Computer Vision
Relightable Gaussian Splatting for Virtual Production Using Image-Based Illumination
Adrian Azzarelli, Nantheera Anantrasirichai, James Pollock et al. (+1 more)
Virtual production (VP) use LED walls to provide both background imagery and image-based lighting. While this enables on-set compositing, it couples lighting to background and scene appearance, limiting flexibility for downstream editing. In addition, inverse rendering conventionally relies on physically-based rendering to estimates 3D geometry and lighting, using environment maps. However, these maps are typically low-resolution and assume far-field lighting. In VP, with near-field and high-resolution image-based lighting, this can lead to inaccuracies and introduce complexities when editing. Addressing this, we propose a VP-specific framework for 3D reconstruction and relighting using Gaussian Splatting. This uses the known background imagery to condition the relighting process. This avoids relying on environment maps and reduces compositing to a background-image editing task. To realize our framework, we introduce a process (and associated
dataset) that captures real VP scenes under varying background content and illumination conditions. This data is used to decompose a 3D scene into fixed appearance and variable lighting components. The variable lighting process simulates light transport by parameterizing each primitive with a UV coordinate, intensity value and resolution modifier. Using mipmaps, these directly sample the background texture in image space - implicitly capturing reflections and refractions without physically-based rendering. Combined with the fixed appearance component, this allows us to render relit scenes using a Gaussian Splatting rasterizer. Compared to baselines, our approach achieves higher-quality 3D reconstruction and controllable relighting. The method is efficient (<3 GB RAM, <5 GB VRAM, <2 hours training, ~35 FPS) and supports rendering useful arbitrary output variables including depth, lighting intensity, lighting color, and unlit renders.
Read the full abstract ▾
All authors Adrian Azzarelli, Nantheera Anantrasirichai, James Pollock, David R. Bull
Computer Vision
The Direct Integration Theorem: A Rigorous Framework for Consistent Discrete Solutions of the Inverse Radon Problem
Mikhail G. Mozerov
This paper presents a novel Direct Integration
Theorem (DIT), derived as a non-trivial corollary of the classical Central Slice
Theorem (CST). The DIT provides a mathematically consistent transition from the continuous to the discrete domain - a fundamental challenge in computed
tomography - thereby eliminating the need for frequency-domain interpolation without resorting to conventional ramp-filtering. The proposed approach circumvents two principal limitations inherent in traditional methods: (i) the zero-frequency singularity and spectral distortions introduced by the mandatory ramp-filtering step, and (ii) discretization inaccuracies associated with frequency-domain interpolation. Based on the DIT, we develop a rigorous framework for consistent discrete solutions of the inverse Radon problem. Mathematical modeling demonstrates that this approach achieves quasi-exact reconstruction, with errors constrained solely by sampling parameters and grid geometry. Furthermore, while Filtered Back Projection (FBP) inherently distorts the variance of the reconstructed image, the DIT-based
algorithm preserves it. Comparative simulations confirm that the proposed method eliminates common artifacts, such as intensity cupping, and consistently outperforms FBP in terms of PSNR, SSIM, and reprojection fidelity, faithfully restoring the original image's statistical characteristics.
Read the full abstract ▾
Computer Vision
FlashClear: Ultra-Fast Image Content Removal via Efficient Step Distillation and Feature Caching
Yixin Tang, Jiawei Guo, Junxian Li et al. (+6 more)
Recently,
diffusion-based object removal models have achieved impressive results in eliminating objects and their associated visual effects. However, they indiscriminately denoise all
tokens across all timesteps, ignoring that removal usually involves small foreground regions. This strategy introduces substantial computational overhead and prolonged inference times. To overcome this computational burden, we propose a latent discriminator to implement Region-aware
Adversarial Distillation (RAD), yielding a highly efficient few-step model named FlashClear. Furthermore, tailored to few-step
diffusion models, we propose FPAC (Foreground-Prioritized Asymmetric
Attention and Caching), a training-free acceleration strategy. Extensive experiments demonstrate that our framework provides massive acceleration while maintaining or exceeding the performance of our base model, ObjectClear. Notably, on the OBER
benchmark, our FlashClear achieves up to 8.26$\times$ and 122$\times$ speedup over ObjectClear and OmniPaint, respectively, while maintaining high visual quality and fidelity.
Read the full abstract ▾
All authors Yixin Tang, Jiawei Guo, Junxian Li, Zhiteng Li, Jixin Zhao, Bingya Zhang, Chenbo Wang, Yulun Zhang, Shangchen Zhou
Computer Vision
CT-IDP: Segmentation-Derived Quantitative Phenotypes for Interpretable Abdominal CT Disease Classification
Lavsen Dahal and Joseph Y. Lo
In this retrospective multi-institutional study, a quantitative phenotyping framework, CT-IDP (CT Image-Derived Phenotypes) was developed on the MERLIN abdominal CT
benchmark (training, validation, and test sets- 15,175, 5,018, and 5,082 studies, respectively) and externally evaluated on two independent
dataset: Duke-Abdomen (2,000) and AMOS (1,107). Multi-organ
segmentations were generated with TotalSegmentator and used to derive over 900 organ and compartment-level descriptors spanning morphometry, attenuation, and contextual/burden findings. Sparse disease-specific logistic regression with elastic-net regularization was trained on MERLIN and externally validated under a frozen specification. Performance was compared against a DINOv3-based
vision-
transformer baseline using AUC and average precision (AP), supported by phenotype-stratified audits and coefficient-level inspection. Macro-AUC for CT-IDP versus the baseline was 0.897 versus 0.880 on MERLIN, 0.877 versus 0.857 on the Duke-Abdomen
dataset, and 0.780 versus 0.756 on AMOS.
