Computer Vision
Global Context or Local Detail? Adaptive Visual Grounding for Hallucination Mitigation
Yubo Jiang, Xin Yang, Abudukelimu Wuerkaixi et al. (+7 more)
Vision-
Language Models (VLMs) are frequently undermined by object
hallucination–generating content that contradicts visual reality–due to an over-reliance on linguistic priors. We introduce Positive-and-Negative Decoding (PND), a training-free inference framework that intervenes directly in the decoding process to enforce visual fidelity. PND is motivated by our key finding of a critical
attention deficit in VLMs, where visual features are empirically under-weighted. Our framework corrects this via a dual-path contrast: The positive path amplifies salient visual evidence using multi-layer
attention to encourage faithful descriptions, directly counteracting the
attention deficit. Simultaneously, the negative path identifies and degrades the core object's features to create a strong counterfactual, which penalizes ungrounded, prior-dominant generation. By contrasting the model's outputs from these two perspectives at each step, PND steers generation towards text that is not just linguistically probable, but visually factual. Extensive experiments on
benchmarks like POPE, MME, and CHAIR show that PND achieves state-of-the-art performance with up to 6.5% accuracy improvement, substantially reducing object
hallucination while also enhancing descriptive detail–all without requiring any model retraining. The method generalizes effectively across diverse VLM architectures including LLaVA, InstructBLIP, InternVL, and Qwen-VL.
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All authors Yubo Jiang, Xin Yang, Abudukelimu Wuerkaixi, Zheming Yuan, Xuxin Cheng, Fengying Xie, Zhiguo Jiang, Cao Liu, Ke Zeng, Haopeng Zhang
Computer Vision · Cross-listed from cs.LG
Complexity of Linear Regions in Self-supervised Deep ReLU Networks
Mufhumudzi Muthivhi and Terence L. van Zyl
There has been growing interest in studying the complexity of Rectified Linear Unit (ReLU) based activation networks. Recent work investigates the
evolution of the number of piecewise-linear partitions (linear regions) that are formed during training. However, current research is limited to examining the complexity of models trained in a supervised way. Self-Supervised Learning (SSL) differs in that it directly optimises the representation space using a loss function to enhance the model's performance across multiple downstream tasks. This study investigates the local distribution of linear regions produced by SSL models. We demonstrate that the
evolution of linear regions correlates with the representation quality by utilising SplineCam to extract two-dimensional polytopes near the data distribution. We track the number, area, eccentricity, and boundaries of regions throughout training. The study compares supervised, contrastive, and self-
distillation methods over two standard
benchmark datasets, MNIST and FashionMNIST. The analysis of the experimental results shows that self-supervised methods create substantially fewer regions to achieve comparable accuracy to supervised models. Contrastive methods rapidly expand regions over time, whereas self-
distillation methods tend to consolidate by merging neighbouring regions. Lastly, we can detect representation collapse early within the
geometric space of linear regions. Our analysis suggests that polytopal metrics can serve as reliable indicators of representation quality and model performance.
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Computer Vision
Multispectral airborne laser scanning dataset for tree species classification: MS-ALS-SPECIES
Matti Hyyppä, Klaara Salolahti, Eric Hyyppä et al. (+9 more)
The shift from stand-level to individual-tree-level forest assessments supports improved species mapping and biodiversity monitoring, particularly in boreal ecosystems where tree species like aspen (Populus tremula L.) play a keystone role. Airborne
laser scanning (ALS) is the standard for such inventories, but a major limitation for developing improved species classification methods is the small number of publicly available ALS
datasets containing high-quality, field-validated reference data. Recently, multispectral ALS data has shown promise for tree species classification, but the progress is hindered by the lack of open multispectral ALS
datasets with high-quality field reference data. This paper presents and details an open multispectral ALS
dataset for tree species classification that was used before its public release for an international benchmarking study of machine learning and deep learning classification methods in a related publication by Taher et al.,(2026). The
dataset comprises 6326 segment-level point clouds of individual trees representing nine species in southern Finland. The point cloud data has been acquired using two multispectral
laser scanning systems each operating at three
laser wavelengths: a helicopter-borne system (HeliALS) with a point density exceeding 1000 points\m2 and an Optech Titan system with approximately 35 points\m2. Furthermore, we present a crowdsourcing application that facilitates the collection of high-quality field reference data of tree species in an efficient and scalable manner. Our article showcases the versatility of the open
dataset by presenting new analyses on species classification using multispectral data building upon the initial findings of Taher et al.,(2026).
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All authors Matti Hyyppä, Klaara Salolahti, Eric Hyyppä, Xiaowei Yu, Josef Taher, Leena Matikainen, Matti Lehtomäki, Paula Litkey, Teemu Hakala, Harri Kaartinen, Juha Hyyppä, Antero Kukko
Computer Vision
ARETE: Attention-based Rasterized Encoding for Topology Estimation using HSV-transformed Crowdsourced Vehicle Fleet Data
Daniel Fritz, Dimitrios Lagamtzis, Michael Mink et al. (+2 more)
The continuous advancement of autonomous driving (AD) introduces challenges across multiple disciplines to ensure safe and efficient driving. One such challenge is the generation of High-Definition (HD) maps, which must remain up to date and highly accurate for downstream automotive tasks. One promising approach is the use of crowdsourced data from a vehicle fleet, representing road
topology and lane-level features. This work focuses on the generation of centerlines and lane dividers from crowdsourced vehicle trajectories. We adopt a Detection
Transformer (DETR)-based approach, where a rasterized representation of vehicle trajectories is used as input to predict vectorized lane representations. Each lane consists of a centerline with an associated direction and corresponding lane dividers that are geometrically constrained by the centerline. Our method includes the extraction of local tiles, from which crowdsourced vehicle trajectories are aggregated. Each tile undergoes a transformation into a rasterized representation encoding both the presence and direction of each trajectory, enabling the prediction of vectorized directed lanes. Experiments are conducted on an internal
dataset as well as on the public
datasets nuScenes and nuPlan.
