GenAI4ELab/papercli-papers huggingface dataset:
Breaking the Continuum: Discrete Distribution Learning for Structural MRI Reconstruction
Authors: Tianle Lyu; Mengjingcheng Mo; Ting Wen; Zhen Song; Zinan Xiong; Yanjie Zhu
Venue: CVPR 2026 (main)

Abstract: Anatomical structures in MRI exhibit strong spatial priors, including well-defined boundaries, low inter-subject variability, and consistent topology. These properties naturally induce clustered patterns in the latent space, which are difficult to capture using conventional continuous generative priors that assume smooth manifold distributions. To address this limitation, we propose DiCoS (Discrete-Continuous Synthesis), a generative reconstruction framework that integrates discrete structural reasoning with continuous refinement. DiCoS models an anatomy-aware discrete distribution and generates diverse reconstructions in one coarse-to-fine pass through a Discrete Prior Network (DPN). A Dual-domain Balanced Scoring (DBS) mechanism adaptively evaluates candidates using both image-domain fidelity and k-space consistency.  To further enhance realism, Micro Diffusion Cycles (MDC) perform efficient score-guided refinement to enhance texture realism without disturbing global topology. Experiments on the fastMRI knee and brain datasets demonstrate that DiCoS achieves state-of-the-art reconstruction quality with sharper boundaries and improved anatomical consistency. Beyond pixel metrics, segmentation-based evaluations further confirm superior structural overlap and semantic alignment, highlighting DiCoS's advantages in anatomy-aware reconstruction. The project page is available at https://kincin.github.io/DiCoS/.
SpaceDrive: Infusing Spatial Awareness into VLM-based Autonomous Driving
Authors: Peizheng Li; Zhenghao Zhang; David Holtz; Hang Yu; Yutong Yang; Yuzhi Lai; Rui Song; Andreas Geiger; Andreas Zell
Venue: CVPR 2026 (main)

Abstract: End-to-end autonomous driving methods built on vision language models (VLMs) have undergone rapid development driven by their universal visual understanding and strong reasoning capabilities obtained from the large-scale pretraining. However, we find that current VLMs struggle to understand fine-grained 3D spatial relationships which is a fundamental requirement for systems interacting with the physical world. To address this issue, we propose SpaceDrive, a spatial-aware VLM-based driving framework that treats spatial information as explicit positional encodings (PEs) instead of textual digit tokens, enabling joint reasoning over semantic and spatial representations. SpaceDrive employs a universal positional encoder to all 3D coordinates derived from multi-view depth estimation, historical ego-states, and text prompts. These 3D PEs are first superimposed to augment the corresponding 2D visual tokens. Meanwhile, they serve as a task-agnostic coordinate representation, replacing the digit-wise numerical tokens as both inputs and outputs for the VLM. This mechanism enables the model to better index specific visual semantics in spatial reasoning and directly regress trajectory coordinates rather than generating digit-by-digit, thereby enhancing planning accuracy. Extensive experiments validate that SpaceDrive achieves state-of-the-art open-loop performance on the nuScenes dataset and the second-best Driving Score of 78.02 on the Bench2Drive closed-loop benchmark over existing VLM-based methods. Code is available at: https://github.com/zhenghao2519/SpaceDrive.
H2-Surv: Hierarchical Hyperbolic Multimodal Representation Learning for Survival Prediction
Authors: Jiaqi Yang; Wenting Chen; Xiangjian He; Yuanbai Li; Sen Yang; Linlin Shen; Xiaohan Xing
Venue: CVPR 2026 (main)

Abstract: 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.
CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval
Authors: Jiahui Geng; Qing Li; Fengyu Cai; Fakhri Karray
Venue: CVPR 2026 (main)

