Computer Vision
On the Role of Rotation Equivariance in Monocular 2D-to-3D Human Pose Lifting
Pavlo Melnyk, Cuong Le, Urs Waldmann et al. (+2 more)
We consider
monocular 3D human pose estimation (HPE), where the goal is to predict 3D human skeletal joints from a single 2D image, typically via 2D keypoint detection followed by 2D-to-3D lifting. Despite their success, we find that current lifting models exhibit strong performance degradation under rotations. We systematically study rotation equivariance across lifting architectures with increasing degrees of equivariant inductive bias, and investigate whether equivariance should be enforced by architectural design or learned from data, given the inherent ambiguity of
monocular 2D-to-3D lifting. Utilising common HPE
benchmarks, we demonstrate that rotation equivariance can be effectively learned via rotation-based data augmentation applied jointly to input and output poses. Compared with non-augmented models, this reduces error on rotated poses by over 30% across the
benchmarks, with reductions of up to 72%. Moreover, augmentation-based models achieve up to 15% lower error than methods that are fully equivariant by design, while providing up to 37$\times$ faster inference. We further show that these findings generalise to real-world human pose sequences involving full-body rotations and to a state-of-the-art HPE model.
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All authors Pavlo Melnyk, Cuong Le, Urs Waldmann, Per-Erik Forssén, Bastian Wandt
Computer Vision
Towards Visually Explaining Statistical Tests with Applications in Biomedical Imaging
Masoumeh Javanbakhat, Piotr Komorowski, Dilyara Bareeva et al. (+3 more)
Deep neural two-sample tests have recently shown strong power for detecting distributional differences between groups, yet their black-box nature limits interpretability and practical adoption in biomedical analysis. Moreover, most existing post-hoc explainability methods rely on class labels, making them unsuitable for label-free statistical testing settings. We propose an explainable deep statistical testing framework that augments deep two-sample tests with sample-level and feature-level explanations, revealing which individual samples and which input features drive statistically significant group differences. Our method highlights which image regions and which individual samples contribute most to the detected group difference, providing spatial and instance-wise insight into the test's decision. Applied to biomedical imaging data, the proposed framework identifies influential samples and highlights anatomically meaningful regions associated with disease-related variation. This work bridges statistical inference and explainable AI, enabling interpretable, label-free population analysis in medical imaging.
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All authors Masoumeh Javanbakhat, Piotr Komorowski, Dilyara Bareeva, Wei-Chang Lai, Wojciech Samek, Christoph Lippert
Computer Vision
OmniOVCD: Streamlining Open-Vocabulary Change Detection with SAM 3
Xu Zhang, Danyang Li, Yingjie Xia et al. (+4 more)
Change Detection (CD) is a fundamental task in remote sensing. It monitors the
evolution of land cover over time. Based on this, Open-Vocabulary Change Detection (OVCD) introduces a new requirement. It aims to reduce the reliance on predefined categories. Existing training-free OVCD methods mostly use CLIP to identify categories. These methods also need extra models like DINO to extract features. However, combining different models often causes problems in matching features and makes the system unstable. Recently, the Segment Anything Model 3 (SAM 3) is introduced. It integrates
segmentation and identification capabilities within one promptable model, which offers new possibilities for the OVCD task. In this paper, we propose OmniOVCD, a standalone framework designed for OVCD. By leveraging the decoupled output heads of SAM 3, we propose a Synergistic Fusion to Instance Decoupling (SFID) strategy. SFID first fuses the semantic, instance, and presence outputs of SAM 3 to construct land-cover masks, and then decomposes them into individual instance masks for change comparison. This design preserves high accuracy in category recognition and maintains instance-level consistency across images. As a result, the model can generate accurate change masks. Experiments on four public
benchmarks (LEVIR-CD, WHU-CD, S2Looking, and SECOND) demonstrate SOTA performance, achieving IoU scores of 67.2, 66.5, 24.5, and 27.1 (class-average), respectively, surpassing all previous methods. The code is available at https://github.com/Erxucomeon/OmniOVCD.
