Computer Vision
InceptionGS: Generative Bootstrapping for Large-Scale Gaussian Splatting under Unstructured View Sampling
Tianheng Lu, Guangyu Wang, Ruqi Huang et al. (+1 more)
Achieving truly immersive large-scale scene digitization necessitates consistent and visually pleasing rendering across all possible viewing perspectives. However, collecting multi-view images covering every fine detail of a large-scale scene is prohibitive due to scene complexity, capture cost, negligence, or accessibility constraints. As a result, the sampled views tend to be highly unstructured – the majority of the scene is well covered yet certain regions inevitably lack sufficient observations. Existing reconstruction based methods are vulnerable to view scarcity while generation based approaches suffer from generalization, controllability, and 3D consistency issues. To address this challenge, we propose InceptionGS, which bootstraps Gaussian splatting by subtly balancing reconstruction and generation. Starting from an initial Gaussian splatting, InceptionGS reasonably rethinks and repairs problematic regions caused by view scarcity while preserving the quality elsewhere, by softly incorporating scene- and view-adaptive generative priors. Extensive experiments on real-world large-scale scenes demonstrate the superiority and broad applicability of our approach in handling unstructured imagery and boosting high-fidelity Gaussian splatting. Please refer to the supplementary
video for better visual demonstrations.
Read the full abstract ▾
All authors Tianheng Lu, Guangyu Wang, Ruqi Huang, Lu Fang
Computer Vision
Canjie Liu, Jiawen Kang, Jinbo Wen et al. (+1 more)
Large
vision-
language models have achieved remarkable success in
vision-
language tasks. However, they remain prone to Visual
Hallucinations (VHs), undermining their reliability in real-world applications. Existing solutions typically require curated
datasets, additional training, or multi-round decoding, resulting in considerable computational overhead. In this paper, we propose RVSD (\underline{R}etrieval \underline{V}ision \underline{S}parse \underline{D}ecoding), a training-free and plug-and-play decoding framework that, for the first time, unifies
token sparsification and Semantic-Space Visual
Retrieval (SSVR) within a single decoding pass. Within RVSD, we introduce a semantics-directed
token selection strategy that selectively sparsifies redundant
tokens while preserving critical visual information. We further propose the SSVR mechanism, which reformulates visual compensation as an on-demand cross-modal
retrieval process within a shared semantic space. Extensive experiments demonstrate that RVSD achieves state-of-the-art performance in mitigating VHs while maintaining robust suppression capabilities under long-context generation settings. Our code is available here.\footnote{https://github.com/canjie-liu/RVSD}
Read the full abstract ▾
All authors Canjie Liu, Jiawen Kang, Jinbo Wen, Zishao Zhong
Computer Vision
MV-dVRK: A Multi-Viewpoint Benchmark for Spatial Surgical Perception
Guido Caccianiga, Sergey Prokudin, Yutong Chen et al. (+9 more)
Large-scale training and refined optimization techniques have greatly improved sparse multi-view 3D reconstruction. Despite their relevance to surgery, such methods have never before been rigorously evaluated on real endoscopic images. Current clinical telerobots deploy a single stereo camera inside the patient, making multi-viewpoint data extremely rare. This paper presents MV-dVRK, the first ex-vivo
surgical dataset to combine multiple exposure-synchronized stereo viewpoints with accurate surface geometry and camera poses. The static subset of the
benchmark provides dense SfM reference geometry, validated against an industrial 3D scanner, together with ground-truth camera poses and sparse-view test sets. We use MV-dVRK to systematically compare zero-shot
monocular, stereo, multi-stereo, and multi-view 3D reconstruction methods as the number of viewpoints increases. With two endoscopes, multi-stereo reconstruction achieves the highest coverage. With a third viewpoint, optimization-based multi-view methods perform best, covering 67% of ground-truth surface points within a 1 mm tolerance and recovering highly accurate relative camera poses. By contrast, feed-forward foundation models cover only 43% of the ground-truth surface in the same setting. MV-dVRK also includes ten dynamic sequences spanning multiple
surgical tasks, with increasing kinematic complexity and tissue deformation, providing a basis for future research in multi-viewpoint
surgical perception. The project is available at: https://mv-dvrk.is.mpg.de.
Read the full abstract ▾
All authors Guido Caccianiga, Sergey Prokudin, Yutong Chen, Bernard Javot, Rachael L'Orsa, Omer Burak Aladağ, Yarden Sharon, Jens Rolinger, Ivan Capobianco, Anton Deguet, Siyu Tang, Katherine J. Kuchenbecker
Computer Vision
A Top-Down Framework for Metric-Scale Athlete Localization from Single Broadcast Frames
Thanh-Khoi Nguyen, Hoang-Phuc Nguyen, Linh-Huynh et al. (+1 more)
Accurate world-coordinate localization of athletes from single-frame broadcast footage is inherently challenging due to extreme scale disparities in ultra-high-resolution imagery. In this paper, we propose a top-down framework for metric-scale athlete localization from a single calibrated frame. Our approach centers on three key contributions. First, we propose Boundary-Aware Adaptive Tiling, a semantics-guided extension of standard sliced inference. By iteratively expanding tile boundaries based on coarse bounding-box predictions, it systematically ensures full object containment, effectively mitigating boundary-splitting artifacts through a lightweight pipeline adaptation without architectural modifications. By substantially mitigating recall degradation under extreme scale variance, Boundary-Aware Adaptive Tiling enables us to isolate perspective distortion as the primary source of residual localization error. Second, we adapt the RTMPose-X architecture into a specialized two-keypoint
estimator (pelvis and ground projection), employing a reformulated Gated
Attention Unit optimized for this geometrically coupled point pair, and then deterministically lift the 2D ground projections into world coordinates via camera-calibrated ray casting. On the public test set, our method achieves a LocSim score of 97.44 and an mAP of 0.9128, outperforming the baseline by over 21 % and establishing a robust solution for high-resolution scale variance.