Abstract: Code search, framed as information retrieval (IR), underpins modern software engineering and increasingly powers retrieval-augmented generation (RAG), improving code discovery, reuse, and the reliability of LLM-based coding.Yet existing code IR models remain largely text-centric and often overlook the visual and structural aspects inherent in programming artifacts such as web interfaces, data visualizations, SVGs, schematic diagrams, and UML.To bridge this gap, we introduce MMCoIR, the first comprehensive benchmark for evaluating multimodal code IR across five visual domains, and show through extensive evaluation the task is challenging.Therefore, we then propose CodeMMR, a unified retrieval model that jointly embeds natural language, code, and images into a shared semantic space through instruction-based multimodal alignment.CodeMMR achieves strong generalization across modalities and languages, outperforming competitive baselines (e.g., UniIR, GME, VLM2Vec) by an average of 10 points on nDCG@10.Moreover, integrating CodeMMR into RAG enhances code generation fidelity and visual grounding on unseen code generation tasks, underscoring the potential of multimodal retrieval as a core enabler for next-generation intelligent programming systems.
RemedyGS: Defend 3D Gaussian Splatting Against Computation Cost Attacks
Authors: Yanping Li; Zhening Liu; Zijian Li; Zehong Lin; Jun Zhang
Venue: CVPR 2026 (main)

Abstract: As a mainstream technique for 3D reconstruction, 3D Gaussian splatting (3DGS) has been applied in a wide range of applications and services. Recent studies have revealed critical vulnerabilities in this pipeline and introduced computation cost attacks that lead to malicious resource occupancies and even denial-of-service (DoS) conditions, thereby hindering the reliable deployment of 3DGS. In this paper, we propose the first effective and comprehensive black-box defense framework, named RemedyGS, against such computation cost attacks, safeguarding 3DGS reconstruction systems and services. Our pipeline comprises two key components: a detector to identify the attacked input images with poisoned textures and a purifier to recover the benign images from their attacked counterparts, mitigating the adverse effects of these attacks. Moreover, we incorporate adversarial training into the purifier to enforce distributional alignment between the recovered and original natural images, thereby enhancing the defense efficacy. Experimental results demonstrate that our framework effectively defends against white-box, black-box, and adaptive attacks in 3DGS systems, achieving state-of-the-art performance in both safety and utility. Our code is available at https://github.com/Polly-LYP/RemedyGS.
Beyond Explicit Language: Plug-and-Play Visual-to-Linguistic Modeling Toward General Object Tracking
Authors: Kaiyang Lan; Ying Cui; Chenchen Jing; Jianwei Zheng; Dongyan Guo
Venue: CVPR 2026 (main)

Abstract: Natural language provides valuable auxiliary information for enhancing visual object tracking. While existing vision-language tracking methods explicitly leverage linguistic descriptions to aid tracking, they suffer from two critical limitations: the inability to dynamically adapt descriptions to the moving target and changing context; and the strong dependency on language input may causes failure when text is unavailable. To address the issues, we design a simple yet effective plug-and-play module that leverages linguistic assistance implicitly, without requiring explicit language input. The proposed textual inversion module converts visual features from template and search regions into text tokens in the CLIP text embedding space. It effectively inverts visual representations into linguistic forms, integrating contextual information from the both template and search region. The linguistic cues are then injected into the visual feature space via a multi-layer semantic injection mechanism. The design enhances the completeness of cross-modal feature representations and the accuracy of inter-modal semantic alignment, thus enabling dynamically updated linguistic information guidance for general object tracking. Extensive experiments demonstrate the effectiveness of our proposed method. We integrate the proposed module into several advanced trackers and evaluate on both visual and vision-language tracking datasets, including MCITrack, DUTrack, and SeqTrack. By training only the newly introduced module and the corresponding decoder, the proposed approach achieves significant performance gains with minimal computational overhead. Code will be made publicly available.
BiProLoRA: Bilevel Prompt LoRA for Real Scene Recovery
Authors: Nan An; Long Ma; Tengyu Ma; Zhu Liu; Yingchi Liu; Risheng Liu
Venue: CVPR 2026 (main)