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All authors Xu Zhang, Danyang Li, Yingjie Xia, Xiaohang Dong, Hualong Yu, Jianye Wang, Qicheng Li
Computer Vision
MTV: Revisiting Multi-Task Visual Representation Learning
Shangzhe Di, Zhonghua Zhai and Weidi Xie
Current visual representation learning remains bifurcated:
vision-
language models (e.g., CLIP) excel at global semantic
alignment but lack spatial precision, while self-supervised methods (e.g., MAE, DINO) capture intricate local structures yet struggle with high-level semantic context. We argue that these paradigms are fundamentally complementary and can be integrated into a principled multi-task framework, further enhanced by dense spatial supervision. We introduce MTV, a multi-task visual pretraining framework that jointly optimizes a shared backbone across
vision-
language contrastive, self-supervised, and dense spatial objectives. To mitigate the need for manual annotations, we leverage high-capacity "expert" models–such as Depth Anything V2 and OWLv2–to synthesize dense, structured pseudo-labels at scale. Beyond the framework, we provide a systematic investigation into the mechanics of multi-task visual learning, analyzing: (i) the individual and marginal gain of each objective, (ii) task synergies versus interference, and (iii) scaling behavior across varying data and model scales. Our results demonstrate that MTV achieves "best-of-both-worlds" performance, significantly enhancing fine-grained spatial
reasoning without compromising global semantic understanding. Our findings suggest that multi-task learning, fueled by high-quality pseudo-supervision, is a scalable path toward more general visual encoders.
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Computer Vision · Cross-listed from cs.MM
Chain-of-Thought Compression Should Not Be Blind: V-Skip for Efficient Multimodal Reasoning via Dual-Path Anchoring
Dongxu Zhang, Yiding Sun, Cheng Tan et al. (+4 more)
While Chain-of-Thought (CoT)
reasoning significantly enhances the performance of
Multimodal Large
Language Models (MLLMs), its autoregressive nature incurs prohibitive latency constraints. Current efforts to mitigate this via
token compression often fail by blindly applying text-centric metrics to
multimodal contexts. We identify a critical failure mode termed Visual Amnesia, where linguistically redundant
tokens are erroneously pruned, leading to
hallucinations. To address this, we introduce V-Skip that reformulates
token pruning as a Visual-Anchored Information Bottleneck (VA-IB) optimization problem. V-Skip employs a dual-path gating mechanism that weighs
token importance through both linguistic surprisal and cross-modal
attention flow, effectively rescuing visually salient anchors. Extensive experiments on Qwen2-VL and Llama-3.2 families demonstrate that V-Skip achieves a $2.9\times$ speedup with negligible accuracy loss. Specifically, it preserves fine-grained visual details, outperforming other baselines over 30% on the DocVQA.
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All authors Dongxu Zhang, Yiding Sun, Cheng Tan, Wenbiao Yan, Ning Yang, Jihua Zhu, Haijun Zhang
Computer Vision
OCCAM: Class-Agnostic, Training-Free, Prior-Free and Multi-Class Object Counting
Michail Spanakis, Iason Oikonomidis and Antonis Argyros
Class-Agnostic object Counting (CAC) involves counting instances of objects from arbitrary classes within an image. Due to its practical importance, CAC has received increasing
attention in recent years. Most existing methods assume a single object class per image, rely on extensive training of large deep learning models and address the problem by incorporating additional information, such as visual exemplars or text
prompts. In this paper, we present OCCAM, the first training-free approach to CAC that operates without the need of any supplementary information. Moreover, our approach addresses the multi-class variant of the problem, as it is capable of counting the object instances in each and every class among arbitrary object classes within an image. We leverage Segment Anything Model 2 (SAM2), a foundation model, and a custom threshold-based variant of the First Integer Neighbor Clustering Hierarchy (FINCH)
algorithm to achieve competitive performance on widely used
benchmark datasets, FSC-147 and CARPK. We propose a synthetic multi-class
dataset and F1 score as a more suitable evaluation metric. The code for our method and the proposed synthetic
dataset will be made publicly available at https://mikespanak.github.io/OCCAM_counter.
