Fine-grained spatiotemporal reasoning on surgical videos is critical, yet the capabilities of Multi-modal Large Language Models (MLLMs) in this domain remain largely unexplored. To bridge this gap, we introduce SurgCoT, a unified benchmark for evaluating chain-of-thought (CoT) reasoning in MLLMs across 7 surgical specialties and 35 diverse procedures. SurgCoT assesses five core reasoning dimensions: Causal Action Ordering, Cue-Action Alignment, Affordance Mapping, Micro-Transition Localization, and Anomaly Onset Tracking, through a structured CoT framework with an intensive annotation protocol (Question-Option-Knowledge-Clue-Answer), where the Knowledge field provides essential background context and Clue provides definitive spatiotemporal evidence. Evaluation of 10 leading MLLMs shows: 1) commercial models outperform open-source and medical-specialized variants; 2) significant gaps exist in surgical CoT reasoning; 3) SurgCoT enables effective evaluation and enhances progressive spatiotemporal reasoning. SurgCoT provides a reproducible testbed to narrow the gap between MLLM capabilities and clinical reasoning demands. Code and data: https://github.com/CVI-SZU/SurgCoT.
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cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Despite significant progress in Multi-modal Large Language Models (MLLMs), their clinical reasoning capacity for multi-modal diagnosis remains largely unexamined. Current benchmarks, mostly single-modality data, can't evaluate progressive reasoning and cross-modal integration essential for clinical practice. We introduce the Cross-Modality Progressive Clinical Reasoning (X-PCR) benchmark, the first comprehensive evaluation of MLLMs through a complete ophthalmology diagnostic workflow, with two reasoning tasks: 1) a six-stage progressive reasoning chain spanning image quality assessment to clinical decision-making, and 2) a cross-modality reasoning task integrating six imaging modalities. The benchmark comprises 26,415 images and 177,868 expert-verified VQA pairs curated from 51 public datasets, covering 52 ophthalmic diseases. Evaluation of 21 MLLMs reveals critical gaps in progressive reasoning and cross-modal integration. Dataset and code: https://github.com/CVI-SZU/X-PCR.
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cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Cancer survival prediction through multimodal learning that combines histopathology images with genomic data represents a promising research direction. However, current approaches still suffer from two key limitations. First, most methods operate in a Euclidean feature space, which makes it difficult to capture the intrinsic hierarchies in histopathology, where information is organized from patches to whole-slide images to patients, and in genomics, where it progresses from genes to pathways to patients. Second, they typically discretize survival times into coarse risk intervals, neglecting fine-grained ordinal relationships among samples within the same interval and thus failing to capture the continuous ranking characteristics of survival outcomes.To address these issues, we propose \ourmethod, a hyperbolic hierarchical multimodal learning framework for survival prediction. H2-SurvNet first employs a **hyperbolic hierarchical information modeling** (H2IM) module that maps multimodal features into a shared hyperbolic space and explicitly encodes intra-modal and inter-modal hierarchies across patches, WSIs, patients, genes, and pathways. On top of this representation, we design a **Temporal Ordinal Contrastive learning** (TOCL) module that models the temporal progression of survival outcomes by enforcing ordinal risk ordering through contrastive objectives, thereby promoting continuity in the learned risk scores.Extensive experiments on heterogeneous cohorts from TCGA, CPTAC, and NLST demonstrate that H2-SurvNet consistently outperforms state-of-the-art multimodal survival prediction methods and exhibits strong robustness and generalization across diverse data distributions. Source code will be released upon acceptance.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Face forgery detection faces a critical challenge: a persistent gap between offline benchmarks and real-world efficacy, which we attribute to the ecological invalidity of training data. This work introduces Agent4FaceForgery to address two fundamental problems: (1) how to capture the diverse intents and iterative processes of human forgery creation, and (2) how to model the complex, often adversarial, text-image interactions that accompany forgeries in social media. To solve this, we propose a multi-agent framework where LLM-powered agents, equipped with profile and memory modules, simulate the forgery creation process. Crucially, these agents interact in a simulated social environment to generate samples labeled for nuanced text-image consistency, moving beyond simple binary classification. An Adaptive Rejection Sampling (ARS) mechanism ensures data quality and diversity. Extensive experiments validate that the data generated by our simulation-driven approach brings significant performance gains to detectors of multiple architectures, fully demonstrating the effectiveness and value of our framework.
