The rapid advancement of generative models, which produce increasingly realistic synthetic images, urgently demands robust and generalizable detection methods. Consequently, research has largely pivoted to leveraging large-scale Vision Foundation Models (VFMs) for enhanced generalization. However, existing VFM-based approaches primarily adhere to either perceptual or generative paradigms, each with limitations: perceptual models capture high-level semantics but often miss subtle artifacts, whereas generative models emphasize fine-grained flaws yet overlook semantic inconsistency. To resolve this inherent trade-off, we introduce SynerDetect, a novel hierarchical synergistic framework that fundamentally unifies the two paradigms. SynerDetect achieves deep integration of heterogeneous forensic representations through two levels of synergy: Cross-Model Interactive Distillation (CMID) distills generative forensic signals into perceptual encoders via prompt-guided reconstruction; and Optimal Transport-Guided Discriminative Contrastive Learning (OT-DCL) structurally aligns and integrates these heterogeneous representations, consolidating them into a robust, unified detection space. SynerDetect achieves superior performance on standard benchmarks (AIGCDetectBenchmark and GenImage) and attains a notable 5.20% accuracy gain on the challenging Chameleon benchmark, whose synthetic images consistently pass the Visual Turing Test. These results unequivocally validate the robust, real-world generalization of our unified cross-paradigm framework.

High-precision scene parsing tasks, including image matting and dichotomous segmentation, aim to accurately predict masks with extremely fine details (such as hair). Most existing methods focus on salient, single foreground objects. While interactive methods allow for target adjustment, their class-agnostic design restricts generalization across different categories. Furthermore, the scarcity of high-quality annotation has led to a reliance on inharmonious synthetic data, resulting in poor generalization to real-world scenarios. To this end, we propose a Foreground Consistent Learning model, dubbed as FCLM, to address the aforementioned issues. Specifically, we first introduce a Depth-Aware Distillation strategy where we transfer the depth-related knowledge for better foreground representation. Considering the data dilemma, we term the processing of synthetic data as domain adaptation problem where we propose a domain-invariant learning strategy to focus on foreground learning. To support interactive prediction, we contribute an Object-Oriented Decoder that can receive both visual and language prompts to predict the referring target. Experimental results show that our method quantitatively and qualitatively outperforms state-of-the-art methods.

Despite the recent advances in the application of diffusion models to the realm of image restoration, their inherent stochastic nature can often lead to inaccuracies in reconstructing spatial structures and fine details. This paper introduces an innovative paradigm that harnesses the rich knowledge encapsulated in existing high-level pre-trained models to guide the diffusion process, offering a flexible and potent approach to restoration tasks. However, there are significant challenges in leveraging a pre-trained model for image restoration, including a data gap between natural clean images and degradation images, and paradigm differences that cause insufficient intermediate knowledge. To tackle these issues, we introduce informative Messenger prompts and a Self-adapter for the pre-trained model, whose appropriate information acts as explicit constraints for diffusion, enabling reliable result generation. Specifically, ourMeSa-IRsuccessfully adapts to feature exploration for degraded samples, via disseminating distinctive information from degraded instances. Furthermore, it bolsters knowledge representation through the bidirectional interchange of hierarchical information facilitated by the innovative use of messenger prompts. Experimental results demonstrate the state-of-the-art performance of our framework on five tasks in terms of perceptual and distortion metrics. We will release codes at https://github.com/Ephemeral182/MeSa-IR.

The Transformer architecture has demonstrated remarkable results in 3D medical image segmentation due to its capability of modeling global relationships. However, it poses a significant computational burden when processing high-dimensional medical images. Mamba, as a State Space Model (SSM), has recently emerged as a notable approach for modeling long-range dependencies in sequential data. Although a substantial amount of Mamba-based research has focused on natural language and 2D image processing, few studies explore the capability of Mamba on 3D medical images. In this paper, we propose SegMamba-V2, a novel 3D medical image segmentation model, to effectively capture long-range dependencies within whole-volume features at each scale. To achieve this goal, we first devise a hierarchical scale downsampling strategy to enhance the receptive field and mitigate information loss during downsampling. Furthermore, we design a novel tri-orientated spatial Mamba block that extends the global dependency modeling process from one plane to three orthogonal planes to improve feature representation capability. Moreover, we collect and annotate a large-scale dataset (named CRC-2000) with fine-grained categories to facilitate benchmarking evaluation in 3D colorectal cancer (CRC) segmentation. We evaluate the effectiveness of our SegMamba-V2 on CRC-2000 and three other large-scale 3D medical image segmentation datasets, covering various modalities, organs, and segmentation targets. Experimental results demonstrate that our Segmamba-V2 outperforms state-of-the-art methods by a significant margin, which indicates the universality and effectiveness of the proposed model on 3D medical image segmentation tasks. The code for SegMamba-V2 is publicly available at: https://github.com/ge-xing/SegMamba-V2.