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Computer Vision
Probabilistic Deep Discriminant Analysis for Wind Blade Segmentation
Raül Pérez-Gonzalo, Andreas Espersen and Antonio Agudo
Linear discriminant analysis improves class separability but struggles with non-linearly separable data. To overcome this, we introduce Deep Discriminant Analysis (DDA), which directly optimizes the Fisher criterion utilizing deep networks. To ensure stable training and avoid computational instabilities, we incorporate signed between-class variance, bound outputs with a sigmoid function, and convert multiplicative relationships into additive ones. We present two stable DDA loss functions and augment them with a probability loss, resulting in Probabilistic DDA (PDDA). PDDA effectively minimizes class overlap in output distributions, producing highly confident predictions with reduced within-class variance. When applied to wind blade
segmentation, PDDA showcases notable advances in performance and consistency, critical for wind energy maintenance. To our knowledge, this is the first application of DDA to image
segmentation.
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Computer Vision
DisasterVQA: A Visual Question Answering Benchmark Dataset for Disaster Scenes
Aisha Al-Mohannadi, Ayisha Firoz, Yin Yang et al. (+2 more)
Social media imagery provides a low-latency source of situational information during natural and human-induced disasters, enabling rapid damage assessment and response. While Visual Question Answering (VQA) has shown strong performance in general-purpose domains, its suitability for the complex and safety-critical
reasoning required in disaster response remains unclear. We introduce DisasterVQA, a
benchmark dataset designed for perception and
reasoning in crisis contexts. DisasterVQA consists of 1,395 real-world images and 4,405 expert-curated question-answer pairs spanning diverse events such as floods, wildfires, and earthquakes. Grounded in humanitarian frameworks including FEMA ESF and OCHA MIRA, the
dataset includes binary, multiple-choice, and open-ended questions covering situational awareness and operational decision-making tasks. We
benchmark seven state-of-the-art
vision-
language models and find performance variability across question types, disaster categories, regions, and humanitarian tasks. Although models achieve high accuracy on binary questions, they struggle with fine-grained quantitative
reasoning, object counting, and context-sensitive interpretation, particularly for underrepresented disaster scenarios. DisasterVQA provides a challenging and practical
benchmark to guide the development of more robust and operationally meaningful
vision-
language models for disaster response. The
dataset is publicly available at https://doi.org/10.5281/zenodo.18267769.
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All authors Aisha Al-Mohannadi, Ayisha Firoz, Yin Yang, Muhammad Imran, Ferda Ofli
Computer Vision
FastGHA: Generalized Few-Shot 3D Gaussian Head Avatars with Real-Time Animation
Xinya Ji, Sebastian Weiss, Manuel Kansy et al. (+4 more)
Despite recent progress in 3D Gaussian-based head avatar modeling, efficiently generating high fidelity avatars remains a challenge. Current methods typically rely on extensive multi-view capture setups or
monocular videos with per-identity optimization during inference, limiting their scalability and ease of use on unseen subjects. To overcome these efficiency drawbacks, we propose FastGHA, a feed-forward method to generate high-quality Gaussian head avatars from only a few input images while supporting real-time animation. Our approach directly learns a per-pixel Gaussian representation from the input images, and aggregates multi-view information using a
transformer-based encoder that fuses image features from both DINOv3 and Stable
Diffusion VAE. For real-time animation, we extend the explicit Gaussian representations with per-Gaussian features and introduce a lightweight MLP-based dynamic network to predict 3D Gaussian deformations from expression codes. Furthermore, to enhance
geometric smoothness of the 3D head, we employ point maps from a pre-trained large reconstruction model as geometry supervision. Experiments show that our approach significantly outperforms existing methods in both rendering quality and inference efficiency, while supporting real-time dynamic avatar animation.
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All authors Xinya Ji, Sebastian Weiss, Manuel Kansy, Jacek Naruniec, Xun Cao, Barbara Solenthaler, Derek Bradley
Computer Vision · Cross-listed from cs.CL
FutureOmni: Evaluating Future Forecasting from Omni-Modal Context for Multimodal LLMs
Qian Chen, Jinlan Fu, Changsong Li et al. (+3 more)
Although
Multimodal Large
Language Models (MLLMs) demonstrate strong omni-modal perception, their ability to forecast future events from audio-visual cues remains largely unexplored, as existing
benchmarks focus mainly on retrospective understanding. To bridge this gap, we introduce FutureOmni, the first
benchmark designed to evaluate omni-modal future
forecasting from audio-visual environments. The evaluated models are required to perform cross-modal causal and temporal
reasoning, as well as effectively leverage internal knowledge to predict future events. FutureOmni is constructed via a scalable LLM-assisted, human-in-the-loop pipeline and contains 919
videos and 1,034 multiple-choice QA pairs across 8 primary domains. Evaluations on 13 omni-modal and 7
video-only models show that current systems struggle with audio-visual future prediction, particularly in
speech-heavy scenarios, with the best accuracy of 64.8% achieved by Gemini 3 Flash. To mitigate this limitation, we curate a 7K-sample instruction-tuning
dataset and propose an Omni-Modal Future
Forecasting (OFF) training strategy. Evaluations on FutureOmni and popular audio-visual and
video-only
benchmarks demonstrate that OFF enhances future
forecasting and generalization. We publicly release all code (https://github.com/OpenMOSS/FutureOmni) and
datasets (https://huggingface.co/
datasets/OpenMOSS-Team/FutureOmni).
