CVPR 2026 论文列表

Detectors often suffer from degraded performance, primarily due to the distributional gap between the source and target domains. This issue is especially evident in single-source domains with limited data, as models tend to rely on confounders (e.g., illumination, co-occurrence, and style) from the source domain, leading to spurious correlations that hinder generalization. To this end, this paper proposes a novel Basis-driven framework for domain generalization, namely Bridge, that incorporates causal inference into object detection. By learning the low-rank bases for front-door adjustment, Bridge blocks confounders' effects to mitigate spurious correlations, while simultaneously refining representations by filtering redundant and task-irrelevant components.Bridge can be seamlessly integrated with both discriminative (e.g., DINOv2/3, SAM) and generative (e.g., Stable Diffusion) Vision Foundation Models (VFMs). Extensive experiments across multiple domain generalization object detection datasets, i.e., Cross-Camera, Adverse Weather, Real-to-Artistic, Diverse Weather Datasets, and Diverse Weather DroneVehicle (our newly augmented real-world UAV-based benchmark), underscore the superiority of our proposed method over previous state-of-the-art approaches. The project page is available at: https://mingbohong.github.io/Bridge/.

Understanding how transcriptomic programs shape tissue morphology remains a central challenge in computational pathology. Gene-to-WSI tile synthesis offers a principled generative framework to translate molecular profiles into histological images. However, most existing methods compress RNA-Seq into a single global embedding injected once at initialization, an oversimplified design that weakens transcriptomic signals and induces spurious, non-biological associations. We present GeneVAR, an Autoregressive Gene-to-WSI model that reformulates synthesis as an iterative, coarse-to-fine generative process. At its core is a novel Causal MeanFlow module, which leverages counterfactual interventions to suppress non-biological variations (e.g., staining or contrast artifacts) and enforce an artifact-invariant generation process. Concurrently, it reinforces transcriptome-informed guidance across multiple stages, preserving biological fidelity throughout the generative trajectory. Combined with a b-VAE for compact gene embeddings and a multi-scale vector quantizer for discrete morphology representation, GeneVAR generates H&E-stained WSI tiles that are both visually realistic and transcriptomically faithful. Extensive experiments across five TCGA cancer benchmarks demonstrate consistent state-of-the-art performance, surpassing prior methods in both generative fidelity and downstream classification accuracy.

Although reinforcement learning (RL) has significantly advanced reasoning capabilities in large multimodal language models (MLLMs), its efficacy remains limited for lightweight models essential for edge deployments. To address this issue, we leverage causal analysis and experiment to reveal the underlying phenomenon of perceptual bias, demonstrating that RL-based fine-tuning compels lightweight models to preferentially adopt perceptual shortcuts induced by data biases, rather than developing genuine reasoning abilities. Motivated by this insight, we propose VideoThinker, a causal-inspired framework that cultivates robust reasoning in lightweight models through a two-stage debiasing process. First, the Bias Aware Training stage forges a dedicated "bias model" to embody these shortcut behaviors. Then, the Causal Debiasing Policy Optimization (CDPO) algorithm fine-tunes the primary model, employing an innovative repulsive objective to actively push it away from the bias model's flawed logic while simultaneously pulling it toward correct, generalizable solutions. Our model, VideoThinker-R1, establishes a new state-of-the-art in video reasoning efficiency. For same-scale comparison, requiring no Supervised Fine-Tuning (SFT) and using only 1% of the training data for RL, it surpasses VideoRFT-3B with a 3.2% average gain on widely-used benchmarks and a 7% lead on VideoMME. For cross-scale comparison, it outperforms the larger Video-UTR-7B model on multiple benchmarks, including a 2.1% gain on MVBench and a 3.8% gain on TempCompass. Code is available at https://github.com/falonss703/VideoThinker.

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.

Autoregressive (AR) language models rely on causal tokenization, but extending this paradigm to vision remains non-trivial. Current visual tokenizers either flatten 2D patches into non-causal sequences or enforce heuristic orderings that misalign with the "next-token prediction" pattern. Recent diffusion autoencoders similarly fall short: conditioning the decoder on all tokens lacks causality, while applying nested dropout mechanism introduces imbalance. To address these challenges, we present CaTok, a 1D causal image tokenizer with a MeanFlow decoder. By selecting tokens over time intervals and binding them to the MeanFlow objective, CaTok learns causal 1D representations that support both fast one-step generation and high-fidelity multi-step sampling, while naturally capturing diverse visual concepts across token intervals. To further stabilize and accelerate training, we propose a straightforward regularization REPA-A, which aligns encoder features with Vision Foundation Models (VFMs). Experiments demonstrate that CaTok achieves state-of-the-art results on ImageNet reconstruction, reaching 0.75 FID, 22.53 PSNR and 0.674 SSIM with fewer training epochs, and the AR model attains performance comparable to leading approaches. Project website is available in https://sharelab-sii.github.io/catok-web.