Mirror detection in dynamic scenes plays a crucial role in ensuring safety for various applications, such as drone tracking and robot navigation. However, current mirror detection models often fail in areas with mirrors that have a similar visual and color appearance to their surrounding objects. They also struggle to generalize well in complex cases, primarily due to limited annotated datasets. In this work, we propose a novel temporal prompt learning network with depth memory (TPD-Net) to address these critical challenges. Our approach includes several key components. First, we introduce a Temporal Prompt Generator (TPG) to learn temporal prompt features. Then, we devise Multi-layer Depth-aware Adaptor (MDA) modules to progressively adapt prompt features from the TPG, thereby learning mirror-related features by embedding temporal depth information as guidance. Moreover, we further refine these mirror-related features by constructing a depth memory and a Depth Memory Read module to read the temporal depths stored in the memory, boosting video mirror detection. Experimental results on a benchmark dataset show that our TPD-Net significantly outperforms 22 state-of-the-art methods in video mirror detection tasks. Our code, models, and results are publicly available athttps://github.com/ge-xing/TPDNet.

Real-world image dehazing remains a challenging task due to the diverse nature of haze degradation and the lack of large-scale paired datasets. Existing methods based on hand-crafted priors or generative priors struggle to recover accurate backgrounds and fine details from dense haze regions. In this work, we propose a novel paradigm, PromptHaze, for real-world image dehazing via the depth prompt from the Depth Anything model. By employing a prompt-by-prompt strategy, our method iteratively updates the depth prompt and progressively restores the background through a dehazing network with controllable dehazing strength. Extensive experiments on widely-used real-world dehazing benchmarks demonstrate the superiority of PromptHaze in recovering authentic backgrounds and fine details from various haze scenes, outperforming state-of-the-art methods across multiple quality metrics.

Existing low-light image enhancement (LIE) methods have achieved noteworthy success in solving synthetic distortions, yet they often fall short in practical applications. The limitations arise from two inherent challenges in real-world LIE: 1) the collection of distorted/clean image pairs is often impractical and sometimes even unavailable, and 2) accurately modeling complex degradations presents a non-trivial problem. To overcome them, we propose the Attribute Guidance Diffusion framework (AGLLDiff), a training-free method for effective real-world LIE. Instead of specifically defining the degradation process, AGLLDiff shifts the paradigm and models the desired attributes, such as image exposure, structure and color of normal-light images. These attributes are readily available and impose no assumptions about the degradation process, which guides the diffusion sampling process to a reliable high-quality solution space. Extensive experiments demonstrate that our approach outperforms the current leading unsupervised LIE methods across benchmarks in terms of distortion-based and perceptual-based metrics, and it performs well even in sophisticated wild degradation.

Snowfall presents significant challenges for visual data processing, necessitating specialized desnowing algorithms. However, existing models often fail to generalize effectively due to their heavy reliance on synthetic datasets. Furthermore, current real-world snowfall datasets are limited in scale and lack dedicated evaluation metrics designed specifically for snowfall degradation, thus hindering the effective integration of real snowy images into model training to reduce domain gaps. To address these challenges, we first introduce RealSnow10K, a large-scale, high-quality dataset consisting of over 10,000 annotated real-world snowy images. In addition, we curate a preference dataset comprising 36,000 expert-ranked image pairs, enabling the adaptation of multimodal large language models (MLLMs) to better perceive snowy image quality through our innovative Multi-Model Preference Optimization (MMPO). Finally, we propose the SnowMaster, which employs MMPO-enhanced MLLM to perform accurate snowy image evaluation and pseudo-label filtering for semi-supervised training. Experiments demonstrate that SnowMaster delivers superior desnowing performance under real-world conditions.

Mirror detection is a challenging task because a mirror’s visual appearance varies depending on the reflected content. Due to limited annotated data, current methods failed to generalize well for detecting diverse mirror scenes. Semi-supervised learning with large-scale unlabeled data can improve generalization capabilities on mirror detection, but these methods often suffer from unreliable pseudo-labels due to distribution differences between labeled and unlabeled data, therefore affecting the learning process. To address this issue, we first collect a large-scale dataset of approximately 0.4 million mirror-related images from the internet, significantly expanding the data scale for mirror detection. To effectively exploit this unlabeled dataset, we propose the first semi-supervised framework (namely an iterative data engine) consisting of four steps: (1) mirror detection model training, (2) pseudo label prediction, (3) dual guidance scoring, and (4) selection of highly reliable pseudo labels. In each iteration of the data engine, we employ a geometric accuracy scoring approach to assess pseudo labels based on multiple segmentation metrics, and design a multi-modal agent-driven semantic scoring approach to enhance the semantic perception of pseudo labels. These two scoring approaches can effectively improve the reliability of pseudo labels by selecting unlabeled samples with higher scores. Our method demonstrates promising performance across three mirror detection tasks and exhibits strong generalization on unseen examples. Our code will be publicly available at https://github.com/ge-xing/DAM.

