CVPR 2026 Papers

Worldwide visual geo-localization aims to determine the geographic location of an image anywhere on Earth using only its visual content. Despite recent progress, learning expressive representations of geographic space remains challenging due to the inherently low-dimensional nature of geographic coordinates. We formulate global geo-localization as aligning the visual representation of a query image with a learned geographic representation. Our approach explicitly models the world as a hierarchy of learned geographic embeddings, enabling a distributed and multi-scale representation of geographic space. In addition, we introduce a semantic fusion module that efficiently integrates appearance features with semantic segmentation through latent cross-attention, producing a more robust visual representation for localization. Experiments on five widely used geo-localization benchmarks demonstrate that our method achieves new state-of-the-art results on 22 of 25 reported metrics. Ablation studies show that these improvements are primarily driven by the proposed geographic representation and semantic fusion mechanism.

Existing ViT backdoor attacks based on backbone-overwriting full-tuning are computationally expensive and inflict performance degradation. This has forced adversaries towards the Visual Parameter-Efficient Fine-Tuning (PEFT) paradigm, dominated by adapter-based (e.g., LoRA) and prompt-based (e.g., VPT) approaches. While adapter security has seen initial study, the risks of the burgeoning prompt-based ecosystem remain critically unexplored. We fill this critical gap, exposing how the evolution of VPT towards dynamic and context-aware architectures can facilitate a far more dangerous and emergent threat.This vulnerability arises even though these dynamic modules unlock superior benign performance. We propose VIPER, an attack framework built on a lightweight, dynamic Visual Prompt Generator (VPG) that demonstrates this vulnerability. Critically, this dynamic architecture enables Functional Fusion: an emergent phenomenon where malicious logic and benign task utility are tightly fused into the same sparse, high-magnitude parameter core. This fusion creates a formidable "hostage" dilemma, as pruning the attack necessarily destroys the benign performance. Comprehensive evaluations show VIPER effectively addresses the attacker's trilemma: VIPER not only achieves state-of-the-art performance on clean data, but also maintains near-100% ASR even under 90% VPG-module pruning (where LoRA attacks collapse), while adding only an imperceptible 0.06ms (1.16%) of inference latency. VIPER's results, driven by Functional Fusion, expose a new, paradigm-level risk in dynamic prompt architectures.

Tracking any point (TAP) is a fundamental yet challenging task in computer vision, requiring high precision and long-term motion reasoning. Recent attempts to combine RGB frames and event streams have shown promise, yet they typically rely on synchronous or non-adaptive fusion, leading to temporal misalignment and severe degradation when one modality fails. We introduce TAPFormer, a transformer-based framework that performs asynchronous temporal-consistent fusion of frames and events for robust and high-frequency arbitrary point tracking.Our key innovation is a Transient Asynchronous Fusion (TAF) mechanism, which explicitly models the temporal evolution between discrete frames through continuous event updates--bridging the gap between low-rate frames and high-rate events. In addition, a Cross-modal Locally Weighted Fusion (CLWF) module adaptively adjusts spatial attention according to modality reliability, yielding stable and discriminative features even under blur or low light.To evaluate our approach under realistic conditions, we construct a novel real-world frame-event TAP dataset under diverse illumination and motion conditions.Our method outperforms existing point trackers, achieving a 28.2% improvement in average pixel error within threshold. Moreover, on standard point tracking benchmarks, our tracker consistently achieves the best performance. We will release the code and dataset upon acceptance to support future research.

The development of AI-generated technology requires effective image quality assessment (AGIQA) methods to jointly evaluate visual quality and text-content alignment, ensuring that the generated content is both visually appealing and faithful to the user's instructions. Nevertheless, visual degradation and text-content misalignment often coincide, and it is difficult to tell whether a bad subjective evaluation arises from prompt noncompliance or rendering artifacts. As such, disentangling image content and rendering distortions is vital. We propose the dual-prior guided fusion network (DPGF-Net), which leverages image-side priors to disentangle distortions from content and combines them with text-side prompt templates to simulate their interactions, to address this issue. DPGF-Net employs a local text-conditioned aggregation branch to highlight semantically relevant and quality-sensitive regions in conjunction with a global modulation branch that captures holistic perceptual characteristics. Finally, adaptive fusion produces a single score. Experiments on three AGIQA datasets demonstrate that our method is highly correlated with human judgments, with lower prediction error and stable evaluation behavior. The source code is available at https://github.com/leeto221/AGIQA.