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All authors Qian Chen, Jinlan Fu, Changsong Li, Min Zhang, See-Kiong Ng, Xipeng Qiu
Computer Vision
Discriminant Learning-based Colorspace for Blade Segmentation
Raül Pérez-Gonzalo, Andreas Espersen and Antonio Agudo
Suboptimal color representation often hinders accurate image
segmentation, yet many modern
algorithms neglect this critical preprocessing step. This work presents a novel multidimensional nonlinear discriminant analysis
algorithm, Colorspace Discriminant Analysis (CSDA), for improved
segmentation. Extending Linear Discriminant Analysis into a deep learning context, CSDA customizes color representation by maximizing multidimensional signed inter-class separability while minimizing intra-class variability through a generalized discriminative loss. To ensure stable training, we introduce three alternative losses that enable end-to-end optimization of both the discriminative colorspace and
segmentation process. Experiments on wind turbine blade data demonstrate significant accuracy gains, emphasizing the importance of tailored preprocessing in domain-specific
segmentation.
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Computer Vision
CFM: Language-aligned Concept Foundation Model for Vision
Kai Wittenmayer, Sukrut Rao, Amin Parchami-Araghi et al. (+2 more)
Language-aligned
vision foundation models perform strongly across diverse downstream tasks. Yet, their learned representations remain opaque, making interpreting their decision-making difficult. Recent work decompose these representations into human-interpretable concepts, but provide poor spatial grounding and are limited to image classification tasks. In this work, we propose CFM, a
language-aligned concept foundation model for
vision that provides fine-grained concepts, which are human-interpretable and spatially grounded in the input image. When paired with a foundation model with strong semantic representations, we get explanations for any of its downstream tasks. Examining local co-occurrence dependencies of concepts allows us to define concept relationships through which we improve concept naming and obtain richer explanations. On
benchmark data, we show that CFM provides performance on classification,
segmentation, and captioning that is competitive with opaque foundation models while providing fine-grained, high quality concept-based explanations. Code at https://github.com/kawi19/CFM. Interactive visualizations at https://concept-foundation-model.mpi-inf.mpg.de.
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All authors Kai Wittenmayer, Sukrut Rao, Amin Parchami-Araghi, Bernt Schiele, Jonas Fischer
Computer Vision
PREGEN: Uncovering Latent Thoughts in Composed Video Retrieval
Gabriele Serussi, David Vainshtein, Jonathan Kouchly et al. (+2 more)
Composed
Video Retrieval (CoVR) aims to retrieve a
video based on a query
video and a modifying text. Current CoVR methods fail to fully exploit modern
Vision-
Language Models (VLMs), either using outdated architectures or requiring computationally expensive fine-tuning and slow caption generation. We introduce PREGEN (PRE GENeration extraction), an efficient and powerful CoVR framework that overcomes these limitations. Our approach uniquely pairs a frozen, pre-trained VLM with a lightweight encoding model, eliminating the need for any VLM fine-tuning. We feed the query
video and modifying text into the VLM and extract the hidden state of the final
token from each layer. A simple encoder is then trained on these pooled representations, creating a semantically rich and compact
embedding for
retrieval. PREGEN significantly advances the state of the art, surpassing all prior methods on standard CoVR
benchmarks with substantial gains in Recall@1 of +27.23 and +69.59. Our method demonstrates robustness across different VLM backbones and exhibits strong zero-shot generalization to more complex textual modifications, highlighting its effectiveness and semantic capabilities.