Mirror detection is inherently difficult due to the ambiguous nature of reflections and the absence of distinctive visual indicators. While existing approaches often focus on specific mirror attributes to enhance detection accuracy, they tend to overlook the complex and diverse environments in which mirrors typically appear. Recently, vector quantized models have shown promise in capturing discrete features of images, enabling a more comprehensive understanding of environmental context. Inspired by this, we propose a novel framework-QuantPrompt-which decouples environmental context from mirror-specific features for more effective mirror detection. Our framework begins with a Semantic Quantization Module, which encodes input images into discrete semantic tokens using a learnable codebook. This step compresses continuous visual features into context-rich tokens, enhancing the model's understanding of the surrounding environment. To complement this, we introduce a Semantic Reservation Module with a dynamic prompt decoder. This component learns mirror-specific features while preserving the original semantic context through a frozen encoder, allowing the model to better distinguish mirrors from their surroundings. Extensive experiments on benchmark mirror detection datasets show that QuantPrompt achieves state-of-the-art performance, demonstrating superior accuracy and generalization compared to existing methods.

Real-world image dehazing is crucial for enhancing visual quality in computer vision applications. However, existing physics-based haze generation paradigms struggle to model the complexities of real-world haze and lack controllability, limiting the performance of existing baselines on real-world images. In this paper, we introduce GenHaze, a pioneering haze generation framework that enables the one-step generation of high-quality, reference-controllable hazy images. GenHaze leverages the pre-trained latent diffusion model (LDM) with a carefully designed clean-to-haze generation protocol to produce realistic hazy images. Additionally, by leveraging its fast, controllable generation of paired highquality hazy images, we illustrate that existing dehazing baselines can be unleashed in a simple and efficient manner. Extensive experiments indicate that GenHaze achieves visually convincing and quantitatively superior hazy images. It also significantly improves multiple existing dehazing models across 7 non-reference metrics with minimal fine-tuning epochs. Our work demonstrates that LDM possesses the potential to generate realistic degradations, providing an effective alternative to prior generation pipelines.

Consistency learning with feature perturbation is a widely used strategy in semi-supervised medical image segmentation. However, many existing perturbation methods rely on dropout, and thus require a careful manual tuning of the dropout rate, which is a sensitive hyperparameter and often difficult to optimize and may lead to suboptimal regularization. To overcome this limitation, we propose VQ-Seg, the first approach to employ vector quantization (VQ) to discretize the feature space and introduce a novel and controllable Quantized Perturbation Module (QPM) that replaces dropout. Our QPM perturbs discrete representations by shuffling the spatial locations of codebook indices, enabling effective and controllable regularization. To mitigate potential information loss caused by quantization, we design a dual-branch architecture where the post-quantization feature space is shared by both image reconstruction and segmentation tasks. Moreover, we introduce a Post-VQ Feature Adapter (PFA) to incorporate guidance from a foundation model (FM), supplementing the high-level semantic information lost during quantization. Furthermore, we collect a large-scale Lung Cancer (LC) dataset comprising 828 CT scans annotated for central-type lung carcinoma. Extensive experiments on the LC dataset and other public benchmarks demonstrate the effectiveness of our method, which outperforms state-of-the-art approaches. Codes will be released.

Ultrasound video segmentation gains increasing attention in clinical practice due to the redundant dynamic references in video frames. However, traditional convolutional neural networks have a limited receptive field and transformer-based networks are unsatisfactory in constructing long-term dependency from the perspective of computational complexity. This bottleneck poses a significant challenge when processing longer sequences in medical video analysis tasks using available devices with limited memory. Recently, state space models (SSMs), famous by Mamba, have exhibited linear complexity and impressive achievements in efficient long sequence modeling, which have developed deep neural networks by expanding the receptive field on many vision tasks significantly. Unfortunately, vanilla SSMs failed to simultaneously capture causal temporal cues and preserve non-casual spatial information. To this end, this paper presents a Video Vision Mamba-based framework, dubbed as Vivim, for ultrasound video segmentation tasks. Our Vivim can effectively compress the long-term spatiotemporal representation into sequences at varying scales with our designed Temporal Mamba Block. We also introduce an improved boundary-aware affine constraint across frames to enhance the discriminative ability of Vivim on ambiguous lesions. Extensive experiments on thyroid segmentation in ultrasound videos, breast lesion segmentation in ultrasound videos, and polyp segmentation in colonoscopy videos demonstrate the effectiveness and efficiency of our Vivim, superior to existing methods. The code and dataset are available at: https://github.com/scott-yjyang/Vivim.