Read the full abstract ▾
All authors Thanh-Khoi Nguyen, Hoang-Phuc Nguyen, Linh-Huynh, Minh-Triet Tran
Computer Vision
Generating Medical Image Counterfactuals using Causal Explanations
David A. Kelly, Tom Yaacov, Nathan Blake et al. (+2 more)
Deep learning models have achieved impressive performance in medical image diagnosis, yet their deployment in clinical settings remains constrained by limited explainability. Counterfactual images provide one means of
auditing model behavior by showing how an image would need to change for a classifier to produce a different prediction. Existing approaches typically generate such explanations using auxiliary models, including generative
adversarial networks and
diffusion models. While often capable of producing visually realistic images, these methods explain one black-box model using another, making it difficult to separate the classifier's decision-making process from the inductive biases of the generator. We propose a novel counterfactual-generation framework that requires no generative model. Instead, counterfactuals are constructed directly from causal evidence extracted from the classifier. The resulting approach is deterministic, requires no additional model training, and enables controllable edits within user-specified regions of interest. Experiments on real-world medical imaging
datasets demonstrate that the proposed method successfully changes classifier predictions while remaining closer to the original image than generative baselines, providing a more direct and transparent view of the classifier's decision boundary.
Read the full abstract ▾
All authors David A. Kelly, Tom Yaacov, Nathan Blake, Sander Beckers, Hana Chockler
Computer Vision
GaLe: memory-efficient Global Approximate and Local Exact features
Alberto Ancilotto and Elisabetta Farella
Embedded devices typically lack the resources of GPU-equipped machines, and existing inference methods suffer from either high computational overhead (patch-based) or accuracy loss (approximation-based). We propose GaLe, a
memory-efficient technique that enables the deployment of pretrained networks on constrained devices without retraining. GaLe partitions feature maps into two components: a local exact (Le) representation that preserves fine details and a global approximate (Ga) representation that retains long-range dependencies. Unlike standard tiling, GaLe supports global operations and
attention mechanisms found in hybrid CNN-
transformer models. Validated on ImageNet, our method matches exact-inference performance while achieving up to 65% speedup and 90% RAM reduction on a Cortex-M33 compared to patch-based inference. We further demonstrate GaLe's versatility across classification, detection, and generation tasks, highlighting its potential as a foundation for resource-efficient architecture design.
Read the full abstract ▾
Computer Vision
Genesis: A Generative Engine for Hierarchical Satellite Image Synthesis
Subash Khanal, Yangzhi Cui, Daniel Cher et al. (+4 more)
Earth observation is fundamentally multi-scale; geospatial tasks span varied resolutions, and satellite imagery is organized into cascading tile pyramids that nest fine detail within wide coverage. Current generative models of satellite imagery, however, operate along a single axis: they either zoom to enhance a single tile's resolution or pan to extend imagery at a fixed scale. As a result, no existing method produces a complete pyramid that stays consistent across both scale and space, where a high-zoom tile must agree with the coarse context it refines and with the neighbors it meets. Motivated by this gap, we introduce a new task, multi-scale tile completion: given a sparse set of seed tiles at arbitrary zoom levels and positions, synthesize a complete, uniform quadtree that is globally consistent across both scale and space. We approach this task with Genesis, a generative engine that brings both axes together by composing two specialized operators over the quadtree: a vertical super-resolution model and a horizontal mask-based outpainting model, producing pyramids that are consistent across zoom levels and seamless across neighboring tiles. Each operator achieves state-of-the-art results on its subtask, and the engine propagates sparse seeds into seamless, multi-resolution maps from any initial configuration. To evaluate the task and
benchmark Genesis, we introduce dense500, a fully observed multi-scale pyramid
dataset spanning diverse geographic regions, together with a suite of pyramid-level metrics. Code, models, and our
dataset are available at https://github.com/mvrl/genesis.
Read the full abstract ▾
All authors Subash Khanal, Yangzhi Cui, Daniel Cher, Eric Xing, Brian Wei, Srikumar Sastry, Nathan Jacobs
Computer Vision · Cross-listed from eess.IV
LoFi RADIO: A Distilled In-Domain Backbone Applied for Artifact-Severity Grading of Ultra-Low-Field Neonatal Brain MR
Jonathan B. Martin, Yashwant Kurmi and Charlotte R. Sappo
Ultra-low-field MRI makes neonatal
brain imaging deploy- able in low-resource settings, but its low SNR, lack of shielding, and long scan duration make it especially prone to acquisition artifacts, motivating automated quality control. We address the LISA 2026 Task 1a challenge: multi-label severity grading (0/1/2) of seven common image artifacts on ULF T2 weighted volumes. We identify that a number of backbones may be successfully paired with a classification MLP, but that no single backbone is uniformly best across artifacts. To improve performance, we evaluate routing complementary foundation model teachers through a per-artifact gate, as well as distilling the teachers into a single in-domain ViT-S student (LoFi
RADIO) over an unlabeled low-field MRI corpus. Both of these strategies improve the weighted composite. The distilled backbone matches or exceeds the gate and has the added advantage of not requiring deployment of multiple large foundation models at infer- ence.