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All authors Daniel Fritz, Dimitrios Lagamtzis, Michael Mink, Markus Enzweiler, Steffen Schober
Computer Vision · Cross-listed from cs.LG
Diffusion Templates: A Unified Plugin Framework for Controllable Diffusion
Zhongjie Duan, Hong Zhang and Yingda Chen
Controllable
diffusion methods have substantially expanded the practical utility of
diffusion models, but they are typically developed as isolated, backbone-specific systems with incompatible training pipelines, parameter formats, and runtime hooks. This fragmentation makes it difficult to reuse infrastructure across tasks, transfer capabilities across backbones, or compose multiple controls within a single generation pipeline. We present
Diffusion Templates, a unified and open plugin framework that decouples base-model inference from controllable capability injection. The framework is organized around three components: Template models that map arbitrary task-specific inputs to an intermediate capability representation, a Template cache that functions as a standardized interface for capability injection, and a Template pipeline that loads, merges, and injects one or more Template caches into the base
diffusion runtime. Because the interface is defined at the systems level rather than tied to a specific control architecture, heterogeneous capability carriers such as KV-Cache and LoRA can be supported under the same abstraction. Based on this design, we build a diverse model zoo spanning structural control, brightness adjustment, color adjustment, image editing, super-resolution, sharpness enhancement, aesthetic
alignment, content reference, local inpainting, and age control. These case studies show that
Diffusion Templates can unify a broad range of controllable generation tasks while preserving modularity, composability, and practical extensibility across rapidly evolving
diffusion backbones. All resources will be open sourced, including code, models, and
datasets.
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Computer Vision · Cross-listed from eess.IV
Semantic Segmentation for Histopathology using Learned Regularization based on Global Proportions
Yangping Li, Thomas Pinetz, Michael Hölzel et al. (+2 more)
In pathology, the spatial distribution and proportions of tissue types are key indicators of disease progression, and are more readily available than fine-grained annotations. However, these assessments are rarely mapped to pixel-wise
segmentation. The task is fundamentally underdetermined, as many spatially distinct
segmentations can satisfy the same global proportions in the absence of pixel-wise constraints. To address this, we introduce Variational
Segmentation from Label Proportions (VSLP), a two-stage framework that infers dense
segmentations from global label proportions, without any pixel-level annotations. This framework first leverages a pre-trained
transformer model with test-time augmentation to produce a pixel-wise confidence estimate. In the second stage, these estimates are fused by solving a variational optimization problem that incorporates a Wasserstein data fidelity term alongside a learned regularizer. Unlike end-to-end networks, our variational method can visualize the fidelity-regularization energy, resulting in more interpretable
segmentation. We validate our approach on two public
datasets, achieving superior performance over existing weakly supervised and unsupervised methods. For one of these
datasets, proportions have been estimated by an experienced pathologist to provide a realistic
benchmark to the community. Furthermore, the method scales to an in-house
dataset with noisy pathologist labels, severely outperforming state-of-the-art methods, thereby demonstrating practical applicability. The code and data will be made publicly available upon acceptance at https://github.com/xiaoliangpi/VSLP.
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All authors Yangping Li, Thomas Pinetz, Michael Hölzel, Marieta Toma, Alexander Effland
Computer Vision
SycoPhantasy: Quantifying Sycophancy and Hallucination in Small Open Weight VLMs for Vision-Language Scoring of Fantasy Characters
Arya Shah, Deepali Mishra and Chaklam Silpasuwanchai
Vision-
language models (VLMs) are increasingly deployed as evaluators in tasks requiring nuanced image understanding, yet their reliability in scoring
alignment between images and text descriptions remains underexplored. We investigate whether small, open-weight VLMs exhibit sycophantic behavior when evaluating image-text
alignment: assigning high scores without grounding their judgments in visual evidence. To quantify this phenomenon, we introduce the Bluffing Coefficient (\bc), a metric that measures the mismatch between a model's score and its evidence recall. We evaluate six open-weight VLMs ranging from 450M to 8B parameters on a
benchmark of 173,810 AI-generated character portraits paired with detailed textual descriptions. Our analysis reveals a significant inverse correlation between model size and sycophancy rate ($r = -0.96$, $p = 0.002$), with smaller models exhibiting substantially higher rates of unjustified high scores. The smallest model tested (LFM2-VL, 450M) produced sycophantic evaluations in 22.3% of cases, compared to 6.0% for the largest (LLaVA-1.6, 7B). These findings have direct implications for the deployment of small, open-weight VLMs as automated evaluators within attribute-rich, synthetic image evaluation tasks, where the gap between assigned scores and cited visual evidence is both measurable and consequential.
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Computer Vision
See Further, Think Deeper: Advancing VLM's Reasoning Ability with Low-level Visual Cues and Reflection
Zhiheng Wu, Tong Wang, Shuning Wang et al. (+2 more)
Recent advances in
Vision-
Language Models (VLMs) have benefited from Reinforcement Learning (RL) for enhanced
reasoning. However, existing methods still face critical limitations, including the lack of low-level visual information and effective visual feedback. To address these problems, this paper proposes a unified
multimodal interleaved
reasoning framework ForeSight, which enables VLMs to See Further with low-level visual cues and Think Deeper with effective visual feedback. First, it introduces a set of low-level visual tools to integrate essential visual information into the
reasoning chain, mitigating the neglect of fine-grained visual features. Second, a mask-based visual feedback mechanism is elaborated to incorporate visual reflection into the thinking process, enabling the model to dynamically re-examine and update its answers. Driven by RL, ForeSight learns to autonomously decide on tool invocation and answer verification, with the final answer accuracy as the reward signal. To evaluate the performance of the proposed framework, we construct a new
dataset, Character and Grounding SalBench (CG-SalBench), based on the SalBench
dataset. Experimental results demonstrate that the ForeSight-7B model significantly outperforms other models with the same parameter scale, and even surpasses the current SOTA closed-source models on certain metrics.
