler, a novel module that generates high-quality ground and non-ground labels through self-supervised per-scan runtime optimization. Extensive evaluations demonstrate that, despite using no manual labels, TerraSeg achieves state-of-the-art results on nuScenes, SemanticKITTI, and Waymo Perception while delivering real-time performance. Our code and model weights are publicly available.