Read the full abstract ▾
Computer Vision
LLaVA-UHD v4: What Makes Efficient Visual Encoding in MLLMs?
Kechen Fang, Yihua Qin, Chongyi Wang et al. (+3 more)
Visual encoding constitutes a major computational bottleneck in
Multimodal Large
Language Models (MLLMs), especially for high-resolution image inputs. The prevailing practice typically adopts global encoding followed by post-ViT compression. Global encoding produces massive
token sequences, while post-ViT compression incurs the full quadratic
attention cost of the ViT before any
token reduction takes place. In this work, we revisit this convention along two dimensions: the encoding strategy and visual
token compression. First, controlled experiments show that slice-based encoding outperforms global encoding across
benchmarks, suggesting that preserving local details through sliced views can be more beneficial than applying global
attention for fine-grained perception. Second, we introduce intra-ViT early compression, which reduces
tokens in shallow ViT layers and substantially lowers visual-encoding FLOPs while preserving downstream performance. By integrating intra-ViT compression into the slice-based encoding framework, we present LLaVA-UHD v4, an efficient and compute-controllable visual encoding scheme tailored for high-resolution inputs. Across a diverse set of
benchmarks covering document understanding, OCR, and general VQA, LLaVA-UHD v4 reduces visual-encoding FLOPs by 55.8% while matching or even surpassing baseline performance. These results suggest that visual-encoding efficiency can be substantially improved without sacrificing downstream performance, providing a practical design direction for efficient high-resolution MLLMs. All model weights and code will be publicly released to support further research.
Read the full abstract ▾
All authors Kechen Fang, Yihua Qin, Chongyi Wang, Wenshuo Ma, Tianyu Yu, Yuan Yao
Computer Vision
Tracking the Truth: Object-Centric Spatio-Temporal Monitoring for Video Large Language Models
Tri Cao, Khoi Le, Thong Nguyen et al. (+7 more)
While
multimodal large
language models (MLLMs) have advanced
video understanding, they remain highly prone to
hallucinations in dynamic scenes. We argue this stems from a failure in spatio-temporal monitoring, the ability to persistently track object identities, states, and relations over time. Existing
benchmarks obscure this deficit by relying on single final-answer evaluations for queries that can often be resolved via local visual cues or statistical priors. To rigorously diagnose this, we introduce STEMO-Bench (Spatio-TEmporal MOnitoring), a
benchmark of human-verified object-centric facts that evaluates intermediate
reasoning by decomposing queries into sub-questions, distinguishing genuine temporal understanding from coincidental correctness. To address failure modes exposed by STEMO, we propose STEMO-Track, a novel object-centric framework that explicitly constructs and reasons over structured object trajectories via chunk-wise state extraction and temporal aggregation. Extensive experiments demonstrate that our object-centric framework significantly reduces hallucinated answers and improves spatio-temporal
reasoning consistency over state-of-the-art MLLMs.
Read the full abstract ▾
All authors Tri Cao, Khoi Le, Thong Nguyen, Cong-Duy Nguyen, Quynh Vo, Anh Tuan Luu, Chunyan Miao, See-Kiong Ng, Shuicheng Yan, Bryan Hooi
Computer Vision
Extrusion Segmentation Strategy to improve CAD Reconstruction from Point Cloud
Said Harb, Mehdi Maboudi and Markus Gerke
Computer-Aided Design is ubiquitous in todays world, as almost every manufactured object begins as a digital model across industries. At the same time, advances in 3D sensing have made point clouds a dominant form of raw 3D data. Recovering the CAD model of a physical object from its point cloud scan has two major applications: reverse engineering, where physical or hand-crafted prototypes need to be reconstructed automatically as editable digital models, and quality control, where recovering the CAD description of a manufactured object helps quantify and understand deviations introduced during the production process. Thus, converting unordered point clouds into structured CAD models is increasingly important for modern applications. Deep learning has enabled major progress in computer
vision for both 2D and 3D data, and new
datasets facilitate data-driven CAD reconstruction. Building on this foundation, we develop an end-to-end model that reconstructs CAD models from point clouds and introduce a
segmentation approach that decomposes them into individual extrusions. These partial shapes increase data diversity, improving the generalization and robustness of deep learning models. Our strategy thereby provides a simple, yet effective way to increase reconstruction performance of deep learning models.
Read the full abstract ▾
Computer Vision
Can MLLMs Reason About Visual Persuasion? Evaluating the Efficacy and Faithfulness of Reasoning
Naeun Lee, Hyunjong Kim, Sunghwan Choi et al. (+2 more)
Persuasive visuals play a central role in advertising, public communication, and online media, making it increasingly important to understand whether an image is persuasive and why. However, current
Multimodal Large
Language Models (MLLMs) have limited ability to reason about visual persuasion. Our analysis reveals that models often rely on a shortcut—selectively citing easily recognizable visual elements and treating their mere presence as evidence of persuasiveness—rather than
reasoning over the diverse visual cues in an image to determine how and why they contribute to persuasiveness. To address this limitation, we propose (1) a fine-tuning approach that trains models on rationales reflecting diverse perspectives on visual persuasiveness, and (2) an evaluation framework that measures the faithfulness of models' rationales through three complementary metrics. Fine-tuning on multi-perspective rationales improves persuasiveness prediction and
reasoning effectiveness. However, our evaluation framework reveals a discrepancy between prediction performance and rationale faithfulness, showing that higher prediction performance does not necessarily correspond to more faithful
reasoning. These findings highlight the need to evaluate effectiveness and faithfulness separately and inform future directions for improving both the training and evaluation of visual persuasion
reasoning.