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All authors Zhiheng Wu, Tong Wang, Shuning Wang, Naiming Liu, Yumeng Zhang
Computer Vision
An Affordable, Wearable Stereo-Eye-Tracking Platform
Alexander Zimmer, Yasmeen Abdrabou and Enkelejda Kasneci
Research on
video-based eye-tracking has long explored stereo and glint-based methods, yet existing wearable eye trackers - both commercial and open-source - offer limited flexibility for
algorithm development and comparative evaluation. We present an affordable, wearable stereo eye-tracking platform built from off-the-shelf and 3D-printable components that explicitly targets this gap. The system combines four infrared eye cameras, infrared illumination, an optional scene camera, and software support for calibration and synchronized data acquisition. By design, the platform supports multiple eye-tracking paradigms, including stereo, glint-based, and binocular approaches, within a single hardware configuration. Rather than optimizing for end-user robustness, the platform prioritizes modularity and extensibility for research use. This paper focuses on the hardware architecture and calibration pipeline and demonstrates the feasibility of the approach using a prototype implementation. All hardware designs and documentation are made openly available.
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Computer Vision
Monocular Depth Estimation via Neural Network with Learnable Algebraic Group and Ring Structures
Qianlei Wang, Kexun Chen, Shaolin Zhang et al. (+3 more)
Monocular depth estimation (MDE) has witnessed remarkable progress driven by Convolutional Neural Networks and
transformer-based architectures. However, these approaches typically treat the problem as a generic image-to-image regression on Euclidean grids, thereby overlooking the intrinsic algebraic and
geometric structures induced by perspective projection. To address this limitation, we propose LAGRNet, a novel framework that fundamentally grounds MDE in algebraic geometry by explicitly
embedding learnable group, ring, and sheaf structures into the deep learning pipeline. Modeling feature maps as sections of a sheaf over an approximated image
manifold, our method first establishes a Group-defined Feature
Manifold (GFM) parameterized by a learned algebraic group action to enforce projective equivariance and robustness against view changes. To facilitate algebraically consistent cross-scale interactions, we subsequently introduce a Ring Convolution Layer (RCL) that formulates feature fusion as a graded ring homomorphism. Furthermore, to ensure global topological consistency, a Sheaf-based Module (SM) aggregates local depth cues via Čech nerve on the image
topology. Extensive zero-shot evaluations across the KITTI, NYU-Depth V2, and ETH3D
benchmarks demonstrate that LAGRNet significantly outperforms state-of-the-art methods in both accuracy and generalization capabilities.
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All authors Qianlei Wang, Kexun Chen, Shaolin Zhang, Hongli Gao, Chaoning Zhang, Xiaolin Qin
Computer Vision
Don't Pause! Every prediction matters in a streaming video
Dibyadip Chatterjee, Zhanzhong Pang, Fadime Sener et al. (+2 more)
Streaming
video models should respond at the moment an event unfolds, not after the moment has passed. Yet existing online VideoQA
benchmarks remain largely retrospective. They pause the
video at fixed timestamps, pose questions about current or past events, and score models only at those moments. This protocol leaves streaming outputs untested. To close this gap, we introduce SPOT-Bench, featuring multi-turn proactive queries that evaluate general streaming perception required by an always-on, real-time assistant. SPOT-Bench comes with Timeliness-F1, a consolidated metric that measures streaming outputs by their temporal precision and balanced coverage across the entire
video. Our
benchmark reveals that during streaming inference: (i) MLLMs detect events reliably in a stream but spam predictions unprompted; (ii) post-training MLLMs for silence reduces spamming but induces unresponsiveness; (iii) half of the streaming
video expects no response, which we term dead-time - compute spent here does not affect response latency. These findings motivate Ctrl-SPOT, a training-free streaming controller for MLLMs, that retains their event perception while controlling their streaming outputs for balanced coverage. This establishes a strong baseline on SPOT-Bench.
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All authors Dibyadip Chatterjee, Zhanzhong Pang, Fadime Sener, Yale Song, Angela Yao
Computer Vision
Unconstrained Multi-view Human Pose Estimation with Algebraic Priors
Xiaolin Qin, Qianlei Wang, Jiacen Liu et al. (+3 more)
Recovering 3D human pose from multi-view imagery typically relies on precise camera calibration, which is often unavailable in real-world scenarios, thereby severely limiting the applicability of existing methods. To overcome this challenge, we propose an unconstrained framework that synergizes deep neural networks, algebraic priors, and temporal dynamics for uncalibrated multi-view human pose estimation. First, we introduce the Triangulation with
Transformer Regressor (TTR), which reformulates classical triangulation into a data-driven
token fusion process to bypass the dependency on explicit camera parameters. Second, to explicitly embed the inherent algebraic relations of the multi-view variety into the learning process, we propose the Gröbner basis Corrector (GC). This pioneering loss formulation enforces constraints derived from the multi-view variety to ensure the neural predictions strictly adhere to the laws of projective geometry. Finally, we devise the Temporal Equivariant Rectifier (TER), which exploits the equivariance property of human motion to impose temporal coherence and structural consistency, effectively mitigating scale ambiguity in uncalibrated settings. Extensive evaluations on standard
benchmarks demonstrate that our framework establishes a new state-of-the-art for uncalibrated multi-view human pose estimation. Notably, our approach significantly closes the performance gap between calibration-free methods and fully calibrated oracles.