Abstract: The emergence of large generative models has substantially advanced learning-based scene recovery in the synthetic domain. However, these models generalize poorly to real scenarios stemming from the significant distribution gap, alongside poor adaptation to complex and unforeseen degradations. Consequently, it is imperative to develop a real scene adaptation strategy that yields faithful restorations with reliable generalizability. To this end, we propose Bilevel Prompt LoRA, a novel learning paradigm designed to effectively adapt pre-trained generative models for real scene recovery. First, we introduce a self-supervised distribution-fidelity learning scheme to calibrate the autoencoding pathway under task-irrelevant real distributions to improve texture fidelity. Subsequently, a bilevel joint modeling via hyperparameter optimization is further established, empowering robust synthetic-to-real adaptation for both seen and unseen scenes by exploiting the complementary advantages between LoRA and Prompts to foster mutual promotion. Extensive evaluations on diverse real adverse scenarios demonstrate our superiority, with comprehensive algorithm analyses proving our effectiveness.
Benchmarking Endoscopic Surgical Image Restoration and Beyond
Authors: Jialun Pei; Diandian Guo; Donghui Yang; Zhixi Li; Yuxin Feng; Long Ma; Bo Du; Pheng-Ann Heng
Venue: CVPR 2026 (main)

Abstract: In endoscopic surgery, a clear and high-quality visual field is critical for surgeons to make accurate intraoperative decisions. However, persistent visual degradation, including smoke generated by energy devices, lens fogging from thermal gradients, and lens contamination due to blood or tissue fluid splashes during surgical procedures, severely impairs visual clarity. These degenerations can seriously hinder surgical workflow and pose risks to patient safety. To systematically investigate and address various forms of surgical scene degradation, we introduce a real-world open-source surgical image restoration dataset covering laparoscopic environments, called SurgClean, which involves multi-type image restoration tasks from two medical sites, i.e., desmoking, defogging, and desplashing. SurgClean comprises 3,113 images with diverse degradation types and corresponding paired reference labels. Based on SurgClean, we establish a standardized evaluation benchmark and provide performance for 22 representative generic task-specific image restoration approaches, including 12 generic and 10 task-specific image restoration approaches. Experimental results reveal substantial performance gaps relative to clinical requirements, highlighting a critical opportunity for algorithm advancements in intelligent surgical restoration. Furthermore, we explore the degradation discrepancies between surgical and natural scenes from structural perception and semantic understanding perspectives, providing fundamental insights for domain-specific image restoration research. Our work aims to empower restoration algorithms and improve the efficiency of clinical procedures. Sources can be available at: https://github.com/PJLallen/Surgical-Image-Restoration.
DRAMA: Next-Gen Dynamic Orchestration for Resilient Multi-Agent Ecosystems in Flux
Authors: Xinkui Zhao; Yifan Zhang; Sai Liu; Naibo Wang; Guanjie Cheng; Yueshen Xu; Chang Liu; Shuiguang Deng; Jianwei Yin
Venue: CVPR 2026 (main)

Abstract: Embodied Multi-Agent Systems have proven highly effective in addressing complex tasks through coordinated collaboration among heterogeneous agents. However, real-world environments and task specifications are inherently dynamic, exhibiting frequent changes, uncertainty, and variability. Despite these characteristics, most existing frameworks employ static architectures with fixed agent capabilities and rigid task allocation strategies, which substantially constrain their adaptability to evolving conditions. This inflexibility presents significant challenges to maintaining robust and efficient multi-agent cooperation in dynamic and unpredictable settings.To address these limitations, we propose DRAMA, short for Dynamic Orchestration for Resilient Multi-Agent Ecosystems, tailored for rapidly changing environments. DRAMA adopts a multilayer architecture that incorporates three principal mechanisms: adaptive scheduling through an affinity-driven mechanism, fault-tolerant continuity via hierarchical trust-chain task takeover, and collective spatial intelligence that consolidates distributed observations for predictive reasoning. Together, these components enable event-triggered rescheduling and decentralized fault recovery, ensuring uninterrupted task execution amid agent arrivals, dropouts, or recoveries. Extensive experiments in the embodied VirtualHome-Social environment demonstrate that DRAMA achieves a 7% improvement in runtime efficiency and a 10% increase in throughput compared with state-of-the-art baselines, while maintaining superior stability and robustness under dynamic agent populations.
EagleVision: A Dual-Stage Framework with BEV-grounding-based Chain-of-Thought for Spatial Intelligence
Authors: Jiaxu Wan; Xu Wang; Mengwei Xie; Hang Zhang; Mu Xu; Yang Han; Ding Yuan; Hong Zhang; Yifan Yang
Venue: CVPR 2026 (main)