Read the full abstract ▾
All authors Naeun Lee, Hyunjong Kim, Sunghwan Choi, Injin Kong, Yohan Jo
Computer Vision
FugSeg: Fast Uncertainty-aware Ground Segmentation for 3D Point Cloud
Yu Li and Volker Schwieger
In
LiDAR-based environment perception systems, ground
segmentation is a key preprocessing step supporting various applications such as mapping and
navigation. Although extensively studied, problems such as reflection noise and isolated ground remain challenging. To address these issues, we propose FugSeg, a fast uncertainty-aware ground
segmentation method. A polar grid map is adopted as the point cloud representation to ensure generalizability across
LiDAR types. Building on that, we develop a within- and cross-segment ground labeling strategy that identifies not only directly visible ground
cells but also those that are isolated or occluded. During this process, an adaptive slope is introduced, which incorporates measurement uncertainties to enhance its reliability under complex terrain. Finally, to achieve point-level ground
segmentation, a fine-grained ground elevation estimation method is introduced. Throughout the complete workflow, reflection noise is explicitly handled via the proposed noisy ground
cells. We conduct comprehensive evaluations on four public
datasets covering both structured and unstructured environments. Results show that FugSeg outperforms state-of-the-art non-learning methods, achieving the highest F1, accuracy, and mIoU across all
datasets, while maintaining the fastest runtime (135 Hz and 487 Hz for 64- and 32-layer
LiDARs) using a single CPU thread, making it suitable for resource-limited systems. The code will be available at https://github.com/Leo-YuLi/FugSeg.
Read the full abstract ▾
Computer Vision
MLCR: Multi-Level Cue Refinement for Long-Term Multimodal Action Quality Assessment
Qiqi Li, Pengfei Wang, Hongyu Chen et al. (+1 more)
Long-term
multimodal action quality assessment (AQA) evaluates action execution in several-minute audiovisual sequences by mining discriminative quality cues for score prediction. Existing
multimodal methods usually model entire sequences with a single temporal encoder and fuse modality features by direct
alignment or concatenation, causing key cues to be obscured by global trends, weakened by modal redundancy, and distorted during one-shot score mapping. To address this issue, we reformulate long-term
multimodal AQA as a quality cue organization problem and propose MLCR, a multi-level cue refinement framework. MLCR organizes quality evidence at three levels: intra-modal representation, cross-modal interaction, and stage-wise aggregation. Specifically, the intra-modal decoupling encoder (IMDE) preserves modality identity while refining global temporal context and local frequency details. The cross-modal dynamic complementarity-aware
retrieval (CMDCR) module retrieves incremental evidence conditioned on the evolving fused state and suppresses redundant responses. The stage-wise
multimodal integration (SMI) block progressively accumulates intra-modal and cross-modal cues to refine the fused representation. Experiments on the Rhythmic Gymnastics and Fis-V
datasets show that MLCR achieves the best or second-best performance in both Spearman correlation and prediction error, demonstrating its effectiveness and robustness.
Read the full abstract ▾
All authors Qiqi Li, Pengfei Wang, Hongyu Chen, Nenggan Zheng
Computer Vision
ClickSeg3D: Few-Click Interactive Segmentation via Semantic Embeddings
Xueyang Kang, Zijian Yu, Kourosh Khoshelham et al. (+1 more)
Interactive
segmentation allows efficient label generation by leveraging user-provided clicks to progressively refine predictions, which is critical when fully supervised labels are costly or generalization to unseen classes is needed. Existing 3D interactive methods are limited: most operate sequentially, predicting only one object per iteration with binary masks, while several recent approaches depend on 2D foundation models and camera
alignment to bridge the 2D-3D gap. To address these limitations, we propose a novel interactive
segmentation framework that operates directly on sparse, randomly downsampled 3D points and processes multiple object clicks in a single forward pass. Our framework consists of a point
Transformer-based encoder and a hierarchical mask decoder, which integrates multi-level crop-and-merge operations conditioned on learnable semantic
embeddings. Unlike prior interactive approaches that require repeated model updates after each manually corrective click, our method jointly reasons over all click queries, modeling inter-instance relationships and refining both spatial masks and semantic predictions through spatial and semantic
embeddings. Extensive experiments demonstrate that our model improves the mIoU metric by over 20 percent compared to strong baselines and achieves 8-10 percent gains under cross-
dataset evaluation for a one-click per instance setting, often requiring only a single click per object. Our approach provides a generalizable and efficient solution for interactive 3D instance
segmentation, particularly suitable for real-time applications such as robotic manipulation,
navigation, and rapid 3D semantic annotation.