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All authors Xiaolin Qin, Qianlei Wang, Jiacen Liu, Chaoning Zhang, Fei Zhu, Zhang Yi
Computer Vision
BIMStruct3D: A Fully Automated Hybrid Learning Scan-to-BIM Pipeline with Integrated Topology Refinement
Mahdi Chamseddine, Fabian Kaufmann, Marius Schellen et al. (+3 more)
Automatic generation of Building Information Models (BIM) from building scans is a key challenge in architecture and construction. We present a modular pipeline for generating IFC-compliant BIM from 3D point clouds. The hybrid approach combines learning-based semantic
segmentation with
topology-aware
geometric reconstruction to model structural elements accurately. We propose vIoU, adapting voxel-based overlap evaluation to Scan-to-BIM by enabling holistic, instance-matching-free comparison of reconstructed and ground-truth models. We release the German Hospital
dataset (DeKH), including high-resolution point clouds, ground truth BIMs, and semantic annotations. Experiments on DeKH and CV4AEC
datasets show significant improvements over a RANSAC-based baseline, demonstrating robustness and scalability.
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All authors Mahdi Chamseddine, Fabian Kaufmann, Marius Schellen, Christian Glock, Didier Stricker, Jason Rambach
Computer Vision
ReVSI: Rebuilding Visual Spatial Intelligence Evaluation for Accurate Assessment of VLM 3D Reasoning
Yiming Zhang, Jiacheng Chen, Jiaqi Tan et al. (+3 more)
Current evaluations of spatial intelligence can be systematically invalid under modern
vision-
language model (VLM) settings. First, many
benchmarks derive question-answer (QA) pairs from point-cloud-based 3D annotations originally curated for traditional 3D perception. When such annotations are treated as ground truth for
video-based evaluation, reconstruction and annotation artifacts can miss objects that are clearly visible in the
video, mislabel object identities, or corrupt geometry-dependent answers (e.g., size), yielding incorrect or ambiguous QA pairs. Second, evaluations often assume full-scene access, while many VLMs operate on sparsely sampled frames (e.g., 16-64), making many questions effectively unanswerable under the actual model inputs. We improve evaluation validity by introducing ReVSI, a
benchmark and protocol that ensures each QA pair is answerable and correct under the model's actual inputs. To this end, we re-annotate objects and geometry across 381 scenes from 5
datasets to improve data quality, and regenerate all QA pairs with rigorous bias mitigation and human verification using professional 3D annotation tools. We further enhance evaluation controllability by providing variants across multiple frame budgets (16/32/64/all) and fine-grained object visibility metadata, enabling controlled diagnostic analyses. Evaluations of general and domain-specific VLMs on ReVSI reveal systematic failure modes that are obscured by prior
benchmarks, yielding a more reliable and diagnostic assessment of spatial intelligence.
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All authors Yiming Zhang, Jiacheng Chen, Jiaqi Tan, Yongsen Mao, Wenhu Chen, Angel X. Chang
Computer Vision
Instance Awareness of Multi-class Semantic Segmentation Loss Functions
Soumya Snigdha Kundu, Florian Kofler, Marina Ivory et al. (+3 more)
Instance-sensitive losses for semantic
segmentation such as blob loss and CC loss were designed to address instance imbalance, ensuring small lesions generate the same gradient as large ones, but operate only on single-class
segmentation. In multi-class settings, class imbalance poses an additional problem: rare classes with few instances receive a disproportionately small share of the training signal. We show that extending instance-sensitive losses to multi-class
segmentation via a one-vs-rest class decomposition repurposes them to also address class imbalance, as uniform averaging over classes ensures each class contributes equally regardless of frequency. We further show that inverse-size weighting, which destabilizes training when applied globally due to weight imbalances across rare and common classes, becomes effective when integrated within the per-component loss, confining the reweighting to each component's spatial context. On the BraTS-METS 2025
dataset (260 test cases), multi-class CC loss improves foreground Dice (0.64 +/- 0.26 vs. 0.59 +/- 0.27 baseline) and rare-class Dice, while maintaining Panoptic Quality at DSC threshold 0.5. Multi-class blob loss achieves the best Panoptic Quality at threshold 0.5 (0.40 +/- 0.24 vs. 0.38 +/- 0.25 baseline) and recognition quality (0.53 +/- 0.29 vs. 0.49 +/- 0.30). Integrating inverse-size weighting within the per-component loss increases rare-class Dice to 0.44 +/- 0.36 at the cost of reduced detection quality.
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All authors Soumya Snigdha Kundu, Florian Kofler, Marina Ivory, Hendrik Moller, Jonathan Shapey, Tom Vercauteren
Computer Vision · Cross-listed from eess.IV
Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring
Nikolaos Salaris, Adrien Desjardins and Manish K. Tiwari
The escalating
climate crisis and ecosystem degradation demand intelligent, low-cost sensors capable of robust, long-term monitoring in real-world environments. Absolute dissolved oxygen (DO) concentration is a key parameter for predicting
climate tipping points. Inexpensive optoelectronic sensors based on microstructured
polymer films doped with phosphorescent dyes could be readily deployable; however, signal drift and marine biofouling remain major challenges. Here, we introduce a sensing paradigm that combines camera-based DO sensors with a visual
transformer (ViT)-based physics-informed neural network (PINN) for high-fidelity sensing under biofouling conditions. Training and testing data were obtained from an algae-laden water tank over 14 days to capture accelerated biofouling. The ViT-PINN, which embeds the Stern-Volmer (SV) equation into the loss function, reduces mean average error (MAE) by 92% and 89% compared to classical statistical and ML approaches, achieving ~2 umol/L absolute error. A deep ensemble further quantifies predictive uncertainty, enabling self-diagnostic sensing.