Computer-aided ultrasound (US) imaging is an important prerequisite for early clinical diagnosis and treatment. Due to the harsh ultrasound (US) image quality and the blurry tumor area, recent memory-based video object segmentation models (VOS) achieve frame-level segmentation by performing intensive similarity matching among the past frames which could inevitably result in computational redundancy. In this paper, we first build a larger annotated benchmark dataset for breast lesion segmentation in ultrasound videos, then we propose a lightweight clip-level VOS framework for achieving higher segmentation accuracy while maintaining the speed. Then an Inner-Outer Clip Retformer is proposed to extract spatial-temporal tumor features in parallel. Specifically, the proposed Outer Clip Retformer extracts the tumor movement feature from past video clips to locate the current clip tumor position, while the Inner Clip Retformer detailedly extracts current tumor features that can produce more accurate segmentation results. Then a Clip Contrastive loss function is further proposed to align the extracted tumor features along both the spatial-temporal dimensions to improve the segmentation accuracy. In addition, the Global Retentive Memory is proposed to maintain the complementary tumor features with lower computing resources which can generate coherent temporal movement features. In this way, our model can significantly improve the spatial-temporal perception ability without increasing a large number of parameters, achieving more accurate segmentation results while maintaining a faster segmentation speed. Finally, we conduct extensive experiments to evaluate our proposed model on several video object segmentation datasets, the results show that our framework outperforms state-of-the-art segmentation methods.

Recently, Denoising Diffusion Models have achieved outstanding success in generative image modeling and attracted significant attention in the computer vision community. Although a substantial amount of diffusion-based research has focused on generative tasks, few studies apply diffusion models to medical diagnosis. In this paper, we propose a diffusion-based network (named DiffMIC-v2) to address general medical image classification by eliminating unexpected noise and perturbations in image representations. To achieve this goal, we first devise an improved dual-conditional guidance strategy that conditions each diffusion step with multiple granularities to enhance step-wise regional attention. Furthermore, we design a novel Heterologous diffusion process that achieves efficient visual representation learning in the latent space. We evaluate the effectiveness of our DiffMIC-v2 on four medical classification tasks with different image modalities, including thoracic diseases classification on chest X-ray, placental maturity grading on ultrasound images, skin lesion classification using dermatoscopic images, and diabetic retinopathy grading using fundus images. Experimental results demonstrate that our DiffMIC-v2 outperforms state-of-the-art methods by a significant margin, which indicates the universality and effectiveness of the proposed model on multi-class and multi-label classification tasks. DiffMIC-v2 can use fewer iterations than our previous DiffMIC to obtain accurate estimations, and also achieves greater runtime efficiency with superior results. The code will be publicly available at https://github.com/scott-yjyang/DiffMICv2.

Diffusion Models have shown remarkable performance in image generation tasks, which are capable of generating diverse and realistic image content. When adopting diffusion models for image restoration, the crucial challenge lies in how to preserve high-level image fidelity in the random-ness diffusion process and generate accurate background structures and realistic texture details. In this paper, we propose a general framework and develop a Diffusion Texture Prior Model (DTPM) for image restoration tasks. DTPM explicitly models high-quality texture details through the diffusion process, rather than global contextual content. In phase one of the training stage, we pretrain DTPM on approximately 55K high-quality image samples, after which we freeze most of its parameters. In phase two, we insert conditional guidance adapters into DTPM and equip it with an initial predictor, thereby facilitating its rapid adaptation to downstream image restoration tasks. Our DTPM could mitigate the randomness of traditional diffusion models by utilizing encapsulated rich and diverse texture knowledge and background structural information provided by the initial predictor during the sampling process.