Read the full abstract ▾
Computer Vision
Query Rewriting for Complex Object Segmentation in 4D Gaussian Representations
Thanh-Khoi Nguyen, Thien-Phuc Tran and Minh-Triet Tran
Recent 4D Gaussian representation frameworks have demonstrated strong performance in
language-guided dynamic scene understanding. However, these methods remain highly sensitive to verbose and narrative-style queries that contain noisy contextual information. In this paper, we investigate the impact of query rewriting for complex object
segmentation in 4D Gaussian representations. Inspired by recent findings in
retrieval-augmented
language models and keyword-guided query reformulation, we propose a training-free reinterpretation strategy that transforms long descriptive queries into concise keyword-grounded forms. Our approach progressively reduces linguistic noise while preserving semantic anchors relevant to object-centric representations. Experiments on HyperNeRF and Neu3D demonstrate that concise rewritten queries significantly improve both temporal localization and spatial
segmentation performance. In particular, our method improves average temporal accuracy from 60.92% to 92.21% and average vIoU from 20.08% to 76.94% without any additional fine-tuning. Extensive ablation studies further reveal that shorter, keyword-focused queries consistently yield stable
video-feature similarity distributions and better
alignment with object-centric Gaussian representations
Read the full abstract ▾
Computer Vision
Characterizing Text Branch Sensitivity in Medical Vision-Language Segmentation via Evidence Decoupling
Ziquan Liu, Zhewei Zhu and Xuyang Shi
Pretrained
vision-
language models (VLMs) have shown promising performance in medical image
segmentation by incorporating clinical text. However, it remains unclear how much textual information actually contributes to pixel-level predictions. In this work, we systematically investigate the role of text in
multimodal medical image
segmentation. We first analyze several commonly used fusion strategies and find that
segmentation performance is largely insensitive to the choice of fusion module. To further understand modality interactions, we propose an Evidence Decoupling Decoder (EDD) based on evidential deep learning and deep supervision. EDD serves as an internal representation analysis tool that decomposes image evidence and text-modulated evidence throughout the decoding process while maintaining competitive
segmentation performance. Experimental results show that the sensitivity to text perturbation varies substantially across
datasets. On BUSI and BTMRI, removing text causes catastrophic performance drops, indicating strong model reliance on textual input. On ISIC and Kvasir-SEG, text exerts relatively marginal influence. We further find that text affects predictions mainly through global semantic modulation rather than independent spatial localization, and that the specific semantic components driving text sensitivity differ across
datasets. These findings provide a deeper understanding of modality interaction in
multimodal medical image
segmentation and offer practical insights for future model design.
Read the full abstract ▾
Computer Vision
Learning to Attract and Repel: Dual Quality Margin Learning for Face Recognition (DQM-Face)
El Ouanas Belabbaci, Bhavesh Wani and Philipp Terhörst
Face recognition in unconstrained environments remains highly challenging due to diverse and extreme variations encountered in real-world scenarios. To mitigate these effects, existing margin-based approaches model sample quality through feature magnitude. However, magnitude-based modeling alone is susceptible to identity-agnostic noise, which can degrade the reliability and discriminative power of learned representations. In this paper, we propose Dual Quality Margin Learning for Face Recognition (DQM-Face), a novel framework that enables refined attraction and repulsion dynamics during representation learning. Our approach unifies conventional magnitude-based quality estimation with a newly introduced semantic quality learning mechanism, realized via squeeze-and-excitation semantic
attention. By jointly leveraging magnitude and semantic cues, we construct enhanced quality-aware margins that adaptively strengthen intra-class compactness through improved attraction during learning. To further enhance inter-class discrimination, we introduce a repulsion margin formulation that explicitly enlarges inter-class separation. The unified integration of semantic quality modeling with dual attraction-repulsion margin optimization results in a more structured and discriminative feature geometry. Extensive experiments on multiple challenging
benchmarks demonstrate that DQM-Face consistently outperforms state-of-the-art face recognition methods. Moreover, we show that the quality learned for margin optimization is highly effective for face image quality assessment within the proposed framework, demonstrating that the learned quality signal is intrinsically aligned with the recognition objective. The code is publicly available: https://github.com/RAIB-group/DQM-Face
Read the full abstract ▾
Computer Vision
From Detection to Localization: A Unified Forensics Framework for Fully Synthetic and Tampered Images
Annalisa Gallina, Marco Fiorucci, Marco Brigo et al. (+2 more)
The rapid advancement of generative models has significantly worsened the problem of manipulated image detection, as these methods are capable of producing highly realistic forgeries, reinforcing the importance of multimedia forensics. Conventional approaches typically frame image manipulation detection as a binary classification task (real vs. generated), which limits the capability to distinguish and localize different forms of manipulation. To address these constraints, this work extends an existing detector by introducing a unified multiclass framework (real vs. fully generated vs. tampered). In addition to classifying image authenticity, the framework incorporates a
segmentation branch to enable pixel-level localization of tampered regions. The proposed approach outperforms selected recent
benchmarks, offering an efficient solution with improved classification accuracy and higher IoU scores for the localization task. Find the code at https://github.com/anngal01/From-Detection-to-Localization-A-Unified-Forensics-Framework-for-Fully-Synthetic-and-Tampered-Images.