Understanding how the brain constructs coherent 3D visual percepts from multifaceted experiences remains a pivotal yet underexplored challenge. To investigate this, we introduce BrainSSD, a novel framework for decoding 3D representations from electroencephalography (EEG) signals. The core of BrainSSD is a neuro-inspired fusion architecture, Hierarchical Phase-Amplitude Coupling guided Fusion (HPACF), which synergistically integrates EEG from two distinct viewing paradigms: brief presentations of a static 3D object view, and sustained observation of the object undergoing full rotation. HPACF embodies two key principles of neural computation, namely hierarchical processing realized through multi-level cross-attention, and neural synchrony actualized by using a differentiable estimator of Phase-Amplitude Coupling (PAC) to dynamically guide the integration. The resulting fused representations are subsequently mapped to the visual domain via a multi-level alignment loss. Our framework establishes a new state-of-the-art across a range of EEG decoding tasks, achieving superior discriminative power and exceptional generative fidelity. Furthermore, our static-dynamic dominance analysis provides the first direct visual evidence for a functional specialization in the brain's 3D perception, revealing that neural responses to static object views primarily underpin the object's holistic structure and form, while responses to rotational observation are indispensable for resolving its fine-grained geometric details. Our work presents an advanced framework for probing EEG-based visual decoding and offers computational insights into the brain's synergistic strategies for 3D perception.

Existing image fusion methods face difficulties in adapting to unseen fusion tasks and have limitations in balancing semantic information with pixel-level details. This limitation can be attributed to three key challenges: (1) the lack of a unified, task-agnostic optimization objective; (2) the inherent difficulty in balancing semantic fidelity and pixel-level richness; and (3) an over-reliance on supervised learning, which limits transferability across tasks. To overcome these issues, this work proposes a unified fusion framework that generalizes to diverse fusion tasks even when trained solely on infrared-visible image pairs. Specifically, inspired by the free-energy principle, we introduce a fusion paradigm that combines high pixel-entropy expectation with low semantic-entropy expectation, and we design a frequency-aware feature decoupling mechanism to balance semantic content and pixel detail. Furthermore, an unsupervised dual-path trade-off strategy provides collaborative constraints at both semantic and pixel levels. Experiments show that our method significantly outperforms existing state-of-the-art methods in visual quality and downstream-task performance. It not only handles trained tasks efficiently but also generalizes well to unseen fusion tasks, while featuring lightweight model parameters and strong practical applicability. The code is available at https://github.com/XiaoW-Liu/DECC.

Current collaborative perception systems have significantly improved 3D object detection performance. However, widely used LiDAR and camera systems often suffer performance degradation under adverse weather conditions. The weather-robust 4D radar provides a promising solution to address this challenge. Nevertheless, the effective fusion of sparse 4D radar measurements with degraded LiDAR data remains a significant challenge due to cross-modal corruption and information loss. In this work, we propose a novel hybrid robust collaborative perception framework (HRCP), designed to improve the collaborative perception performance under adverse weather conditions through LiDAR-4D radar fusion. Specifically, we introduce a hybrid collaboration strategy that considers their distinct physical properties and differently processes them during information transmission. Additionally, we propose a bidirectional cross-modal gating (BCMG) module that enables LiDAR and 4D radar to mutually validate feature reliability, ensuring consistent cross-modal representation, and an adaptive feature enhancement (AFE) module that enables comprehensive refinement of degraded and suppressed regions to mitigate information loss. Extensive experimental results demonstrate that our method outperforms previous state-of-the-art approaches under adverse weather conditions.