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All authors Gabriele Serussi, David Vainshtein, Jonathan Kouchly, Dotan Di Castro, Chaim Baskin
Computer Vision
Towards Onboard Continuous Change Detection for Floods
Daniel Kyselica, Jonáš Herec, Oliver Kutis et al. (+1 more)
Natural disaster monitoring through continuous satellite observation requires processing multi-temporal data under strict operational constraints. This paper addresses flood detection, a critical application for hazard management, by developing an onboard change detection system that operates within the
memory and computational limits of small satellites. We propose History Injection mechanism for
Transformer models (HiT), that maintains historical context from previous observations while reducing data storage by over 99% of original image size. Moreover, testing on the STTORM-CD flood
dataset confirms that the HiT mechanism within the Prithvi-tiny foundation model maintains detection accuracy compared to the bi-temporal baseline. The proposed HiT-Prithvi model achieved 43 FPS on Jetson Orin Nano, a representative onboard hardware used in nanosats. This work establishes a practical framework for satellite-based continuous monitoring of natural disasters, supporting real-time hazard assessment without dependency on ground-based processing infrastructure. Architecture as well as model checkpoints is available at https://github.com/zaitra/HiT-change-detection .
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All authors Daniel Kyselica, Jonáš Herec, Oliver Kutis, Rado Pitoňák
Computer Vision
Facial Spatiotemporal Graphs: Leveraging the 3D Facial Surface for Remote Physiological Measurement
Sam Cantrill, David Ahmedt-Aristizabal, Lars Petersson et al. (+2 more)
Facial remote photoplethysmography (rPPG) methods estimate physiological signals by modeling subtle color changes on the 3D facial surface over time. However, existing methods fail to explicitly align their receptive fields with the 3D facial surface-the spatial support of the rPPG signal. To address this, we propose the Facial Spatiotemporal Graph (STGraph), a novel representation that encodes facial color and structure using 3D facial mesh sequences-enabling surface-aligned spatiotemporal processing. We introduce MeshPhys, a lightweight spatiotemporal graph convolutional network that operates on the STGraph to estimate physiological signals. Across four
benchmark datasets, MeshPhys achieves state-of-the-art or competitive performance in both intra- and cross-
dataset settings. Ablation studies show that constraining the model's receptive field to the facial surface acts as a strong structural prior, and that surface-aligned, 3D-aware node features are critical for robustly encoding facial surface color. Together, the STGraph and MeshPhys constitute a novel, principled modeling paradigm for facial rPPG, enabling robust, interpretable, and generalizable estimation. Code is available at https://samcantrill.github.io/facial-stgraph-rppg/ .
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All authors Sam Cantrill, David Ahmedt-Aristizabal, Lars Petersson, Hanna Suominen, Mohammad Ali Armin
Computer Vision
Hierarchical Long Video Understanding with Audiovisual Entity Cohesion and Agentic Search
Xinlei Yin, Xiulian Peng, Xiao Li et al. (+2 more)
Long
video understanding presents significant challenges for
vision-
language models due to extremely long context windows. Existing solutions relying on naive chunking strategies with
retrieval-augmented generation, typically suffer from information fragmentation and a loss of global coherence. We present HAVEN, a unified framework for long-
video understanding that enables coherent and comprehensive
reasoning by integrating audiovisual entity cohesion and hierarchical
video indexing with agentic search. First, we preserve semantic consistency by integrating entity-level representations across visual and auditory streams, while organizing content into a structured hierarchy spanning global summary, scene, segment, and entity levels. Then we employ an agentic search mechanism to enable dynamic
retrieval and
reasoning across these layers, facilitating coherent narrative reconstruction and fine-grained entity tracking. Extensive experiments demonstrate that our method achieves good temporal coherence, entity consistency, and
retrieval efficiency, establishing a new state-of-the-art with an overall accuracy of 84.1% on LVBench. Notably, it achieves outstanding performance in the challenging
reasoning category, reaching 80.1%. These results highlight the effectiveness of structured,
multimodal reasoning for comprehensive and context-consistent understanding of long-form
videos.