Read the full abstract ▾
All authors Annalisa Gallina, Marco Fiorucci, Marco Brigo, Federica Battisti, Lamberto Ballan
Computer Vision
AffectDelta: Beyond Emotion Labels for Image Editing
Xingzu Zhan, Lin Gu and Ruogu Fang
Emotion-driven image editing aims to evoke a specified target
emotion by modifying
emotion-relevant visual cues in a source image, while preserving the overall composition and semantic-structural coherence of the original scene. Existing scene-level editors typically specify the target with a single
emotion category and often learn visual transformations from operation-level text instructions. A category collapses a mixed affective endpoint into one dominant label, while
language cannot precisely quantify how coexisting
emotions should increase, decrease, or remain stable. We introduce AffectDelta, a source-aware editor that treats editing as a transition between eight-dimensional
emotion distributions. A frozen
Emotion Distribution Predictor estimates the source state, and the signed source-to-target difference encodes the direction and magnitude of the requested transition. Within AffectDelta, an internal transition encoder and a source-aware
diffusion backbone jointly translate this signal into context-dependent semantic and appearance changes. To train this formulation, we construct AffectPair-249K, comprising 248,841 source-target pairs with predicted eight-dimensional distributions and spanning both cross-category and within-category transitions. Experiments against six baselines, combining quantitative evaluation with qualitative comparisons, demonstrate improved affective
alignment and content preservation, while ablations validate our design choices. Code and
dataset will be made publicly available upon acceptance.
Read the full abstract ▾
Computer Vision
Generalizable Brain Tumor Segmentation with Self-Training and Tumor-Aware Deformations
Henrique Zan Grande, Jeovane Honorio Alves, Rayson Laroca et al. (+1 more)
This work presents an approach to the Generalizability Across Tumors (BraTS-GoAT) task of the BraTS 2026 Challenge, which focuses on robust
segmentation of
brain tumor sub-regions across a heterogeneous patient population. The proposed method employs the nnU-Net framework with a large residual encoder architecture, integrating a semi-supervised learning technique with pseudo-labels generated from the unlabeled training data and a tumor-aware deformable augmentation that locally deforms the lesion while preserving the surrounding anatomy. We evaluate the individual contributions of each component, as well as their combination, using varying proportions of the most confident pseudo-labeled cases. The submitted configuration for the generalization task achieves Dice and NSD scores of 0.881 and 0.473 for Whole Tumor, 0.817 and 0.490 for Tumor Core, and 0.775 and 0.533 for Enhancing Tumor on the BraTS-GoAT validation set, improving over the labeled-only baselines across all tumor regions and confirming that self-training and the proposed augmentation are complementary. Our source code is publicly available at https://github.com/Henrique-zan/brats-goat-2026/.
Read the full abstract ▾
All authors Henrique Zan Grande, Jeovane Honorio Alves, Rayson Laroca, Andre Gustavo Hochuli
Computer Vision
Deeply Interleaved Text-Image Contexts for Multimodal LLMs Assessment
Zihao Wang, Xi Xiang, Yuwen Sun et al. (+5 more)
Current evaluations and training of
multimodal models predominantly focus on multi-image tasks, largely overlooking interleaved text-image scenarios. In such multi-image tasks, text typically serves merely as task instructions, lacking deep semantic interaction with the visual content. In contrast, realworld applications like text-image co-creation, character tracking, and spatial reconstruction require constant interaction between text and images. Consequently, models must possess a deep understanding of these interleaved contexts. To bridge this gap, we introduce a novel
benchmark, TIC-Bench (deeply interleaved Text-Image Contexts), designed to evaluate the capability of models to integrate text-image clues and recover the ground truth facts within deeply interleaved contexts. This
benchmark encompasses three core domains: Logical, Temporal, and Spatial Association, which are further categorized into eight specific types, comprising a total of 2,280 questions. We evaluated 10 state-of-the-art MLLMs and observed a substantial performance gap compared to human experts, together with persistent difficulties in integrating evidence distributed across interleaved visual and textual inputs. Ultimately, this
benchmark provides a valuable analytical tool for assessing and advancing the ability of
multimodal models to effectively integrate text and image information in deeply interleaved contexts. TIC-Bench is publicly available at https://huggingface.co/
datasets/pino10010/TIC-Bench
Read the full abstract ▾
All authors Zihao Wang, Xi Xiang, Yuwen Sun, Yingyu Li, Yabo Zhang, Yihan Zeng, Fan Li, Wangmeng Zuo
Computer Vision
Uicheol Jung, Juyoung Hong, Geuntaek Lim et al. (+1 more)
Text-
video retrieval requires representations that can distinguish
videos with similar scenes, actions, and temporal patterns. Recent
multimodal large
language models have been adapted as
embedding models, but they often represent each input using a single
token from the final layer. This can compress diverse
video-text cues into a single vector and limit fine-grained
retrieval. To address this limitation, we propose MARS, a multi-layer and multi-slot
embedding framework for text-
video retrieval. MARS constructs multiple adaptive representation slots by combining hidden states from different decoder layers, compares corresponding text and
video slots, and aggregates their similarities for
retrieval. To better handle confusing candidates, we further introduce a hard-negative-aware slot specialization objective that encourages the slots to capture discriminative matching cues. Experiments on four text-
video retrieval benchmarks show that MARS achieves state-of-the-art results in both direct similarity-based
retrieval and reranking settings. Ablation studies and analyses demonstrate that multi-layer fusion, multiple slots, and hard-negative-aware slot specialization provide complementary gains. Code is available at https://github.com/sejong-rcv/MARS.