Read the full abstract ▾
All authors Xueyang Kang, Zijian Yu, Kourosh Khoshelham, Liangliang Nan
Computer Vision
Unified Modeling of Lane and Lane Topology for Driving Scene Reasoning
Han Li, Yulu Gao, Si Liu et al. (+3 more)
Autonomous vehicles need to perceive not only physical elements in the driving scene, such as lane lines and traffic lights, but also logical elements like lane centerlines and their
topology. Existing lane
topology reasoning methods typically follow a
reasoning-by-detection paradigm, where lane topological relationships are primarily derived from lane detection results. In this paper, we propose an innovative method called Unified Modeling of Lane and Lane
Topology (UniTopo), which represents the topological relationships between lanes as connected lanes, encompassing predecessor lanes, successor lanes, and their interconnections. This unified representation of lanes and lane
topology allows us to simultaneously obtain both the positions and topological information of lanes within a shared perception pipeline, establishing a new paradigm for directly perceiving lane
topology from original image features. We validate our method on the driving scene
reasoning benchmark OpenLane-V2, which consists of two subsets, built based on Argoverse2 and nuScenes, respectively. Our method achieves TOP_ll of 30.1% and 31.8% on the two subsets, significantly surpassing the existing state-of-the-art method T^2SG by 6.0% and 8.6%.
Read the full abstract ▾
All authors Han Li, Yulu Gao, Si Liu, Yuhang Wang, Bo Liu, Beipeng Mu
Computer Vision
DAPE: Dynamic Non-uniform Alignment and Progressive Detail Enhancement Techniques for Improving the Performance of Efficient Visual Language Models
Mengyuan Tian, Qiyan Zhao, Yanan Wang et al. (+1 more)
In recent years, pre-trained visual-linguistic models have demonstrated tremendous potential, becoming a crucial foundational framework for numerous downstream tasks. However, the information density between text and images is not uniformly distributed. Existing methods often overlook the inherent and dynamic differences in information density and semantic scope between text tags and image blocks. These common uniform
alignment strategies result in coarse-grained cross-modal interactions and loss of fine semantic details. Moreover, pursuing finer
alignment typically requires substantial computational overhead, limiting practical model deployment. To address this challenge, this paper proposes a novel framework for dynamic cross-modal
alignment with continuous detail introduction. First, we design a dynamically adaptive cross-modal matching mechanism that uses a learnable matching function to dynamically assign varying numbers and sizes of image tags to text tags of the same size but different information density, enabling more precise
attention interaction. Second, we develop a continuous detail introduction module to progressively incorporate high-resolution visual feature enhancement into the
alignment process. Extensive experiments across multiple
benchmarks demonstrate significant improvements in the accuracy of various downstream tasks while reducing computational overhead.
Read the full abstract ▾
All authors Mengyuan Tian, Qiyan Zhao, Yanan Wang, Da-Han Wang
Computer Vision · Cross-listed from cs.CL
DocScope: Benchmarking Verifiable Reasoning for Trustworthy Long-Document Understanding
Xiang Feng, Jiawei Zhou, Zhangfeng Huang et al. (+6 more)
Evaluating whether
Multimodal Large
Language Models can produce trustworthy, verifiable
reasoning over long, visually rich documents requires evaluation beyond end-to-end answer accuracy. We introduce DocScope, a
benchmark that formulates long-document QA as a structured
reasoning trajectory prediction problem: given a complete PDF document and a question, the model outputs evidence pages, supporting evidence regions, relevant factual statements, and a final answer. We design a four-stage evaluation protocol – Page Localization, Region Grounding, Fact Extraction, and Answer Verification – that audits each level of the trajectory independently through inter-stage decoupling, with all judges selected and calibrated via human
alignment studies. DocScope comprises 1,124 questions derived from 273 documents, with all hierarchical evidence annotations completed by human annotators. We
benchmark 6 proprietary models, 12 open-weight models, and several domain-specific systems. Our experiments reveal that answer accuracy cannot substitute for trajectory-level evaluation: even among correct answers, the highest observed rate of complete evidence chains is only 29%. Across all models, region grounding remains the weakest trajectory stage. Furthermore, the primary difficulty stems from aggregating evidence dispersed across long distances and multiple document clusters, while an oracle study identifies faithful perception and fact extraction as the dominant capability bottleneck. Cross-architecture comparisons further suggest that activated parameter count matters more than total scale. The
benchmark and code will be publicly released at https://github.com/MiliLab/DocScope.
Read the full abstract ▾
All authors Xiang Feng, Jiawei Zhou, Zhangfeng Huang, Kewei Wang, Shanshan Ye, Jinxin Hu, Zulong Chen, Yong Luo, Jing Zhang
Computer Vision
Hoang M. Truong, Hai Nguyen-Truong and Dang Huynh
Open-vocabulary semantic
segmentation requires adapting image-level
vision-
language models such as CLIP to dense pixel-level prediction, which is challenging due to the mismatch between hierarchical structure and semantic
alignment in the
embedding space. While recent works leverage
hyperbolic geometry to model hierarchical relationships, they align
embeddings across hierarchical levels but overlook semantic misalignment among
embeddings within the same level. In this work, we propose HyRo, a
hyperbolic fine-tuning framework that decouples hierarchical and semantic
alignment in the Poincaré ball model. HyRo aligns hierarchical levels by adjusting the
hyperbolic radius and refines semantic relationships through angular
alignment using an orthogonal transformation that theoretically preserves the
hyperbolic radius. Experiments on standard open-vocabulary semantic
segmentation benchmarks demonstrate that HyRo achieves state-of-the-art performance over prior methods.