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All authors Xinlei Yin, Xiulian Peng, Xiao Li, Zhiwei Xiong, Yan Lu
Computer Vision
MVGD-Net: A Novel Motion-aware Video Glass Surface Detection Method
Yiwei Lu, Hao Huang and Tao Yan
Glass surface ubiquitous in both daily life and professional environments presents a potential threat to
vision-based systems, such as
robot and drone
navigation. To solve this challenge, most recent studies have shown significant interest in
Video Glass Surface Detection (VGSD). We observe that objects in the reflection (or transmission) layer appear farther from the glass surfaces. Consequently, in
video motion scenarios, the notable reflected (or transmitted) objects on the glass surface move slower than objects in non-glass regions within the same spatial plane, and this motion inconsistency can effectively reveal the presence of glass surfaces. Based on this observation, we propose a novel network, named MVGD-Net, for detecting glass surfaces in
videos by leveraging motion inconsistency cues. Our MVGD-Net features three novel modules: the Cross-scale
Multimodal Fusion Module (CMFM) that integrates extracted spatial features and estimated optical flow maps, the History Guided
Attention Module (HGAM) and Temporal Cross
Attention Module (TCAM), both of which further enhances temporal features. A Temporal-Spatial Decoder (TSD) is also introduced to fuse the spatial and temporal features for generating the glass region mask. Furthermore, for learning our network, we also propose a large-scale
dataset, which comprises 312 diverse glass scenarios with a total of 19,268 frames. Extensive experiments demonstrate that our MVGD-Net outperforms relevant state-of-the-art methods.
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Computer Vision
Attention-space Contrastive Guidance for Efficient Hallucination Mitigation in LVLMs
Yujin Jo, Sangyoon Bae and Taesup Kim
Hallucinations in large
vision–
language models (LVLMs) often arise when
language priors dominate over visual evidence, leading to object misidentification and visually inconsistent descriptions. We address this problem by framing
hallucination mitigation as contrastive guidance that steers generation toward visually grounded and semantically faithful text. We propose
Attention-space Contrastive Guidance (ACG), a training-free, single-pass method that operates directly in self-
attention layers, where
hallucination-inducing cross-modal biases emerge. ACG constructs both image-conditioned and approximate text-only
attention paths within a single forward pass, enabling efficient guidance before errors accumulate at the output layer. Because this masking-based surrogate can introduce approximation bias, we further apply a lightweight orthogonal projection that suppresses components aligned with the text-only path, yielding a more visually grounded correction. Experiments on CHAIR and POPE show that ACG improves faithfulness over existing training-free baselines while maintaining caption quality, reducing latency by up to $2\times$ compared to multi-pass contrastive decoding methods.
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Computer Vision
ParkingTwin: Training-Free Streaming 3D Reconstruction for Parking-Lot Digital Twins
Xinhao Liu, Yu Wang, Xiansheng Guo et al. (+5 more)
High-fidelity parking-lot digital twins provide essential priors for path planning, collision checking, and perception validation in Automated Valet Parking (AVP). Yet
robot-oriented reconstruction faces a trilemma: sparse forward-facing views cause weak parallax and ill-posed geometry; dynamic occlusions and extreme lighting hinder stable texture fusion; and neural rendering typically needs expensive offline optimization, violating edge-side streaming constraints. We propose ParkingTwin, a training-free, lightweight system for online streaming 3D reconstruction. First, OSM-prior-driven
geometric construction uses OpenStreetMap semantic
topology to directly generate a metric-consistent TSDF, replacing blind
geometric search with deterministic mapping and avoiding costly optimization. Second, geometry-aware dynamic filtering employs a quad-modal constraint field (normal/height/depth consistency) to reject moving vehicles and transient occlusions in real time. Third, illumination-robust fusion in CIELAB decouples luminance and chromaticity via adaptive L-channel weighting and depth-gradient suppression, reducing seams under abrupt lighting changes. ParkingTwin runs at 30+ FPS on an entry-level GTX 1660. On a 68,000 m^2 real-world
dataset, it achieves SSIM 0.87 (+16.0%), delivers about 15x end-to-end speedup, and reduces GPU
memory by 83.3% compared with state-of-the-art 3D Gaussian Splatting (3DGS) that typically requires high-end GPUs (RTX 4090D). The system outputs explicit triangle meshes compatible with Unity/Unreal digital-twin pipelines. Project page: https://mihoutao-liu.github.io/ParkingTwin/
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All authors Xinhao Liu, Yu Wang, Xiansheng Guo, Gordon Owusu Boateng, Yu Cao, Haonan Si, Xingchen Guo, Nirwan Ansari
Computer Vision
Reasoning or Pattern Matching? Probing Large Vision-Language Models with Visual Puzzles
Maria Lymperaiou, Vasileios Karampinis, Giorgos Filandrianos et al. (+3 more)
Puzzles have long served as compact and revealing probes of human cognition, isolating abstraction, rule discovery, and systematic