Read the full abstract ▾
All authors Uicheol Jung, Juyoung Hong, Geuntaek Lim, Yukyung Choi
Computer Vision
Stereo 4D Radar for 3D Object Detection: Integrating Geometric Alignment and Absolute Velocity Estimation
Seung-Hyun Song, Dong-Hee Paek, Woong-Chan Byun et al. (+1 more)
Four-dimensional (4D) Radar is a powerful sensing modality capable of detecting surrounding three-dimensional (3D) objects under diverse weather conditions and providing Doppler-based motion information. However, raw 4D Radar signals contain significant clutter from road surfaces, guardrails, and surrounding vehicles, along with multipath-induced ghost reflections and the receiver's inherent noise floor. Consequently, preprocessing
algorithms designed to remove such invalid measurements often make the Radar data excessively sparse. Moreover, the Doppler measurements provided by 4D Radar describe only the radial component of an object's velocity, limiting their ability to recover the full motion state. In this paper, we introduce a stereo 4D Radar-based 3D object detection framework that exploits the
geometric disparity between left and right Radars to estimate the absolute velocity of objects and achieve more robust perception through the fusion of their complementary features. The effectiveness of the proposed framework is validated on our in-house stereo 4D Radar
dataset, demonstrating performance gains of 8.82 points in AP 3D and 9.0 points in AP BEV over state-of-the-art mono 4D Radar baselines. These results demonstrate that absolute velocity estimation combined with stereo geometry-aware feature fusion leads to substantial improvements in 3D object detection.
Read the full abstract ▾
All authors Seung-Hyun Song, Dong-Hee Paek, Woong-Chan Byun, Seung-Hyun Kong
Computer Vision
RGB-to-IR image translation for infrared vehicle detection in unseen UAV domains
Thijs A. Eker, Ella P. Fokkinga, Jan Erik van Woerden et al. (+4 more)
Synthetic training data is crucial for developing
vision AI when real-world data is scarce, as in thermal infrared (IR) aerial vehicle detection. While abundant
UAV RGB imagery motivates RGB-to-IR translation for data augmentation, unobservable thermal traits (e.g., engine heat) make learning transferable mappings challenging. This work investigates whether modern generative translators can overcome this cross-modal gap to improve infrared vehicle detection on unseen
UAV target domains. Translators are trained on paired RGB-IR source
datasets and applied to RGB training images from held-out target
datasets to generate synthetic IR data. Evaluated methods include supervised GANs, ControlNet-based
diffusion models, and foundation-model editing via LoRA. The resulting synthetic IR imagery is used to train RF-DETR vehicle detectors, which are evaluated on unseen IR target test splits across five aerial
datasets, with Kust4K and VTUAV serving as target domains. Synthetic IR consistently outperforms RGB and grayscale baselines. Stable
Diffusion 3.5 with ControlNet yields the best results, improving mAP from 50.8 to 60.1 on Kust4K and from 25.6 to 38.4 on VTUAV compared to models trained only on source-domain IR data. Increasing output diversity via multiple seeds (+1.1 mAP) and
prompt variations (+3.3 mAP) provides additional gains on VTUAV. Although a performance gap to real target IR data remains, generative RGB-to-IR translation effectively mitigates IR data scarcity and improves cross-domain aerial vehicle detection.
Read the full abstract ▾
All authors Thijs A. Eker, Ella P. Fokkinga, Jan Erik van Woerden, Elfi I.S. Hofmeijer, Sebastiaan P. Snel, Klamer Schutte, Friso G. Heslinga
Computer Vision
Spatially Aware World Action Model via Geometric Latent Diffusion
Javier Alejandro Lopetegui Gonzalez, Paul Pacaud and Cordelia Schmid
World Action Models (WAMs) leverage the capabilities of large-scale pretrained
video diffusion models to jointly predict future observations and actions, inheriting rich visual and physical priors from internet-scale
video. This has made them a promising paradigm for
robot policy learning, yet the prevailing models operate exclusively on RGB observations and do not leverage 3D information. To bridge this gap, we introduce a Spatially Aware World Action Model (SA-WAM), which repurposes a pretrained
video model for joint action, RGB, and depth prediction, enabling 3D-aware world modeling and action prediction within a single
diffusion backbone. We use a nonlinear encoding that maps the unbounded depth signal into the bounded input domain expected by the frozen VAE tokenizer. This allows us to reuse the tokenizer without 3D-specific fine-tuning, incorporating
geometric information without sacrificing the pretrained priors. SA-WAM achieves state-of-the-art results on the RoboCasa and LIBERO-Plus
benchmarks, while simultaneously improving future-state predictions. Furthermore, SA-WAM outperforms strong baselines in real-world evaluation using a UR5 robotic arm, with strong gains in randomized environments. We analyze the correlation between world model prediction quality and rollout success, providing insights into WAM performance and avenues for its improvement.