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cs.CV · cs.CL · cs.MA
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Existing graph models predominantly utilize self-attention mechanisms to model feature correlations between the joints of each sample, which not only neglects dynamic relation dependencies in temporal dimension but also leads to redundant computation and difficulty in establishing a unified framework for joint relation representation. To address these problems, this paper develops a Mamba-based graph convolution network (Gamba) with dynamic graph topology learning. In order to capture local motion patterns, a node classification module has been developed to categorize motion joints into distinct types. To the best of our knowledge, this is the first work to assign motion joints with label information to facilitate correlation learning. To capture the underlying relation of the joints of different categories, the state space model is introduced to process enhanced temporal features, aiming to learn dynamic adjacency matrices for long-range dependencies of the joints across different categories. The proposed framework not only facilitates an adaptive focus on spatio-temporal feature modeling but also has less computational complexity than traditional self-attention-based approaches. Extensive experiments on the public NTU RGB+D 60/120 and NW-UCLA benchmark datasets demonstrate the superiority of Gamba over state-of-the-art methods in recognition accuracy. The source code of Gamba is available at https://github.com/RCEricZhou/Gamba.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Existing vision-language models (VLMs) are tailored for pinhole imagery, stitching multiple narrow field-of-view inputs to piece together a complete omni-scene understanding. Yet, such multi-view perception overlooks the holistic spatial and contextual relationships that a single panorama inherently preserves. In this work, we introduce the Panorama-Language Modeling (PLM) paradigm, a unified 360deg vision-language reasoning that is more than the sum of its pinhole counterparts. Besides, we present PanoVQA, a large-scale panoramic VQA dataset that involves adverse omni-scenes, enabling comprehensive reasoning under object occlusions and driving accidents. To establish a foundation for PLM, we develop a plug-and-play panoramic sparse attention module that allows existing pinhole-based VLMs to process equirectangular panoramas without retraining. Extensive experiments demonstrate that our PLM achieves superior robustness and holistic reasoning under challenging omni-scenes, yielding understanding greater than the sum of its narrow parts. Project page: https://github.com/InSAI-Lab/PanoVQA.
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cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Reconstructing a physiologically plausible cardiac video from a single image remains a fundamental challenge in generative modeling, owing to the complex and nonlinear periodic dynamics of echocardiography. Previous image-to-video (I2V) approaches primarily focus on temporal continuity, yet often struggle to capture the intrinsic periodicity of cardiac motion, leading to limited temporal coherence and semantic consistency. We present EchoVDiff, a novel phase-aware diffusion model that reconstructs a full cardiac cycle from any single frame. Instead of direct pixel synthesis, EchoVDiff integrates physiological priors into a diffusion paradigm, learning interpretable mappings between cardiac phase, anatomy, and motion. By jointly modeling temporal rhythm and spatial semantics within a disentangled latent space, it achieves controllable and physiologically consistent generation. Extensive experiments on EchoNet-Dynamic and EchoNet-Pediatric demonstrate that EchoVDiff consistently surpasses state-of-the-art methods in both fidelity and temporal coherence. Remarkably, it enables accurate reconstruction of complete cardiac cycles from arbitrary phases, marking the first demonstration of single-frame-driven echocardiographic video generation.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表
Multimodal Large Language Models (MLLMs) have exhibited immense potential across numerous medical specialties, yet dentistry remains underexplored, in part due to limited domain-specific data, scarce dental expert annotations, insufficient modality-specific modeling, and challenges in reliability. In this paper, we present OralGPT-Omni, the first dental-specialized MLLM designed for comprehensive and trustworthy analysis across diverse dental imaging modalities and clinical tasks. To explicitly capture dentists' diagnostic reasoning, we construct TRACE-CoT, a clinically grounded chain-of-thought dataset that mirrors dental radiologists' decision-making processes. This reasoning supervision, combined with our proposed four-stage training paradigm, substantially strengthens the model's capacity for dental image understanding and analysis. In parallel, we introduce MMOral-Uni, the first unified multimodal benchmark for dental image analysis. It comprises 2,809 open-ended question-answer pairs spanning five modalities and five tasks, offering a comprehensive evaluation suite to date for MLLMs in digital dentistry. OralGPT-Omni achieves an overall score of 51.84 on the MMOral-Uni benchmark and 45.31 on the MMOral-OPG benchmark, dramatically outperforming the scores of GPT-5. Our work promotes intelligent dentistry and paves the way for future advances in dental image analysis. All code, benchmark, and models will be made publicly available.
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cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表