Read the full abstract ▾
Computer Vision
ProDG: Prototypes for Data-Free Generative Post-Hoc Explainability
Piotr Borycki, Magdalena Trędowicz, Jacek Tabor et al. (+2 more)
Ante-hoc interpretability methods based on prototypes provide highly accurate explanations by utilizing the intuitive "this looks like that"
reasoning paradigm. On the other hand, post-hoc models can explain predictions for a single image without relying on an underlying
dataset or requiring costly neural network retraining. Recent approaches successfully solve the retraining problem for prototype-based networks. However, they still face a fundamental limitation: they require access to a subset of data (e.g., a test or validation set) to search for and extract the visual prototypes. In this paper, we address this issue and introduce ProDG: Generative Prototypes for Data-Free Post-Hoc Explainability, a novel framework that leverages generative models to synthesize pure, high-fidelity prototypes directly from the frozen model's weights, completely eliminating the dependency on any external data. By establishing this new frontier in Data-Free XAI, ProDG unlocks robust visual interpretability for
privacy-sensitive domains, where original data is strictly restricted or fundamentally inaccessible. Project page: https://github.com/piotr310100/ProDG
Read the full abstract ▾
All authors Piotr Borycki, Magdalena Trędowicz, Jacek Tabor, Łukasz Struski, Przemysław Spurek
Computer Vision
Restoration-Aligned Generative Flow Models for Blind Motion Deblurring
Insoo Kim and Jinwoo Shin
Generative flow models offer powerful priors learned from large-scale natural images, but directly adapting them to restoration tasks such as motion deblurring causes severe fidelity degradation, as their training objective is inherently misaligned with restoration. We present DeblurFlow, a framework that resolves this misalignment by reformulating the flow trajectory itself: we replace the noise endpoint with the blur observation, which makes the underlying vector field coincide with the residual error between blur and clean images. Under this formulation, the standard flow matching loss naturally takes the form of a residual loss, allowing pretrained flow models to be optimized under restoration-aligned objectives via LoRA adaptation. This formulation further enables a dual-expert sampling strategy: a fidelity expert provides a high-fidelity initialization, e.g., PSNR 33.69 dB, and DeblurFlow enhances perceptual quality with only a marginal fidelity reduction to 33.05 dB, whereas directly applying a generative model on top of a fidelity expert decreases PSNR to 27.60 dB. To make this practical, we further introduce r-space, a latent space tailored for residual decoding rather than image reconstruction, which reduces encoder-decoder cost by up to 9$\times$over standard VAE latents. Extensive experiments on GoPro, HIDE, RealBlur, and RWBI demonstrate that DeblurFlow achieves strong restoration fidelity and perceptual realism, while remaining computationally practical.
Read the full abstract ▾
Computer Vision
Geometrically Constrained Stenosis Editing in Coronary Angiography via Entropic Optimal Transport
Jialin Li, Zhuo Zhang, Yue Cao et al. (+4 more)
The scarcity of high-quality imaging data for coronary angiography (CAG) stenosis limits the clinical translation of automated stenosis detection. Synthetic stenosis data provides a practical avenue to augment training sets, improving data quality, diversity, and distributional coverage, and enhancing detection precision and generalization. However,
diffusion-based editing commonly relies on soft guidance in a noise-initialized reverse process, offering limited pixel-level precision and structure preservation. We propose the OT-Bridge Editor, which reframes localized editing as a constrained entropic optimal transport (OT) problem and leverages
geometric information to steer the generation path, enabling stronger
geometric control. Extensive experiments show that our synthesized angiograms consistently improve downstream stenosis detection, yielding substantial relative gains of 27.8% on the public ARCADE
benchmark and 23.0% on our multi-center
dataset, supported by consistent qualitative results.
Read the full abstract ▾
All authors Jialin Li, Zhuo Zhang, Yue Cao, Guipeng Lan, Jiabao Wen, Shuai Xiao, Jiachen Yang
Computer Vision
Illusion-Aware Visual Preprocessing and Anti-Illusion Prompting for Classic Illusion Understanding in Vision-Language Models
Junli Zha, Jiahui Wang, Xinkai Lu et al. (+1 more)
Vision-
Language Models (VLMs) exhibit systematic bias toward visual illusions, recalling memorized facts rather than perceiving actual visual differences. This paper presents a training-free framework for the 5th DataCV Challenge Task 1 at CVPR 2026, addressing this perception-versus-
memory conflict through three complementary strategies:(1) illusion-aware image preprocessing that weakens illusion-inducing context via type-specific transformations (edge extraction, color isolation, morphological processing, and reference-line overlay), (2) anti-illusion
prompt engineering guiding VLMs toward qualitative visual comparison, and (3) multi-vote ensemble that further improves robustness. Our method achieves 90.48% accuracy on the official 630-image test set using Claude (claude-opus-4-6) with 5-vote majority ensemble, and 98.41% on a human-verified subset. The approach requires no finetuning, relying solely on visual manipulation and
prompt design. Our solution secured 2nd place in the challenge, only 0.47% behind the 1st-place solution. Code is available at https://github.com/jasminezz/sf-illusion-aware-vlm.git.