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Computer Vision
Touchless Intraoperative Image Access System Based on Vision-Based Hand Tracking
Yin Lin, Domenico Aquino, Alberto Redaelli et al. (+3 more)
Touchless interaction with medical images is becoming increasingly important in the
surgical field, where sterility and continuity of the operational workflow are essential requirements. This work presents a
vision-based system for intraoperative
navigation of medical images through hand gestures acquired using a single RGB camera. Unlike many existing solutions, the system does not require additional hardware or user-specific training. Hand tracking is performed in real time using MediaPipe Hands, which provides a 2.5D estimation of hand landmarks. Simple and intuitive gestures are then mapped into translation, rotation, and zoom commands, enabling continuous and natural interaction with the image viewer. The system architecture is independent from the visualization software and, for implementation simplicity, in this study it was integrated with PyVista. Performance was evaluated through frame-level logging and quantitative analysis of latency, stability, and interaction robustness metrics. Experimental results highlight real-time behavior, with reduced latencies and stable control, in line with the requirements of fluid interaction. The system demonstrates the feasibility of a low-cost touchless solution for intraoperative access to medical images, laying the groundwork for future clinical evaluations.
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All authors Yin Lin, Domenico Aquino, Alberto Redaelli, Massimiliano Del Bene, Riccardo Barbieri, Simona Ferrante
Computer Vision
Graph-augmented Segmentation of Complex Shapes in Laser Powder bed Fusion for Enhanced In Situ Inspection
Stefano Raimondo, Matteo Bugatti and Marco Grasso
The technological maturity of in situ inspection and monitoring methods in additive manufacturing is steadily increasing, enabling more efficient and practical qualification procedures. In this context, image
segmentation of powder bed images in
Laser Powder Bed Fusion (L-PBF) has been investigated by various authors, leveraging both edge detection and machine learning approaches to identify deviations from nominal geometry. Despite these developments, several challenges remain, including the sensitivity of
segmentation performance to industrial illumination conditions and layer-to-layer variability in pixel intensity patterns. The study addresses these limitations by proposing a graph-augmented
segmentation approach. The underlying principle consists of preserving the geometrical information at a global level rather than at pixel-wise level, modeling dependencies and relational information among spatial regions with a Graph Neural Network bottleneck embedded into a U-Net architecture. This allows enhancing the consistency and accuracy of the geometry reconstruction in the presence of spatial and layer-wise photometric variability systematically faced in real data. The method is evaluated against
benchmark techniques for the in situ reconstruction of
lattice structures produced by L-PBF, demonstrating its potential as a scalable solution for robust in situ inspection and
geometric verification in industrial environments.
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Computer Vision
Radiomics- and Clinical Feature-Driven Prediction of Volumetric Response in Skull-Base Meningioma after CyberKnife Radiosurgery
Yin Lin, Elena De Martin, Giacomo Conte et al. (+6 more)
Skull-base meningiomas are often characterized by favorable long-term prognosis, yet their anatomical complexity and proximity to critical neurovascular structures make treatment selection challenging. Stereotactic radiosurgery with CyberKnife represents an effective therapeutic option when
surgical resection is not feasible; however, not all patients benefit equally from this treatment. Early identification of patients likely to respond to radiosurgery remains an open clinical problem. In this study, we propose a radiomics- and clinical feature-driven framework for predicting volumetric response in skull-base meningiomas treated with CyberKnife. Unlike most existing approaches that focus on progression-free survival or recurrence, our method targets volumetric response as an indicator of treatment efficacy. Pre-treatment MRI images from 104 patients were processed to extract radiomic features, which were combined with clinical variables and analyzed using six models. To ensure methodological rigor, the entire modeling process was implemented within a nested cross-validation scheme. Among the evaluated models, TabPFN achieved the best overall performance, with an AUC of 0.81 and consistently favorable classification metrics. These results suggest that advanced machine learning architectures, when combined with robust validation strategies, can effectively capture patterns associated with treatment response even in small-sample, high-dimensional settings.
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All authors Yin Lin, Elena De Martin, Giacomo Conte, Domenico Aquino, Cristiana Pedone, Alberto Redaelli, Riccardo Barbieri, Laura Fariselli, Simona Ferrante
Computer Vision
Computer Vision-Based Early Detection of Container Loss at Sea
Vishakha Lall, Capt. Stanley S Pinto, Capt. Chu Xing Peng et al. (+1 more)
Containerised shipping underpins global trade, yet container loss at sea remains a persistent safety, environmental, and economic challenge. Despite compliance with Cargo Securing Manuals, dynamic maritime conditions such as vessel motion, wind loading, and severe sea states can progressively destabilise container stacks, leading to overboard losses. With the new International Maritime Organisation's (IMO) mandatory reporting requirements for lost containers, there is an urgent need for a reliable, evidence-based early detection solution for destabilised containers. This study showcases a low-cost, retrofittable computer
vision-based system for early detection of destabilised containers using existing onboard cameras. The framework integrates object
segmentation to isolate container stacks, temporal object tracking using optical flow and individual objects' residual motion extraction to quantify relative movement. Experimental evaluation on real onboard ship footage demonstrates that the proposed pipeline effectively isolates container-level motion under challenging conditions of varying sea states and visibility conditions. By enabling early alerts for crew intervention and navigational adjustment, the proposed approach enhances cargo safety, operational resilience, and regulatory compliance.