reasoning with minimal reliance on prior knowledge. Leveraging these properties, visual puzzles have recently emerged as a powerful diagnostic tool for evaluating the
reasoning abilities of Large
Vision-
Language Models (LVLMs), offering controlled, verifiable alternatives to open-ended
multimodal benchmarks. This survey provides a unified perspective of visual puzzle
reasoning in LVLMs. We frame visual puzzles through a common abstraction and organize existing
benchmarks by the
reasoning mechanisms they target (inductive, analogical, algorithmic, deductive, and
geometric/spatial), thereby linking puzzle design to the cognitive operations required for solving. Synthesizing empirical evidence across these categories, we identify consistent limitations in current models, including brittle generalization, tight
entanglement between perception and
reasoning, and a persistent gap between fluent explanations and faithful execution. By framing visual puzzles as diagnostic instruments rather than task formats, this survey elaborates on the state of LVLM
reasoning and outlines key directions for future
benchmarks and
reasoning-aware
multimodal systems.
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All authors Maria Lymperaiou, Vasileios Karampinis, Giorgos Filandrianos, Angelos Vlachos, Chrysoula Zerva, Athanasios Voulodimos
Computer Vision
Dynamic Differential Linear Attention: Enhancing Linear Diffusion Transformer for High-Quality Image Generation
Boyuan Cao, Xingbo Yao, Chenhui Wang et al. (+3 more)
Diffusion transformers (DiTs) have emerged as a powerful architecture for high-fidelity image generation, yet the quadratic cost of self-
attention poses a major scalability bottleneck. To address this, linear
attention mechanisms have been adopted to reduce computational cost; unfortunately, the resulting linear
diffusion transformers (LiTs) models often come at the expense of generative performance, frequently producing over-smoothed
attention weights that limit expressiveness. In this work, we introduce Dynamic Differential Linear
Attention (DyDiLA), a novel linear
attention formulation that enhances the effectiveness of LiTs by mitigating the oversmoothing issue and improving generation quality. Specifically, the novelty of DyDiLA lies in three key designs: (i) dynamic projection module, which facilitates the decoupling of
token representations by learning with dynamically assigned knowledge; (ii) dynamic measure kernel, which provides a better similarity measurement to capture fine-grained semantic distinctions between
tokens by dynamically assigning kernel functions for
token processing; and (iii)
token differential operator, which enables more robust query-to-key
retrieval by calculating the differences between the
tokens and their corresponding information redundancy produced by dynamic measure kernel. To capitalize on DyDiLA, we introduce a refined LiT, termed DyDi-LiT, that systematically incorporates our advancements. Extensive experiments show that DyDi-LiT consistently outperforms current state-of-the-art (SOTA) models across multiple metrics, underscoring its strong practical potential.
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All authors Boyuan Cao, Xingbo Yao, Chenhui Wang, Jiaxin Ye, Yujie Wei, Hongming Shan
Computer Vision
Finally Outshining the Random Baseline: A Simple and Effective Solution for Active Learning in 3D Biomedical Imaging
Carsten T. Lüth, Jeremias Traub, Kim-Celine Kahl et al. (+6 more)
Active learning (AL) has the potential to drastically reduce annotation costs in 3D biomedical image
segmentation, where expert labeling of volumetric data is both time-consuming and expensive. Yet, existing AL methods are unable to consistently outperform improved random sampling baselines adapted to 3D data, leaving the field without a reliable solution. We introduce Class-stratified Scheduled Power Predictive
Entropy (ClaSP PE), a simple and effective query strategy that addresses two key limitations of standard uncertainty-based AL methods: class imbalance and redundancy in early selections. ClaSP PE combines class-stratified querying to ensure coverage of underrepresented structures and log-scale power noising with a decaying schedule to enforce query diversity in early-stage AL and encourage exploitation later. In our evaluation on 24 experimental settings using four 3D biomedical
datasets within the comprehensive nnActive
benchmark, ClaSP PE is the only method that generally outperforms improved random baselines in terms of both
segmentation quality with statistically significant gains, whilst remaining annotation efficient. Furthermore, we explicitly simulate the real-world application by testing our method on four previously unseen
datasets without manual adaptation, where all experiment parameters are set according to predefined guidelines. The results confirm that ClaSP PE robustly generalizes to novel tasks without requiring
dataset-specific tuning. Within the nnActive framework, we present compelling evidence that an AL method can consistently outperform random baselines adapted to 3D
segmentation, in terms of both performance and annotation efficiency in a realistic, close-to-production scenario. Our open-source implementation and clear deployment guidelines make it readily applicable in practice. Code is at https://github.com/MIC-DKFZ/nnActive.