Read the full abstract ▾
All authors Junli Zha, Jiahui Wang, Xinkai Lu, Jinbo Wang
Computer Vision
Cross-Sample Relational Fusion: Unifying Domain Generalization and Class-Incremental Learning
Zhen-Hao Xie, Yan Wang, Hao Sun et al. (+3 more)
Class-Incremental Learning (CIL) requires a learning system to learn new classes while retaining previously learned knowledge. However, in real-world scenarios such as autonomous driving, a system trained on urban roads in sunny weather may later need to operate in rural or highway environments with different traffic patterns and weather conditions. This requires the model not only to overcome catastrophic forgetting, but also to effectively handle domain shifts. In this paper, we propose CrOss-sample Relational Fusion (CORF), a unified framework to address domain shift and catastrophic forgetting simultaneously. To enhance generalizability, we perform selective refinement of training samples by leveraging spatial contribution maps to highlight semantically informative regions. Furthermore, we incorporate predictive confidence to adaptively weigh samples, thereby facilitating the learning of domain-agnostic representations. To alleviate forgetting, we propose a cascaded
distillation framework that captures cross-sample relational dependencies across multiple feature hierarchies, enabling multi-grained knowledge transfer from previous tasks. CORF can be seamlessly integrated into existing CIL
algorithms to enhance their generalizability, achieving competitive performance across various
benchmark datasets. Code is available at https://github.com/LAMDA-CL/TMM26-CORF .
Read the full abstract ▾
All authors Zhen-Hao Xie, Yan Wang, Hao Sun, Han-Jia Ye, De-Chuan Zhan, Da-Wei Zhou
Computer Vision
VECTOR-Drive: Tightly Coupled Vision-Language and Trajectory Expert Routing for End-to-End Autonomous Driving
Rui Zhao, Jianlin Yu, Zhenhai Gao et al. (+2 more)
End-to-end autonomous driving requires models to understand traffic scenes, infer driving intent, and generate executable motion plans. Recent
vision-
language-action (VLA) models inherit semantic priors from large-scale
vision-
language pretraining, yet still face a coupling trade-off: fully shared backbones preserve
multimodal interaction but may entangle
language reasoning and trajectory prediction, whereas decou pled
reasoning-action pipelines reduce task conflict but weaken semantic-motion coupling. We propose VECTOR-DRIVE, a tightly coupled VLA framework built on Qwen2.5-VL-3B. VECTOR-DRIVE keeps all
tokens coupled through shared self
attention and routes feed-forward computation according to
token semantics.
Vision and
language tokens are processed by a
Vision-
Language Expert to preserve semantic priors, while target-point, ego-state, and noisy action
tokens are routed to a Trajectory Expert for motion-specific computation. On the action-
token pathway, a flow-matching planner refines noisy action
tokens into future waypoints and speed profiles. This design couples semantic
reasoning and motion planning within a single
multimodal Transformer while separating task-specific FFN computation. On Bench2Drive, VECTOR-DRIVE achieves 88.91 Driving Score and outperforms representative end-to end and VLA-based baselines. Qualitative results and ablations further validate the benefits of shared
attention, semantic-aware expert routing, progressive training, and flow-based action de coding.
Read the full abstract ▾
All authors Rui Zhao, Jianlin Yu, Zhenhai Gao, Jiaqiao Liu, Fei Gao
Computer Vision
Rethinking Event-Based Object Detection through Representation-Level Temporal Aggregation and Model-Level Hypergraph Reasoning
Meisen Wang, Hao Deng, Wei Bao et al. (+5 more)
Event cameras provide microsecond-level temporal resolution, low latency, and high dynamic range, offering potential for perception under fast motion and challenging illumination conditions. However, existing Event-based Object Detection (EOD) methods face limitations at both the representation and model levels: prior event representations usually encode temporal information indirectly through redundant structures, while detection models struggle to explicitly aggregate fragmented event responses into coherent high-order object features. To address these limitations, we present Event Dual Temporal-Relational Aggregation Detector (Ev-DTAD), a unified EOD framework that integrates representation-level temporal encoding with model-level temporal-hypergraph
reasoning. Specifically, we introduce Hierarchical Temporal Aggregation (HTA), a compact three-channel pseudo-RGB representation that explicitly embeds temporal information across intra- and inter-window events. To further enhance detection under sparse and fragmented event responses, we propose Frequency-aware Hypergraph Temporal Fusion (FHTF), which refines multi-scale event features through temporal
evolution modeling and high-order relational
reasoning. Extensive experiments on Gen1 (+0.8 mAP), 1Mpx/Gen4 (+0.5 mAP), and eTraM (+3.0 mAP) demonstrate that Ev-DTAD achieves a competitive accuracy–efficiency trade-off, validating the complementarity between compact temporal representation and temporal-hypergraph feature
reasoning (Fig.~\ref{fig:bubble}). The code is available at: https://github.com/meisenwang/Ev-DTAD.