Infrared small target detection is one highly special category of object detection, faced with tiny target imaging size and cluttered backgrounds. Currently, almost all existing methods are target-centered, directly learning the target features from backgrounds. However, due to weak target signals, they are often difficult in effectively capturing stable features. Sometimes, they cannot even distinguish real targets from background confounders. To overcome these problems, from an opposite perspective, we propose the first Causal-guided Hierarchical Anomaly-aware Learning (CHAL) framework. Breaking through target-centered paradigm, it focuses on background learning, while the targets are handled as the anomalies in backgrounds. In detail, to fulfill the goal, a spatio-temporal neural field is designed to model the background evolution patterns from generative perspective. Meanwhile, a hierarchical anomaly-aware learning is proposed to decompose anomaly discovery. Furthermore, to block the spurious correlations often caused by background confounders, and enhance true target causality, a causal-guiding mechanism is designed. The experiments on three infrared datasets verify the effectiveness and superiority of our CHAL. Even in visible-light scenarios, it still possesses obvious adaptivity. Source code will be open.

We propose AdapTok, an adaptive temporal causal video tokenizer that can flexibly allocate tokens for different frames based on video content. AdapTok is equipped with a block-wise masking strategy that randomly drops tail tokens of each block during training, and a block causal scorer to predict the reconstruction quality of video frames using different numbers of tokens. During inference, an adaptive token allocation strategy based on integer linear programming is further proposed to adjust token usage given predicted scores. Such design allows for sample-wise, content-aware, and temporally dynamic token allocation under a controllable overall budget. Extensive experiments for video reconstruction and generation on UCF-101 and Kinetics-600 demonstrate the effectiveness of our approach. Without additional image data, AdapTok consistently improves reconstruction quality and generation performance under different token budgets, allowing for more scalable and token-efficient generative video modeling.

Vision-language models (VLMs) have demonstrated strong potential for adapting to downstream biomedical tasks with limited training samples. However, their generalization to unseen classes within the same dataset remains limited, as the image-text alignment semantics often rely on spurious cues present in seen classes that do not transfer. To tackle this, we propose BiomedCCPL (Causal Conditional Prompt Learning), a framework that uses VGAP (Visual Grounder with Adaptive Prototype) to generate image-conditional prompts from multi-scale adaptive prototypes and employs SCD (Synergistic Causal Disentanglement) to regularize the generation of image-conditional prompts. Guided by insights from a causal analysis of generalization to unseen classes, SCD leverages multiple synergistic learning objectives to perform front-door adjustment, ensuring that the dynamically generated image-conditional prompts focus on underlying diagnostic image features shared across seen and unseen classes. Experiments on 11 datasets across 9 modalities demonstrate that BiomedCCPL effectively enhances the model's data efficiency and generalization ability. In particular, on the Base-to-Novel task, BiomedCCPL achieves an average HM of 79.98%, surpassing the previous state-of-the-art by 6.45%. Code is available at https://github.com/burgers0708/BiomedCCPL.

Recent Large Vision-Language Models (LVLMs) have shown impressive capabilities in multimodal understanding and generation. Despite this progress, they remain prone to *hallucination*, where model outputs conflict with the visual input due to an over-reliance on textual priors. Existing inference-time mitigation approaches frequently depend on multi-pass or contrastive decoding, which increases latency and limits their applicability in real-time settings. To address this limitation, we propose **CausalLens**, a training-free and single-pass intervention that directly adjusts the decoder hidden states to strengthen visual grounding. By decomposing attention heads into visual, textual, and system prompt pathways, CausalLens identifies visually reliable heads using a sensitivity measure and selectively adjusts their mid-layer hidden-state contributions. A projection-aligned correction further stabilizes these adjusted states after multi-head fusion, ensuring that the enhanced visual information is preserved throughout decoding. Extensive experiments across multiple hallucination benchmarks and LVLM architectures demonstrate that CausalLens consistently improves visual fidelity while adding negligible computational overhead. The method requires no fine-tuning or architectural changes, making it well-suited for practical, latency-sensitive applications.

Modern multi-label image classification models suffer from a critical reliance on spurious correlations, failing to learn the underlying causal mechanisms. Many causality-inspired methods are impractical, demanding box-level supervision that is rarely available in real-world datasets. Others rely on static confounder dictionaries, which are inherently inflexible and fail to capture complex biases or adapt to feature space changes during training. To address this, we present prototype-based causal intervention (ProCI), a novel framework that approximates the backdoor adjustment using only image-level supervision. It models confounders as learnable contextual prototypes engineered to represent class-wise co-occurring bias. These prototypes are learned dynamically within a stable memory and leveraged to construct sample-specific bias vectors for an adaptive feature adjustment, effectively counteracting spurious correlations. Experiments on MS-COCO, Pascal VOC, COCO-Stuff, and the challenging Sewer-ML dataset validate our approach. ProCI achieves competitive performance on standard benchmarks while setting a new state-of-the-art on the highly-confounded Sewer-ML. It outperforms the previous best model by a remarkable +5.44 points on the F2-CIW metric. These results demonstrate the effectiveness of our approach in mitigating complex real-world biases using only image-level supervision.