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All authors Vishakha Lall, Capt. Stanley S Pinto, Capt. Chu Xing Peng, Wu Kaiwen
Computer Vision
Omni-o3: Deep Nested Omnimodal Deduction for Deliberative Audio-Visual Reasoning
Zhicheng Zhang, Wentao Gu, Weicheng Wang et al. (+5 more)
Omnimodal understanding entails a massive, highly redundant search space of cross-modal interactions, demanding focused and deliberative
reasoning. Current
reasoning paradigms rely on either sequential step-by-step generation or parallel sample-by-sample rollouts, leading to isolated
reasoning trajectories. This inability to share promising intermediate paths severely limits exploration efficiency and causes compounding errors in complex audio-visual tasks. To break this bottleneck, we introduce Omni-o3, a novel framework driven by a deep nested deduction policy. By formulating
reasoning as a dynamic recursive search, Omni-o3 inherently shares
reasoning prefixes across branches, enabling the iterative execution of four atomic cognitive actions: expansion, selection, simulation, and backpropagation. To empower this framework, we propose a robust two-stage training paradigm: (1) cold-start supervised fine-tuning on 101K high-quality, long-chain trajectories distilled from 3.5M diverse omnimodal samples, enabling necessary recursive search patterns; and (2) nested group rollout-driven exploratory reinforcement learning on 18K complex multi-turn samples, explicitly guided by a novel multi-step reward model to stimulate deep nested
reasoning. Extensive experiments demonstrate that Omni-o3 achieves competitive performance across 11
benchmarks, unlocking advanced capabilities in comprehensive audio-visual, visual-centric, and audio-centric
reasoning tasks.
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All authors Zhicheng Zhang, Wentao Gu, Weicheng Wang, Yongjie Zhu, Wenyu Qin, Meng Wang, Pengfei Wan, Jufeng Yang
Computer Vision
Multivariate Gaussian NeRF for Wide Field-of-View Ultrasound Reconstruction
Patris Valera, Magdalena Wysocki, Felix Duelmer et al. (+4 more)
Wide Field-of-View (WFoV) reconstruction enhances 3D ultrasound imaging by providing valuable anatomical context for
segmentation models and visualization. Clinical ultrasound volumes are predominantly acquired using convex probes, which generate expanding, diverging acoustic beams to maximize anatomical coverage. Stitching these sweeps together traditionally introduces significant compounding artifacts and aliasing due to depth-dependent resolution changes. Here, we introduce Ultra-Wide-NeRF, a Multivariate 3D Gaussian (MVG) NeRF-based method for WFoV ultrasound reconstruction. By explicitly modeling the complex beam geometry using distance-dependent convex volumetric sampling and anisotropic 3D Gaussians, our method inherently mitigates these compounding artifacts and provides anti-aliasing. Beyond simply reconstructing a static 3D grid, our NeRF-based approach yields a continuous neural representation of the tissue, enabling the synthesis of high-fidelity novel views from arbitrary virtual trajectories. We validate Ultra-Wide-NeRF for intracardiac echocardiography on phantom and porcine
datasets, demonstrating that our method expands the spatial context important in intraoperative
navigation. Code will be open-sourced upon publication.
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All authors Patris Valera, Magdalena Wysocki, Felix Duelmer, Mohammad Farid Azampour, Sebastian Herz, Stefan Wörz, Nassir Navab
Computer Vision
POCA: Pareto-Optimal Curriculum Alignment for Visual Text Generation
Yaohou Fan, Qingzhong Wang, Yongsong Huang et al. (+3 more)
Current visual text generation models struggle with the trade-off between text accuracy and overall image coherence. We find that achieving high text accuracy can reduce aesthetic quality and instruction-following capability. Although reinforcement learning approaches can alleviate the problem through aligning with multiple rewards, they are often unstable for text generation, as existing approaches normally optimize multiple rewards in a weighted-sum way. In addition, it is difficult to balance the weight of each reward. Moreover, reinforcement learning requires a set of training instructions. A large number of
prompts require more training time and computing resources, while a small set leads to poor performance. Hence, how to select the
prompts for efficient training is an unsolved problem. In this study, we propose Pareto-Optimal Curriculum
Alignment (POCA), a framework that addresses this issue as a multi-objective problem by: 1) identifying the Pareto-optimal set to avoid simple scalarization and 2) designing an adaptive curriculum
alignment strategy to manage a learning sequence of a multi-reward
dataset using automatic difficulty assessment, which is crucial for optimal convergence as RL methods explore in a limited data environment. In synergy, POCA finds the Pareto-optimal set in a unified reward space, which eliminates inconsistent signals to find the best trade-off solution from different rewards under an easy-to-hard optimization landscape. The experimental results show that POCA significantly improves all metrics such as CLIP, HPS scores and sentence accuracy.
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All authors Yaohou Fan, Qingzhong Wang, Yongsong Huang, Junyi Liu, Tomo Miyazaki, Shinichiro Omachi
Computer Vision
PointTransformerX: Portable and Efficient 3D Point Cloud Processing without Sparse Algorithms
Laurenz Reichardt, Nikolas Ebert and Oliver Wasenmüller
3D point cloud perception remains tightly coupled to custom CUDA operators for spatial operations, limiting portability and efficiency on non-NVIDIA, AMD, and embedded hardware. We introduce PointTransformerX (PTX), a fully PyTorch-native
vision transformer backbone for 3D point clouds, removing all custom CUDA operators and external libraries while retaining competitive accuracy. PTX introduces 3D-GS-RoPE, a rotary positional
embedding that encodes 3D spatial relationships directly in self-
attention without neighborhood construction, and further replaces sparse convolutional patch
embedding with a linear projection. PTX explores inference-time scaling of
attention windows to improve accuracy without retraining. With a redesigned feed-forward network, PTX achieves 98.7% of PointTransformer V3's accuracy on ScanNet with 79.2% fewer parameters and executing 1.6\times faster while requiring just 253 MB
memory. PTX runs natively on NVIDIA GPUs, AMD GPUs (ROCm), and CPUs, providing an efficient and portable foundation for point cloud perception.