Read the full abstract ▾
Computer Vision
Fine-Grained Anomaly Perception in Wild UGC-Enhanced Images: A Comprehensive Dataset and Difference-Fusion Framework
Yan Zhong, Gefei Chen, Qiufang Ma et al. (+4 more)
Image enhancement and restoration have become standard back-end operations on short-
video and social media platforms to boost UGC visual experience. Yet these processes inevitably introduce visual anomalies–especially in faces, texts, and textures–that directly undermine perceptual fidelity and viewer trust. While existing IQA methods perform well on classic distortions, they target holistic quality assessment and fail to capture the specific, localized anomalies caused by enhancement
algorithms in real-world UGC. To bridge this gap, we formally define a new task-quality Anomaly Perception for UGC image Enhancement (UEAP), and contribute the first UEAP
benchmark dataset, named UEAP-4k, curated from the real business scenarios. It provides fine-grained annotations for anomaly categories, localization and severity levels. Furthermore, we propose a Difference-Fusion Anomaly Perception Method (DFAP-UGC) for wild UGC-enhanced images, which leverages explicit problem-reference difference fusion with dense spatial querying, regional verification, and quality-aware ranking, enabling robust anomaly identification in challenging scenarios. To handle the inherent coupling of subtasks in this new task, we propose a Locality-Aware Dynamic Task Prioritization (LADTP) training strategy that enables effective end-to-end learning and eliminates multi-stage overhead. Extensive experiments show that our method outperforms baselines adapted from classical approaches for this task, validating the value of this
dataset and the superior of DFAP-UGC for robust UGC-enhanced image anomaly perception. Code and data will be public.
Read the full abstract ▾
All authors Yan Zhong, Gefei Chen, Qiufang Ma, Zhen Wang, Zhiwei Fan, Lei Shi, Tingting Jiang
Computer Vision
Doppio: A Dataset for Contactless Weight Estimation of Falling Particles
Simon Kiefhaber, Jan-Martin O. Steitz, Julia Grabinski et al. (+5 more)
Measuring the mass of powder, including falling particles, is a common task in industrial applications. While scales are effective for static measurements, many applications require contactless sensing, where existing solutions are often costly, application-specific, and technically complex. In this work, we investigate computer
vision as a practical alternative for contactless mass estimation. As an accessible real-world case study, we focus on coffee grinding and introduce Doppio, a novel
video dataset capturing
videos of falling ground coffee, paired with precise, per-frame ground-truth weight measurements. To demonstrate contactless measuring, we evaluate deep learning-based approaches ranging from purely spatial feed-forward networks to recurrent spatio-temporal models. These models are analyzed with respect to their predictive accuracy and computational trade-offs. We demonstrate that deep learning-based computer
vision models accurately estimate the cumulative weight of falling particles, establishing a solid foundation for future
vision-based contactless measurement solutions.
Read the full abstract ▾
All authors Simon Kiefhaber, Jan-Martin O. Steitz, Julia Grabinski, Christoph Reich, Paul Wagner, Max Zimmermann, Simone Schaub-Meyer, Stefan Roth
Computer Vision
Beauty is in the AI of the beholder: MLLMs systematically overrate facial attractiveness
Santiago Grandas, Juan Sebastian Cely-Acosta, Mohit Mendiratta et al. (+2 more)
Beauty assessments from
Multimodal Large
Language Models (MLLMs) are increasingly popular amongst users, companies, and aestheticians. This raises the question of whether these AI models can accurately reflect human judgments of attractiveness. In a pre- registered exploratory study, we compared the attractiveness ratings of 2,513 human participants to four widely used commercial AI models: Claude, Gemini, GPT, and Grok. Results showed that MLLMs systematically rate faces more favourably and within a narrower range than humans and, at the time of study, do not reproduce human ratings in absolute terms. However, MLLMs exhibit strong correlations with human attractiveness judgments, accurately tracking the rank-ordering of faces. MLLMs may judge faces by different cues than humans; only face age was a predictor of facial attractiveness in both humans and MLLMs, with inconsistent patterns across models for ethnicity and gender. AI models strongly agree with one another, except for Grok, which also showed the lowest agreement with humans. Our findings suggest that while they may be able to approximate rank-orderings of human attractiveness, current off-the-shelf commercial MLLMs systematically overrate the beauty of human faces.