Intricate correlations among atomic actions and inherent visual confounders in long-term action recognition (LTAR) contribute to the persistent challenges in this domain. While methods based on vision-language models that employ label text for supervision offer potential for handling visual confounders, their reliance on statistical correlations rather than causal mechanisms introduces two vulnerabilities: (1) spurious alignments with non-causal co-occurring visual features during cross-modal interaction, and (2) misinterpretation of codependencies among actions. To address these limitations, this paper introduces Progressive Cross-Modal Causal Intervention (PCMCI). PCMCI first mitigates co-occurrence hallucination via causal intervention grounded in optimal transport theory. Subsequently, an action relation-aware mechanism counters the backdoor path induced by codependency illusion, enabling the derivation of deconfounded text embeddings. Finally, these deconfounded embeddings serve as mediator to implement front-door adjustment to remove visual confounders. This progressive causal intervention framework facilitates learning robust representations for LTAR. Experiments on three long-term action benchmarks demonstrate the effectiveness of the proposed model.

Crime anticipation enables proactive public safety interventions, yet existing video security systems remain largely reactive, unable to detect precursors of crime. While current visual language models (VLM)-based video understanding methods show promise in high-level reasoning, they are not designed to explicitly model the spatio-temporal causal relationships essential for anticipating crimes. We address this limitation by two causal-driven components. First, we develop the Spatio-Temporal Causal Reasoning Crime (STCRC) dataset, a hierarchical dataset comprising 73K samples across five progressive causal reasoning tasks, facilitating criminal precursors learning. Second, we propose the Spatio-Temporal Causal Hypergraph (STCH), a streaming module that transforms implicit entity dynamics into explicit causal structures to enhance causal reasoning for crime in VLMs. By combining these two components, our framework advances real-time crime anticipation, achieving improvements in anticipatory tasks: a 70.7% relative improvement in crime classification, a 10.1% in crime detection, and a 3.7% reduction in temporal prediction error.

Large Vision-Language Models (LVLMs) have achieved impressive performance across multimodal understanding and reasoning tasks, yet their internal safety mechanisms remain opaque and poorly controlled. In this work, we present a comprehensive framework for diagnosing and repairing unsafe channels within LVLMs. We first perform causal mediation analysis to identify neurons and layers that are causally responsible for unsafe behaviors. Based on these findings, we introduce a dual-modal safety subspace projection method that learns generalized safety subspaces for both visual and textual modalities through generalized eigen-decomposition between benign and malicious activations. During inference, activations are dynamically projected toward these safety subspaces via a hybrid fusion mechanism that adaptively balances visual and textual corrections, effectively suppressing unsafe features while preserving semantic fidelity.Extensive experiments on multiple LVLM safety benchmarks demonstrate that our causal-subspace repair framework significantly enhances safety robustness without degrading general multimodal capabilities, outperforming prior activation steering and alignment-based baselines. Moreover, our approach demonstrates robust transferability, effectively defending against unseen and adaptive attacks.

Facial Action Unit (AU) detection suffers from limited annotated data, severe class imbalance, label noise, and confounding biases, which often lead to overfitting and degraded performance. We propose an Uncertainty-Driven Causal Transformer (UDCT) framework for robust AU detection by jointly modeling uncertainty and causal intervention. Specifically, we parameterize Transformer attention weights as Gaussian distributions to capture robust AU dependencies while explicitly modeling uncertainty in attention. We further introduce an uncertainty-aware loss reweighting strategy to alleviate the effects of class imbalance and label noise during training. In addition, we incorporate a causal intervention module to suppress confounder-dependent AU associations and encourage the model to focus on more stable and less biased AU relationships. Experiments on BP4D and DISFA demonstrate that UDCT achieves competitive performance with stronger robustness under noisy, imbalanced, and distribution-shifted settings.

Causal graphs play a crucial role in AI research as they reveal the data generation processes underlying real-world machine learning and computer vision tasks. Recent studies have leveraged causal graphs to develop more robust and interpretable models. However, limited or biased data often lead to inaccurate causal graph estimation, reducing a model's transferability to unseen domains. To address this challenge, we propose a novel framework that performs Bayesian inference over causal graphs to capture potential underlying causal relations and identify invariant causal features for DG prediction. The key advantage of our framework lies in its ability to quantify causal graph uncertainty in the context of prediction tasks and incorporate it into the prediction process. Our proposed uncertainty provides valuable insights into (i) the reliability of our method on specific datasets, (ii) the alignment between learned causal graphs and unseen test domains, and (iii) the confidence of our predictions. In particular, we go beyond merely quantifying uncertainty and leverage it as weighting factors in a weighted Bayesian inference scheme. Empirical results on multiple benchmark distribution-shift datasets show that our algorithm, **C**ausal **G**raph **U**ncertainty-guided **Bayes**ian Inference (CGU-Bayes), outperforms existing DG methods on challenging datasets and achieves state-of-the-art performance overall.