Abstract: Video-based spatial reasoning -- such as estimating distances, judging directions, or understanding layouts from multiple views -- requires selecting informative frames and, when needed, actively seeking additional viewpoints during inference. Existing multimodal large language models (MLLMs) consume a fixed set of uniformly sampled frames and cannot request new views once reasoning begins, often missing the geometric cues necessary for reliable spatial judgments. We present EagleVision, a dual-stage framework that combines geometry-aware frame selection with active, Bird's-Eye-View (BEV)-grounded reasoning. In the first stage (macro perception), a semantics-perspective-fusion determinantal point process (SPF-DPP) selects a compact set of keyframes that jointly maximize semantic relevance and viewpoint diversity under a fixed token budget. In the second stage (micro verification), the model performs iterative spatial Chain-of-Thought: at each step it can either reason in text or predict a pose on the BEV plane to retrieve the nearest real frame, forming a closed-loop hypothesize-look-verify cycle. The querying policy is trained purely via reinforcement learning with a spatial grounding reward, requiring no human-annotated reasoning traces. On VSI-Bench and SQA3D, EagleVision achieves state-of-the-art performance among open-source vision-language models.
OneStory: Coherent Multi-Shot Video Generation with Adaptive Memory
Authors: Zhaochong An; Menglin Jia; Haonan Qiu; Zijian Zhou; Xiaoke Huang; Zhiheng Liu; Weiming Ren; Kumara Kahatapitiya; Ding Liu; Sen He; Chenyang Zhang; Tao Xiang; Fanny Yang; Serge Belongie; Tian Xie
Venue: CVPR 2026 (main)

Abstract: Storytelling in real-world videos often unfolds through multiple shots--discontinuous yet semantically connected clips that together convey a coherent narrative. However, existing multi-shot video generation (MSV) methods struggle to effectively model long-range cross-shot context, as they rely on limited temporal windows or single keyframe conditioning, leading to degraded performance under complex narratives. In this work, we propose OneStory, achieving global yet compact cross-shot context modeling for consistent and scalable narrative generation. OneStory reformulates MSV as a next-shot generation task, enabling shot-by-shot autoregressive synthesis while leveraging pretrained image-to-video (I2V) models for strong visual conditioning. We introduce two key modules: a Frame Selection module that constructs a semantically-relevant global memory based on informative frames from prior shots, and an Adaptive Conditioner that performs importance-guided patchification to generate compact context for direct conditioning. We further curate a high-quality multi-shot dataset with referential captions to mirror real-world storytelling patterns, and design effective training strategies under the next-shot paradigm. Finetuned from a pretrained I2V model on our curated 60K dataset, OneStory achieves state-of-the-art narrative coherence across diverse and complex scenes in both text- and image-conditioned settings, enabling controllable and immersive long-form video storytelling. See our Project Page for more details.
Tavatar: Topology-Aware Gaussian Attribute Derivation for Animatable Human Avatars
Authors: Hailin Luo; Yifan Yang; Jiazhi Shu; Zixiong Huang; Qi Chen; Qing Du; Mingkui Tan
Venue: CVPR 2026 (main)