Unregistered hyperspectral image (HSI) super-resolution (SR) typically aims to enhance a low-resolution HSI using an unregistered high-resolution reference image. In this paper, we propose an unmixing-based fusion framework that decouples spatial-spectral information to simultaneously mitigate the impact of unregistered fusion and enhance the learnability of SR models. Specifically, we first utilize singular value decomposition for initial spectral unmixing, preserving the original endmembers while dedicating the subsequent network to enhancing the initial abundance map. To leverage the spatial texture of the unregistered reference, we introduce a coarse-to-fine deformable aggregation module, which first estimates a pixel-level flow and a similarity map using a coarse pyramid predictor. It further performs fine sub-pixel refinement to achieve deformable aggregation of the reference features. The aggregative features are then refined via a series of spatial-channel abundance cross-attention blocks. Furthermore, a spatial-channel modulated fusion module is presented to merge encoder-decoder features using dynamic gating weights, yielding a high-quality, high-resolution HSI. Experimental results on simulated and real datasets confirm that our proposed method achieves state-of-the-art super-resolution performance. The code will be available at https://github.com/yingkai-zhang/UAFL.

Nuclei instance segmentation in histopathology images is essential for diagnostic accuracy and downstream computational tasks, yet this task relies heavily on expensive pixel level annotations. Although point level annotations substantially reduce the annotation burden for pathologists, many existing methods utilize only a single type of image and overlook the complementary information contained in alternative representations. To address this limitation, we propose DFGNet, a weakly supervised framework that utilizes dual-representation complementary fusion and interleaved guidance learning by jointly modeling RGB images and their corresponding Hematoxylin components. From the complementary fusion perspective, we propose a Reciprocal Cross-scale Dynamic Fusion Module (RCDF) and an Entropy Confidence Aggregation Unit (ECAU) to integrate multi-scale complementary cues and adaptively combine the outputs of the dual branches. In terms of interleaved guidance, we also propose an Interleaved point-Guided Attention (IGA) that enables bidirectional refinement between the segmentation task and the kernel prediction task. Experiments on three benchmark datasets show that DFGNet achieves state-of-the-art performance across multiple metrics and significantly outperforms existing methods. It also demonstrates strong generalization across tissue types and remains robust to annotation shifts in real-world scenarios.

Heterogeneous reconstruction in cryo-electron microscopy (Cryo-EM) is fundamental for understanding macromolecular structural diversity, yet remains challenging due to extreme noise, continuous conformational changes, and ambiguous image-to-structure mappings. Existing neural approaches often rely on encoder--decoder pipelines or fixed codebooks, which can be computationally demanding or struggle with complex heterogeneity. We propose CryoKRAQEN, a decoder-only framework that integrates triplane implicit representations with kernel-guided latent assignment and quantized embeddings to improve stability and structural discrimination. The method avoids encoder dependencies and mitigates collapse during training, enabling accurate modeling of both conformational and compositional variations. Across diverse Cryo-EM benchmarks, CryoKRAQEN delivers competitive performance, robust reconstructions, and interpretable latent organization compared to state-of-the-art neural and classical methods.

Even when told to "do nothing," modern diffusion models subtly alter their output relative to the input they are supposed to preserve. We call this effect No-Op Drift. We introduce the Drift Kernel K_M(sigma), defined as the expected perceptual deviation induced when running a diffusion model at noise strength sigma under a null instruction. Using 120,000 baseline samples (30,000 per model across SD15, SD21, SDXL, and InstructPix2Pix) and 9,600 ablation samples (four strengths, null versus strict copy prompts), we show that variance-driven diffusion models follow a quadratic form K_M(sigma) approximately equal to k_M sigma^2 plus c_M, with aggregate R^2 equal to 0.97. We derive this scaling from first principles via a Taylor expansion of the decoder, yielding k_M equal to Tr(J_D J_D^T), which depends only on the decoder Jacobian and not on prompts. To validate mechanistic structure, we construct synthetic decoders that reproduce the two regimes seen in practice: quadratic variance-driven drift and flat, high-variance edit-driven drift. We show that prompt wording has negligible effect (less than 17 percent coefficient difference), proving that drift is structural and not prompt-induced. We release NoOp-Bench, a benchmark with 10,000 inputs and full code for reproducible kernel estimation. Additional proofs, ablations, LPIPS and CLIP metrics, and extended visualizations appear in the Supplementary Material.