Video Shadow Detection (VSD) aims to detect the shadow masks with frame sequence. Existing works suffer from inefficient temporal learning. Moreover, few works address the VSD problem by considering the characteristic (i.e., boundary) of shadow. Motivated by this, we propose a Timeline and Boundary Guided Diffusion (TBGDiff) network for VSD where we take account of the past-future temporal guidance and boundary information jointly. In detail, we design a Dual Scale Aggregation (DSA) module for better temporal understanding by rethinking the affinity of the long-term and short-term frames for the clipped video. Next, we introduce Shadow Boundary Aware Attention (SBAA) to utilize the edge contexts for capturing the characteristics of shadows. Moreover, we are the first to introduce the Diffusion model for VSD in which we explore a Space-Time Encoded Embedding (STEE) to inject the temporal guidance for Diffusion to conduct shadow detection. Benefiting from these designs, our model can not only capture the temporal information but also the shadow property. Extensive experiments show that the performance of our approach overtakes the state-of-the-art methods, verifying the effectiveness of our components. We release the codes, weights, and results at \url{https://github.com/haipengzhou856/TBGDiff}.

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified metrics, unfair comparisons, and short-term outcome pressure. As a consequence, good performance on standard benchmarks does not guarantee success in real-world scenarios. To address these problems, we present Touchstone, a large-scale collaborative segmentation benchmark of 9 types of abdominal organs. This benchmark is based on 5,195 training CT scans from 76 hospitals around the world and 5,903 testing CT scans from 11 additional hospitals. This diverse test set enhances the statistical significance of benchmark results and rigorously evaluates AI algorithms across various out-of-distribution scenarios. We invited 14 inventors of 19 AI algorithms to train their algorithms, while our team, as a third party, independently evaluated these algorithms on three test sets. In addition, we also evaluated pre-existing AI frameworks---which, differing from algorithms, are more flexible and can support different algorithms—including MONAI from NVIDIA, nnU-Net from DKFZ, and numerous other open-source frameworks. We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain.

• This paper investigates the capacity of AI models for airway modelling on national datasets with paired clinical metadata. • We evaluated AI models against unharmonised, noisy, and out-of-distribution data, as well as the prognostication for FLD. • We found a new biomarker for mortality prediction, outperforming existing clinical measurements (FVC% and fibrosis scores). • In-depth analysis of AI models on airway modelling and prognosis, highlighting challenges and future research directions. Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway structures remains prohibitively time-consuming. While significant efforts have been made towards enhancing automatic airway modelling, current public-available datasets predominantly concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023′ (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for mortality prediction, a strong airway-derived biomarker (Hazard ratio>1.5, p < 0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.

Masked image modeling (MIM) with transformer backbones has recently been exploited as a powerful self-supervised pre-training technique. The existing MIM methods adopt the strategy to mask random patches of the image and reconstruct the missing pixels, which only considers semantic information at a lower level, and causes a long pre-training time. This paper presents HybridMIM, a novel hybrid self-supervised learning method based on masked image modeling for 3D medical image segmentation. Specifically, we design a two-level masking hierarchy to specify which and how patches in sub-volumes are masked, effectively providing the constraints of higher level semantic information. Then we learn the semantic information of medical images at three levels, including: 1) partial region prediction to reconstruct key contents of the 3D image, which largely reduces the pre-training time burden (pixel-level); 2) patch-masking perception to learn the spatial relationship between the patches in each sub-volume (region-level); and 3) drop-out-based contrastive learning between samples within a mini-batch, which further improves the generalization ability of the framework (sample-level). The proposed framework is versatile to support both CNN and transformer as encoder backbones, and also enables to pre-train decoders for image segmentation. We conduct comprehensive experiments on five widely-used public medical image segmentation datasets, including BraTS2020, BTCV, MSD Liver, MSD Spleen, and BraTS2023. The experimental results show the clear superiority of HybridMIM against competing supervised methods, masked pre-training approaches, and other self-supervised methods, in terms of quantitative metrics, speed performance and qualitative observations.

Existing segmentation methods for brain MRI data usually leverage 3D CNNs on 3D volumes or employ 2D CNNs on 2D image slices. We discovered that while volume-based approaches well respect spatial relationships across slices, slice-based methods typically excel at capturing fine local features. Furthermore, there is a wealth of complementary information between their segmentation predictions. Inspired by this observation, we develop an Uncertainty-aware Multi-dimensional Mutual learning framework to learn different dimensional networks simultaneously, each of which provides useful soft labels as supervision to the others, thus effectively improving the generalization ability. Specifically, our framework builds upon a 2D-CNN, a 2.5D-CNN, and a 3D-CNN, while an uncertainty gating mechanism is leveraged to facilitate the selection of qualified soft labels, so as to ensure the reliability of shared information. The proposed method is a general framework and can be applied to varying backbones. The experimental results on three datasets demonstrate that our method can significantly enhance the performance of the backbone network by notable margins, achieving a Dice metric improvement of 2.8% on MeniSeg, 1.4% on IBSR, and 1.3% on BraTS2020.