Read the full abstract ▾
All authors Meisen Wang, Hao Deng, Wei Bao, Chengjie Wang, Zhiqiang Tian, Shaoyi Du, Siqi Li, Yue Gao
Computer Vision · Cross-listed from cs.GR
HairGPT: Strand-as-Language Autoregressive Modeling for Realistic 3D Hairstyle Synthesis
Haimin Luo, Min Ouyang, Lan Xu et al. (+1 more)
Hair is a rich medium of visual and cultural expression, yet its digital modeling remains challenging due to the duality of fluidity and structure. Many existing generative approaches rely primarily on continuous
diffusion fields, which entangle global
topology with local texture and obscure the semantic and structural organization of hairstyles. To address this, we propose HairGPT, a strand-centric framework that treats strands as generative primitives and formulates realistic 3D hairstyle synthesis as a dual-decoupled autoregressive sequence modeling problem. Our method applies spatial decoupling across semantic scalp regions and structural decoupling along a hierarchical strand representation, progressing from global layout to fine-grained style. We further introduce a
geometric tokenizer and region-aware semantic annotations to guide strand-level generation, enabling compositional editing, synthesis of rare and complex hairstyles, and adaptation to stylized domains. By aligning generative modeling with the workflow of digital grooming, HairGPT turns hair generation from opaque texture synthesis into a structured and semantically controllable authoring process, supporting robust semantic conditioning and high-fidelity results across realistic and stylized domains. Project Page: https://haiminluo.github.io/hairgpt/
Read the full abstract ▾
All authors Haimin Luo, Min Ouyang, Lan Xu, Jingyi Yu
Computer Vision
FraudBench: A Multimodal Benchmark for Detecting AI-Generated Fraudulent Refund Evidence
Xinyu Yan, Boyang Chen, Jiaming Zhang et al. (+12 more)
Artificial Intelligence (AI)-generated images have become increasingly realistic and readily adaptable to real-world claims, creating new challenges for verifying visual evidence. A concrete emerging risk is AI-generated refund fraud, in which manipulated or synthetic images are used to support claims about damaged products, poor delivery conditions, or service-related defects. Existing AI-generated image detection
benchmarks mainly evaluate standalone authenticity classification, cross-generator transfer, or forensic localization, leaving claim-conditioned fraudulent evidence detection underexplored. To bridge this gap, we introduce FraudBench, a
multimodal benchmark for detecting AI-generated fraudulent refund evidence. FraudBench is constructed from real-world user-review evidence across e-commerce, food delivery, and travel-service scenarios. We curate real evidence images together with their associated review and product metadata, identify genuine damaged and undamaged evidence through MLLM-assisted filtering and human annotation, and synthesize fake-damaged evidence from genuine undamaged reference images using 12 state-of-the-art image editing and generation models. Using FraudBench, we evaluate MLLMs, specialized AI-generated image detectors, and human evaluators on the same evaluation images. Experiments show that current MLLMs often recognize real-damaged evidence but fail on many fake-damaged subsets, with fake-damage detection rates (TPR) far below the 50% baseline on most generator subsets. Specialized detectors generally perform better but remain inconsistent across generators and can produce false positives on real-damaged samples, revealing a clear gap between generic AI image detection and reliable claim-conditioned refund-evidence verification.
Read the full abstract ▾
All authors Xinyu Yan, Boyang Chen, Jiaming Zhang, Tiantong Wu, Hong Xi Tae, Yurong Hao, Yachun Mi, Tiantong Wang, Yichen He, Yilei Zhao, Lei Xiao, Longtao Huang, Pengjun Xie, Wei Liu, Wei Yang Bryan Lim
Computer Vision
From pre-training to downstream performance: Does domain-specific pre-training make sense?
Felix Krones
Deep learning techniques have revolutionised medical imaging, improving diagnostic accuracy and enabling both more accurate and earlier disease detection. However, the relationship between pre-training strategies and downstream performance in medical imaging models requires further exploration. Here, we systematically compare convolutional neural networks and
transformers, examining various pre-training approaches, including supervised and self-supervised learning, as well as different initialisations and data modalities. Models are evaluated on natural images, chest X-rays, chest CT and retina OCT images, considering the effects of matching pre-training data with target modalities. Our findings indicate that only pre-training on data closely matching the target modality significantly improves downstream performance. While self-supervised learning can outperform supervised methods, its effectiveness varies with context. The study underscores the importance of pre-training strategies to enhance the reliability and effectiveness of deep learning models in medical imaging. By addressing these key factors, our research aims to contribute to the development of more accurate and dependable diagnostic tools, ultimately improving patient outcomes in clinical settings.
Read the full abstract ▾
Computer Vision
Zero-Shot Chinese Character Recognition via Global-Local Dual-Branch Alignment and Hierarchical Inference
Wei Cao, Hao Xu and Xiaolei Diao
Chinese character categories are extremely large, and unseen characters frequently arise in open-world scenarios, making zero-shot Chinese character recognition an important yet challenging problem. Existing IDS-based
retrieval methods usually encode a character image and its ideographic description sequence into a single global vector for matching. Although efficient, such holistic
alignment often under-models local component differences. Moreover, directly introducing patch-
token level fine-grained interaction suffers from both the noise of structural operators in IDS and the high cost of full-candidate
retrieval.To address these issues, we propose a Global-Local Hierarchical Perception Network (GL-HPN), which jointly learns global and local representations of character images and IDS sequences within a unified cross-modal
alignment framework. The global branch supports efficient coarse recall, while the local branch improves component-level discrimination through patch-
token interaction. We further introduce a structure filtering mask to suppress structurally meaningful but visually non-entity IDS operators in local similarity aggregation. On top of this, we design a coarse-to-fine hierarchical inference strategy that performs global
retrieval over the full candidate set and local reranking only on Top-$K$ candidates, followed by parameter-free multiplicative fusion of normalized posterior scores. Experimental results show that GL-HPN achieves competitive performance across multiple zero-shot splits, performs especially well under low-resource settings, and substantially reduces the inference cost of large-scale candidate
retrieval.