Abstract: Reconstructing high-fidelity, animatable human avatars from monocular videos remains a critical challenge. Existing 3DGS-based human animation methods constrain Gaussian parameters but exclude scale, which we argue is crucial for adapting human poses to challenging out-of-distribution poses. To achieve robust animation under unseen poses, we propose Tavatar, which derives key parameters such as scale, rotation, and other geometric attributes directly from the local mesh geometry, instead of learning them through unconstrained optimization. This paradigm shift enforces topological consistency by design, as each Gaussian is analytically anchored to the local mesh geometry, inheriting its spatial structure and deformation behavior. Specifically, we bind Gaussians to mesh faces and vertices, deriving their scales and orientations from triangle properties and local edge lengths to ensure coherent surface coverage. To ensure the stability of this analytical mapping, we introduce a crucial equilateral regularization term that preserves mesh integrity. Extensive experiments demonstrate that Tavatar achieves superior animation robustness on challenging out-of-distribution poses, reducing normal error by 13.8% on X-Avatar and 17.9% on PeopleSnapshot against the best baseline, while maintaining competitive rendering quality.
From Weights to Concepts: Data-Free Interpretability of CLIP via Singular Vector Decomposition
Authors: Francesco Gentile; Nicola Dall'Asen; Francesco Tonini; Massimiliano Mancini; Lorenzo Vaquero; Elisa Ricci
Venue: CVPR 2026 (main)

Abstract: As vision-language models are deployed at scale, understanding their internal mechanisms becomes increasingly critical. Existing interpretability methods predominantly rely on activations, making them dataset-dependent, vulnerable to data bias, and often restricted to coarse head-level explanations. We introduce SITH (Semantic Inspection of Transformer Heads), a fully data-free, training-free framework that directly analyzes CLIP's vision transformer in weight space. For each attention head, we decompose its value-output matrix into singular vectors and interpret each one via COMP (Coherent Orthogonal Matching Pursuit), a new algorithm that explains them as sparse, semantically coherent combinations of human-interpretable concepts. We show that SITH yields coherent, faithful intra-head explanations, validated through reconstruction fidelity and interpretability experiments. This allows us to use SITH for precise, interpretable weight-space model edits that amplify or suppress specific concepts, improving downstream performance without retraining. Furthermore, we use SITH to study model adaptation, showing how fine-tuning primarily reweights a stable semantic basis rather than learning entirely new features.
OMoBlur: An Object Motion Blur Dataset and Benchmark for Real-World Local Motion Deblurring
Authors: Dingchuan Yu; Jiatong Li; Jingwen Zhou; Zhengyue Zhuge; Yueting Chen; Qi Li
Venue: CVPR 2026 (main)

Abstract: Object motion blur in static scenes is spatially heterogeneous, differing from conventional deblurring problems yet frequently occurring in real handheld capture scenarios. Existing datasets either rely on costly beam-splitting capture with residual misalignment or employ synthetic blur that fails to model the continuous photon-integration process during exposure. To overcome these limitations, we introduce OMoBlur, a physically grounded dataset that emulates realistic exposure integration via programmable sensor control, ensuring close alignment between synthetic and real blur distributions. OMoBlur provides 20,000 blur-sharp-mask pairs covering diverse object motion types. Leveraging this dataset, we further propose OMDNet, an object-motion-aware deblurring network that integrates a Motion-Appearance Extract Block, a Flow-Guided Gate Predictor, and an Adaptive Gated Fusion mechanism. This design enables the network to selectively restore blurred regions while preserving static backgrounds, without requiring pixel-accurate mask annotations. Extensive experiments demonstrate that OMoBlur's physically faithful data collection and large-scale diversity significantly enhance the network's generalization to real-world motion blur, establishing OMoBlur and OMDNet as a robust benchmark and practical solution for local motion deblurring. The dataset and code is publicly released at https://yudingchuan.github.io/OMoBlur_homepage/.