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Computer Vision
PEPS: Positional Encoding Projected Sampling – Extended
Guillaume Perez, Janarbek Matai and Takahiro Harada
Implicit neural representations (INRs) are increasingly being used as tools to map coordinates to signals, encompassing applications from neural fields to texture compression, shape representations, and beyond. Most INR methods are based on using high-dimensional projections of the initial coordinates through encoders such as grid or positional encoding. Nevertheless, positional encoding is often insufficient and grids, as we show in this paper, require high resolution for being able to learn. In this paper, we demonstrate that positional encoding can be used not only as a high-dimensional
embedding but also decomposed as a series of meaningful points. We propose the Positional Encoding Projected Sampling, where we treat the projection of the original coordinate at each frequency as a point of interest. We describe the motion of each point with respect to the frequencies and show that it follows a unique pattern. Finally, we use the unique motion of each point as a basis decomposition for doing learned positional encoding using grids. We prove, using three competitive applications; image representation, texture compression, and signed distance function; that the proposed approach outperforms the current state of the art methods, and often requires 25% less parameters for equivalent reconstruction error or rendering.
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Computer Vision
Robust Deepfake Detection, NTIRE 2026 Challenge: Report
Benedikt Hopf, Radu Timofte, Chenfan Qu et al. (+54 more)
Robustness is a long-overlooked problem in deepfake detection. However, detection performance is nearly worthless in the real world if it suffers under exposure to even slight image degradation. In addition to weaker degradations that can accidentally occur in the image processing pipeline, there is another risk of malicious deepfakes that specifically introduce degradations, purposefully exploiting the detector's weaknesses in that regard. Here, we present an overview of the NTIRE 2026 Robust Deepfake Detection Challenge, which specifically addresses that problem. Participants were tasked with building a detector that would later be tested on an unknown test-set, which included both common and uncommon degradations of various strengths. With a total number of 337 participants and 57 submissions to the final leaderboard, the first edition of the challenge was well received. To ensure the reliability of the results, participants were given only 24h to complete the test run with no labels provided, limiting the possibility of training on the test data. Furthermore, the top solutions were scored on a private test-set to detect any such overfitting. This report presents the competition setting,
dataset preparation, as well as details and performance of methods. Top methods rely on large foundation models, ensembles, and degradation training to combine generality and robustness.
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All authors Benedikt Hopf, Radu Timofte, Chenfan Qu, Junchi Li, Fei Wu, Dagong Lu, Mufeng Yao, Xinlei Xu, Fengjun Guo, Yongwei Tang, Zhiqiang Yang, Zhiqiang Wu, Jia Wen Seow, Hong Vin Koay, Haodong Ren, Feng Xu, Shuai Chen, Minh-Khoa Le-Phan, Minh-Hoang Le, Trong-Le Do, Minh-Triet Tran, Chih-Yu Jian, Yi-Fan Wang, Bang-Kang Chen, You-Chen Chao, Chia-Ming Lee, Fu-En Yang, Yu-Chiang Frank Wang, Chih-Chung Hsu, Aashish Negi, Hardik Sharma, Prateek Shaily, Jayant Kumar, Sachin Chaudhary, Akshay Dudhane, Praful Hambarde, Amit Shukla, Jielun Peng, Yabin Wang, Yaqi Li, Jincheng Liu, Xiaopeng Hong, Krish Wadhwani, Liam Fitzpatrick, Utkarsh Tiwari, Bilel Benjdira, Anas M. Ali, Wadii Boulila, Cristian Lazo Quispe, Aishwarya A, Akshara S, Ashwathi N, Jiachen Tu, Guoyi Xu, Yaoxin Jiang, Jiajia Liu, Yaokun Shi
Computer Vision
6thGrid-Net: Unified Remote Sensing Image Dehazing Based on Color Restoration and Edge-Preserving
Runci Bai, Kui Jiang, Xiang Chen et al. (+4 more)
Remote sensing images are frequently degraded by adverse weather conditions, particularly clouds and haze, which severely impair downstream applications. Existing restoration methods typically rely on computationally heavy architectures or sequential pipelines (e.g., detail enhancement followed by color rendition) that suffer from mutual interference and artifact accumulation. Furthermore, recent unified grid-based approaches utilize fixed, isotropic interpolation kernels, neglecting the intrinsic low-dimensional
manifold of natural images and inevitably causing edge blur. To address these limitations, we propose 6th Grid-Net, a highly efficient and unified remote sensing image restoration framework tailored for resource-constrained edge devices. Specifically, we construct a novel six-dimensional fusion tensor that seamlessly integrates the color rendition capabilities of 3D LUTs with the spatial-luminance detail preservation of bilateral grids. To overcome the drawbacks of standard trilinear interpolation, we introduce a
manifold-adaptive high-dimensional sampling mechanism. This mechanism dynamically adjusts the interpolation kernel based on local edge orientation, texture strength, and color similarity, enabling simultaneous global color stylization and local edge refinement in a single forward pass. Additionally, an edge-aware grid smoothing constraint and dynamic quantization are incorporated to suppress ghosting artifacts and significantly compress the model size. Extensive experiments on multiple
benchmark datasets demonstrate that 6th Grid-Net achieves state-of-the-art restoration quality across various degradation scenarios.