Read the full abstract ▾
All authors Santiago Grandas, Juan Sebastian Cely-Acosta, Mohit Mendiratta, Shafee Hassan, Macken Murphy
Computer Vision
Orthogonal Ensembles and Tested Explanations for Performer-Independent Body-Motion Emotion Recognition
Naoto Nishida and Yoshio Ishiguro
We study body-only, 12-class acted-
emotion classification from skeleton motion under leave-performer-out (LPO) evaluation, a hard, underdetermined setting: chance is 8.3%, and a protocol-matched reproduced STGCN++ baseline reaches only 25.73 +/- 4.03% Macro-F1. We show that reliable gains come not from a new architecture but from combining eleven models with orthogonal error modes: under 10-fold LPO cross-validation on the labeled training performers, an equal-weight logit-mean ensemble reaches 36.80 +/- 4.00% per-fold Macro-F1, a protocol-matched +11.07 pp (+43% relative) over the same-split reproduced baseline. Our central contribution is a tested explanation suite: for a strong ensemble member, part-masking and counterfactual edits show (rather than assert) that its decisions depend on motion-grounded body-region evidence, and this region saliency aligns with rule-based Laban Movement Analysis (LMA) attributes far more than with classical kinematics: region-level saliency-LMA Spearman rho = +0.500 versus +0.033, roughly 15x, and the
alignment holds for the submitted 11-way ensemble itself at rho = +0.517; the audit is post hoc and needs no retraining. The same suite faithfully reports a negative: within-window temporal saliency is diffuse rather than localized. On the hidden challenge test set the submitted ensemble scored 37.23 % Macro-F1 and received the Best Performance Award of the MMAC Challenge 2026 (Human score is 39 %). Code is available at https://github.com/nawta/diema-challenge and the presentation at https://nawta.github.io/mmac2026/.
Read the full abstract ▾
Computer Vision
SR-Edit: Region-Aware Image Editing via Self-Refinement
Andong Wang, Zehua Chen, Yuxuan Jiang et al. (+1 more)
With the recent rapid progress in generative models, image editing has made remarkable advances, yet achieving faithful edits that precisely modify only the target regions while strictly preserving all other regions remains challenging. Since externally provided region annotations are often difficult to obtain in practice, a growing body of work seeks to improve preservation by automatically inferring edit and non-edit regions, and then enforcing consistency on the latter. However, these approaches still suffer from inaccurate region estimation and heuristic correction strategies that distort the native inference process, making methods designed for fidelity themselves a new source of artifacts. We propose SR-Edit, an image editing framework that overcomes these issues via iterative self-refinement. Specifically, at each iteration, SR-Edit first (i) extracts progressively precise and self-consistent region separation from the model's own predictions by lightweight post-processing, and then (ii) enforces preservation in non-edit areas through correction updates that remain aligned with the original sampling dynamics. Extensive experiments demonstrate that SR-Edit achieves superior preservation and overall image quality compared to existing editing techniques.
Read the full abstract ▾
All authors Andong Wang, Zehua Chen, Yuxuan Jiang, Jun Zhu
Computer Vision
Blending Concepts: Benchmarking Visual Metaphor Generation in Text-to-Image Models
Chuer Chen, Zichen Wang, Yi He et al. (+2 more)
Text-to-image (T2I) models have achieved remarkable success at faithfully rendering specified objects and attributes, yet their ability to produce visual metaphors, images that convey abstract ideas by combining elements from two distinct domains, remains largely unexamined. To bridge this gap, we introduce VMetaphor-Bench, the first
benchmark for evaluating visual metaphor generation in T2I models. It comprises 1,500 visual metaphors curated from real-world creative imagery, organized into three levels and ten categories, with each sample paired with two
prompts of differing specificity. For evaluation, we develop a hybrid framework within an MLLM-as-judge paradigm, combining a multiple-choice question (MCQ) based protocol of 9,594 questions across four levels of metaphorical fidelity with a dimension-based scoring protocol along three perceptual dimensions. Extensive evaluation of 11 representative T2I models reveals that even the strongest proprietary models struggle with compositional structuring and cross-domain mapping, key aspects of metaphorical expression, highlighting visual metaphor generation as an important frontier for future T2I research.
Read the full abstract ▾
All authors Chuer Chen, Zichen Wang, Yi He, Zhengxi Yu, Nan Cao
Computer Vision · Cross-listed from cs.IR
ViSAR: Training-Free Adaptive-$k$ Retrieval for Visual Document Question Answering
Adrien Mialland, Marc Plantevit, Julien Gallois et al. (+1 more)
Document Visual Question Answering (DocVQA) often leverages
Retrieval-Augmented Generation (RAG), where late-interaction encoders are commonly used to identify document pages relevant to a user query, before answer generation by a Large
Vision-
Language Model (LVLM). Existing approaches typically retrieve a fixed top-$k$ number of pages regardless of query complexity, which increases LVLM latency and may degrade answer accuracy. We introduce ViSAR (Visual Semantic Activation
Retrieval), a training-free adaptive-$k$
retrieval method for late-interaction visual document
retrieval. ViSAR operates directly in the
embedding space to construct a query-conditioned page-level similarity
matrix that highlights query-relevant semantics and dynamically determines the number of pages to retrieve. Across multiple encoders and LVLMs, ViSAR retrieves compact, query-adapted page sets that reduce RAG latency by up to 58.7%, while maintaining or improving answer accuracy compared with fixed top-$k$ and adaptive
retrieval heuristics. Furthermore, we show that the similarity
matrix structure correlates with answer accuracy, suggesting future directions for
retrieval quality-aware document understanding.