Read the full abstract ▾
Computer Vision
Curvature-Aware Captioning:Leveraging Geodesic Attention for 3D Scene Understanding
Ziyao He, Yingjie Liu, ZhangYangRui et al. (+3 more)
Accurate 3D scene description is fundamental to robotic
navigation and augmented reality, yet current dense captioning methods face significant limitations in processing sparse point cloud data. % Existing approaches that apply Euclidean
embedding spaces struggle to simultaneously preserve fine-grained local
geometric details and model exponentially growing global semantic hierarchies, leading to either inaccurate localization or disjointed, shallow scene descriptions. % In this work, we propose a novel \textbf{
Curvature-Aware Captioning} framework, integrating novel non-Euclidean geodesic
attention mechanisms, to resolve the localization-contextualization conflict. % Specifically, self-
attention within Oblique space enforces dimensional homogeneity while establishing long-range dependencies. Bidirectional geodesic cross-
attention within Lorentz space models hierarchical semantic relationships across scene instances, enabling simultaneous precision in object localization and coherence in scene descriptions. % Theoretical analysis confirms that the
curvature complementarity between the Oblique
manifold and Lorentz hyperboloid resolves the Euclidean-
hyperbolic conflict, ensuring feature stability via isotropic optimization while preserving inherent hierarchical relationships. Extensive experiments on ScanRefer and Nr3D
benchmarks demonstrate state-of-the-art performance, with significant gains in both localization accuracy and descriptive richness.
Read the full abstract ▾
All authors Ziyao He, Yingjie Liu, ZhangYangRui, Mingsong Chen, Xuan Tang, Xian Wei
Computer Vision
L2A: Learning to Accumulate Pose History for Accurate 3D Human Pose Estimation
Zehua Wang, Changwang Mei, Huaijiang Sun et al. (+2 more)
Existing 2D-3D lifting human pose estimation methods have achieved strong performance. But the utilization of historical pose representations across network depth was overlooked. In current pipelines, information is propagated through fixed residual connections, which restricts effective reuse of early-layer features such as fine-grained spatial structures and short-term motion cues. However, naively incorporating historical features across layers is non-trivial. We further identify that maintaining a consistent representation space across layers is a prerequisite for effective cross-layer feature aggregation. To address this issue, we propose a history-aware framework that enables effective network cross-layer history feature utilization. Specifically, we adopt a spatial-temporal parallel
Transformer backbone to prevent alternating spatial-temporal transformations during sequential processing, thereby maintaining a consistent representation space. Building upon this, we introduce a History Pose Accumulation (HPA) mechanism that adaptively aggregates features from all preceding layers to enhance current representations. Furthermore, we propose a Layer Pose History Aggregation (LPA) module that transforms layer pose features into a compact and structured form, reducing redundancy and enabling more stable aggregation. Extensive experiments demonstrate that our approach achieves state-of-the-art performance on
benchmarks.
Read the full abstract ▾
All authors Zehua Wang, Changwang Mei, Huaijiang Sun, Pengqi Hu, Zhaoyang Yin
Computer Vision
LightAVSeg: Lightweight Audio-Visual Segmentation
Qing Zhong, Guodong Ding, Lingqiao Liu et al. (+3 more)
Audio-Visual
Segmentation (AVS) targets pixel level localization of sounding emitting objects in
videos. However, existing models rely on dense cross-modal
attention with quadratic computational cost, limiting their suitability for resource efficient deployment. Most efficiency oriented methods focus on backbone reduction and overlook the interaction module as the primary bottleneck. This paper proposes LightAVSeg, a lightweight framework that replaces heavy
attention with a decoupled design for semantic filtering and spatial grounding, resulting in interaction costs that scale linearly with spatial resolution. Furthermore, we introduce an auxiliary
alignment loss to enforce semantic consistency during training with zero inference overhead. Extensive experiments demonstrate that LightAVSeg achieves a new state-of-the-art among lightweight methods: with 20.5M parameters ~1/7 of AVSegFormer), it reaches 50.4 mIoU on the MS3
benchmark and enables efficient inference on a mobile processor.
Read the full abstract ▾
All authors Qing Zhong, Guodong Ding, Lingqiao Liu, Zaiwen Feng, Lin Yuanbo Wu, Angela Yao
Computer Vision
CoLVR: Enhancing Exploratory Latent Visual Reasoning via Contrastive Optimization
Ziyang Ding, Linjian Meng, Yiming Wu et al. (+3 more)
Due to the potential for exploratory
reasoning of Latent Visual
Reasoning, recent works tend to enable MLLMs (
Multimodal Large
Language Models) to perform visual
reasoning by propagating continuous hidden states instead of decoding intermediate steps into discrete
tokens. However, existing works typically rely on hard
alignment objectives to force latent representations to match predefined visual features, thereby severely limiting the exploratory of latent
reasoning process. To address this problem, we propose CoLVR (Contrastive Optimization for Latent Visual
Reasoning). To obtain a more exploratory visual
reasoning, CoLVR introduces a latent contrastive training framework. Firstly, CoLVR learns diverse and exploratory representations with a latent contrastive objective guided by angle-based perturbation, which expands the semantic latent space and avoids over-constrained
embedding. Then, CoLVR employs a latent trajectory contrastive reward for RL (Reinforcement Learning) post-training to enable fine-grained optimization of latent visual
reasoning process and thus fostering diverse
reasoning behaviors. Experiments demonstrate that CoLVR significantly enhances the exploratory capability of latent representations, achieving average improvements of 5.83% on VSP and 8.00% on Jigsaw, while also outperforming existing latent models on out of domain
benchmarks, with a 3.40% gain on MMStar. The data, codes, and models are released at https://github.com/Oscar-dzy/CoLVR.
Read the full abstract ▾
All authors Ziyang Ding, Linjian Meng, Yiming Wu, Yuhan Li, Yuhao Liu, Zhen Zhao