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All authors Runci Bai, Kui Jiang, Xiang Chen, Chen Wu, Dianjie Lu, Guijuan Zhang, Zhuoran Zheng
Computer Vision
EXACT: an explainable anomaly-aware vision foundation model for analysis of 3D chest CT
Xuguang Bai, Mingxuan Liu, Tongxi Song et al. (+6 more)
Chest computed
tomography (CT) is central to the detection and management of thoracic disease, yet the growing scale and complexity of volumetric imaging increasingly exceed what can be addressed by scan-level prediction alone. Clinically useful AI for CT must not only recognize disease across the whole volume, but also localize abnormalities and provide interpretable visual evidence. Existing
vision-
language foundation models typically compress scans and reports into global image-text representations, limiting their ability to preserve spatial evidence and support clinically meaningful interpretation. Here we developed EXACT, an explainable anomaly-aware foundation model for three-dimensional chest CT that learns spatially resolved representations from paired clinical scans and radiology reports. EXACT was pre-trained on 25,692 CT-reports pairs using anatomy-aware weak supervision, jointly learning organ
segmentation and multi-instance anomaly localization without manual voxel-level annotations. The resulting organ-specific anomaly-aware maps assign each voxel a disease-specific anomaly score confined to its corresponding anatomy, jointly encoding lesion extent and organ-level context. In retrospective multinational and multi-center evaluations, EXACT showed broad and consistent improvements across clinically relevant CT tasks, spanning multi-disease diagnosis, zero-shot anomaly localization, downstream adaptation, and visually grounded report generation, outperforming existing three-dimensional medical foundation models. By transforming routine clinical CT scans and free-text reports into explainable voxel-level representations, EXACT establishes a scalable paradigm for trustworthy volumetric medical AI.
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All authors Xuguang Bai, Mingxuan Liu, Tongxi Song, Yifei Chen, Hongjia Yang, Kasidit Anmahapong, Zihan Li, Ying Zhou, Qiyuan Tian
Computer Vision
Bridging Restoration and Generation in One-step Diffusion for Real-World Image Super-Resolution
Shyang-En Weng, Yi-Cheng Liao, Yu-Syuan Xu et al. (+3 more)
Pretrained
diffusion models have revolutionized real-world image super-resolution (Real-ISR), but their iterative sampling is computationally prohibitive, driving efforts to distill it into a single step. General one-step methods fine-tune the generative prior into a deterministic mapping, restoring efficiency but discarding its
stochastic nature. Conversely, recent attempts re-engage generation by shifting the timestep or injecting random noise, adjusting either the position or the state while the other stays fixed. Because only one side is controlled, the two align at isolated preset timesteps but drift apart once steered, leaving generation unstable. To address this, we present one-step
diffusion via Inversion and Degradation-aware Sampling for Real-ISR (IDaS-SR), a one-step framework that bridges deterministic restoration and
stochastic generation. At its core,
Manifold Anchoring grounds the low-quality latent on the pretrained trajectory through two operations jointly estimated by the
Manifold Inversion Noise
Estimator (MINE): positioning declares where the latent lies and how it deviates from the clean state, while inversion aligns the latent to the declared position. Upon the anchor, CHARIOT reintroduces controlled stochasticity by jointly rescheduling the trajectory and interpolating the noise, enabling a single scalar to smoothly navigate the fidelity-realism trade-off. Extensive experiments demonstrate that IDaS-SR effectively unleashes the generative prior, achieving state-of-the-art performance under explicit control in a single inference step.
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All authors Shyang-En Weng, Yi-Cheng Liao, Yu-Syuan Xu, Chia-Hung Yuan, Wei-Chen Chiu, Ching-Chun Huang
Computer Vision
Open-Vocabulary Semantic Segmentation Network Integrating Object-Level Label and Scene-Level Semantic Features for Multimodal Remote Sensing Images
Jinkun Dai, Yuanxin Ye, Peng Tang et al. (+4 more)
Semantic
segmentation of multi-modal remote sensing imagery plays a pivotal role in land use/land cover (LULC) mapping, environmental monitoring, and precision earth observation. Current multi-modal approaches mainly focus on integrating complementary visual modalities, yet neglect the incorporating of non-visual textual data - a rich source of knowledge that can bridge semantic gaps between visual patterns and real-world concepts. To address this limitation, we propose TSMNet, a text supervised multi-modal open vocabulary semantic
segmentation network that synergistically integrates textual supervision with visual representation for open-vocabulary semantic
segmentation. Unlike conventional multi-modal
segmentation frameworks, TSMNet introduces a dual-branch text encoder to extract both scene-level semantic and object-level label information from various textual data, enabling dynamic cross-modal fusion. These text-derived features dynamically interact with visual
embeddings through the proposed text-guided visual semantic fusion module, enabling domain-aware feature refinement and human-interpretable decision-making. To verify our method, we innovatively construct two new multi-modal
datasets, and carry out extensive experiments to make a comprehensive comparison between the proposed method and other state-of-the-art (SOTA) semantic
segmentation models. Results demonstrate that TSMNet achieves superior
segmentation accuracy while exhibiting robust generalization capabilities across diverse geographical and sensor-specific scenarios. This work establishes a new paradigm for explainable remote sensing analysis, demonstrating that textual knowledge integration significantly enhances model generalizability. The source code will be available at https://github.com/yeyuanxin110/TSMNet
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All authors Jinkun Dai, Yuanxin Ye, Peng Tang, Tengfeng Tang, Xianping Ma, Jing Xiao, Mi Wang