Read the full abstract ▾
All authors Adrien Mialland, Marc Plantevit, Julien Gallois, Céline Robardet
Computer Vision
UnCapsTSR: An Unsupervised Transformer-based Image Super-Resolution Approach for Capsule Endoscopy Images
Anjali Sarvaiya, Shubh Kawa, Lalit Agrawal et al. (+3 more)
Wireless Capsule Endoscopy (WCE) captures and streams
video while passing through a patient's Gastrointestinal (GI) tract and is used to examine its irregularities. Although advantageous over conventional endoscopy, WCE suffers from limitations related to capsule size and wireless transmission, resulting in images with coarser resolution. This work presents UnCapsTSR, an unsupervised
transformer-based Generative
Adversarial Network (GAN) framework for improving the spatial resolution of Low-Resolution (LR) WCE images. The proposed method accomplishes SR without explicit degradation estimation of real-world LR data and eliminates the need for true LR-HR pairs. UnCapsTSR employs a Bilateral Total Variation (BTV) loss to ensure spatial continuity in SR images. A newly curated
dataset from the Kvasir Capsule
dataset is also presented for training WCE SR models. Generalizability is validated on KID and GIANA
datasets that are not used during training. A new non-reference metric, Endoscopy Quality Metric (EndoQM), is introduced for quantitative evaluation of domain-specific WCE data. Experiments demonstrate consistent improvement over state-of-the-art unsupervised SR approaches using NIQE, BRISQUE, PIQE, and EndoQM. Statistical evaluation shows 40 to 80 percent improvement in EndoQM from LR to SR across the evaluated
datasets.
Read the full abstract ▾
All authors Anjali Sarvaiya, Shubh Kawa, Lalit Agrawal, Jagrit Joshi, Kishor Upla, Kiran Raja
Computer Vision
Learning to Track from Privileged Target Appearances
Xin Chen, Jiao Xu, Dong Wang et al. (+2 more)
Target templates define what a visual tracker searches for, yet the templates available at inference trade off localization certainty with appearance freshness: the initial ground-truth template is exact but becomes stale, whereas recent templates better reflect the current appearance but are cropped from uncertain predictions. We quantify this bottleneck with a non-deployable oracle that supplies an exact current-frame target crop, improving AUC on LaSOT by 15.2 percentage points. This gap reveals a training-only opportunity: frame-level ground truths provide exact current- and future-frame target crops, although such crops are unavailable at deployment. We introduce Privileged Appearance Transfer for Tracking (PATT), a teacher-student training framework that transfers these privileged appearances to a deployable tracker through multi-level representation prediction. The privileged teacher observes exact target crops from past, current, and future frames, whereas the student receives only past-frame templates and learns to predict the teacher's search representations. To avoid transferring unreliable teacher signals, PATT weights this transfer by the teacher's relative localization advantage over the student and its absolute localization accuracy. After training, the teacher, latent predictor, reliability weights, and privileged crops are removed, leaving standard student-only inference. Across seven
benchmarks at two model scales, PATT achieves consistent gains under both long- and short-term tracking protocols.
Read the full abstract ▾
All authors Xin Chen, Jiao Xu, Dong Wang, Huchuan Lu, Kede Ma
Computer Vision
VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation
Hoonhee Cho, Jae-Young Kang, Giwon Lee et al. (+3 more)
End-to-end autonomous driving in urban environments requires robust decision-making under partial observability and complex multi-
agent interactions. Severe occlusions and dense traffic at intersections limit the perception capability of single-
agent systems, motivating recent efforts on Vehicle-to-Infrastructure (V2I) cooperation for perception and planning. However, existing evaluation protocols face a fundamental trade-off: open-loop evaluation fails to capture error accumulation and recovery from deviations, while closed-loop evaluation is costly, difficult to scale, and often relies on simulated environments that may suffer from domain gaps. To bridge this gap, we propose VIPS, a
benchmark for cooperative autonomous driving in V2I settings based on pseudo-simulation. VIPS extends pseudo-simulation by integrating vehicle and infrastructure observations. This enables scalable yet realistic evaluation of robustness and error propagation without full simulation. We further present CoS-V2X, a cooperative planning framework based on sparse representations. CoS-V2X models vehicle-infrastructure interactions using compact features for efficient communication and robust decision-making under heterogeneous observations. Code and
dataset are available at https://vips2026.github.io.
Read the full abstract ▾
All authors Hoonhee Cho, Jae-Young Kang, Giwon Lee, Hyemin Yang, Heejun Park, Kuk-Jin Yoon
Computer Vision
WiFlow: Estimating Optical Flow using WiFi Channel State Information
Thomas Weigel, Simon Kiefhaber, Fabian Portner et al. (+2 more)
Knowing where and how fast objects are moving within a scene is important across various domains. Usually, cameras are used to capture the data necessary for this task, but adding cameras often raises
privacy concerns, and the quality of captured frames is heavily influenced by lighting conditions. In this work, we explore using WiFi channel state information (CSI) instead of camera frames for optical flow estimation. We propose WiFlow, a CSI based flow
estimator, a preprocessor evaluation for CSI, and three model architectures that offer different trade-offs between accuracy and complexity. Further, we create the first
dataset for training and evaluating CSI-based optical flow
estimators, and our experiments provide insights into key design elements for this task. Code and data are available at https://visinf.github.io/wiflow.
Read the full abstract ▾
All authors Thomas Weigel, Simon Kiefhaber, Fabian Portner, Matthias Hollick, Simone Schaub-Meyer