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All authors Carsten T. Lüth, Jeremias Traub, Kim-Celine Kahl, Till J. Bungert, Lukas Klein, Lars Krämer, Paul F. Jäger, Klaus Maier-Hein, Fabian Isensee
Computer Vision
Transformer based Multi-task Fusion Network for Food Spoilage Detection and Shelf life Forecasting
Mounika Kanulla, Rajasree Dadigi, Sailaja Thota et al. (+1 more)
Food wastage is one of the critical challenges in the agricultural supply chain, and accurate and effective spoilage detection can help to reduce it. Further, it is highly important to forecast the spoilage information. This aids the longevity of the supply chain management in the agriculture field. This motivated us to propose fusion based architectures by combining CNN with LSTM and DeiT
transformer for the following multi-tasks simultaneously: (i) vegetable classification, (ii) food spoilage detection, and (iii) shelf life
forecasting. We developed a
dataset by capturing images of vegetables from their fresh state until they were completely spoiled. From the experimental analysis it is concluded that the proposed fusion architectures CNN+CNN-LSTM and CNN+DeiT
Transformer outperformed several deep learning models such as CNN, VGG16, ResNet50, Capsule Networks, and DeiT
Transformers. Overall, CNN + DeiT
Transformer yielded F1-score of 0.98 and 0.61 in vegetable classification and spoilage detection respectively and mean squared error (MSE) and symmetric mean absolute percentage error (SMAPE) of 3.58, and 41.66% respectively in spoilage
forecasting. Further, the reliability of the fusion models was validated on noisy images and integrated with LIME to visualize the model decisions.
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All authors Mounika Kanulla, Rajasree Dadigi, Sailaja Thota, Vivek Yelleti
Computer Vision
VIAFormer: Voxel-Image Alignment Transformer for High-Fidelity Voxel Refinement
Tiancheng Fang, Bowen Pan, Lingxi Chen et al. (+4 more)
We propose VIAFormer, a Voxel-Image
Alignment Transformer model designed for Multi-view Conditioned Voxel Refinement–the task of repairing incomplete noisy voxels using calibrated multi-view images as guidance. Its effectiveness stems from a synergistic design: an Image Index that provides explicit 3D spatial grounding for 2D image
tokens, a Correctional Flow objective that learns a direct voxel-refinement trajectory, and a Hybrid Stream
Transformer that enables robust cross-modal fusion. Experiments show that VIAFormer establishes a new state of the art in correcting both severe synthetic corruptions and realistic artifacts on the voxel shape obtained from powerful
Vision Foundation Models. Beyond benchmarking, we demonstrate VIAFormer as a practical and reliable bridge in real-world 3D creation pipelines, paving the way for voxel-based methods to thrive in large-model, big-data wave.
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All authors Tiancheng Fang, Bowen Pan, Lingxi Chen, Jiangjing Lyu, Chengfei Lyu, Chaoyue Niu, Fan Wu
Computer Vision
Face-Voice Association with Inductive Bias for Maximum Class Separation
Marta Moscati, Oleksandr Kats, Mubashir Noman et al. (+4 more)
Face-voice association is widely studied in
multimodal learning and is approached representing faces and voices with
embeddings that are close for a same person and well separated from those of others. Previous work achieved this with loss functions. Recent advancements in classification have shown that the discriminative ability of
embeddings can be strengthened by imposing maximum class separation as inductive bias. This technique has never been used in the domain of face-voice association, and this work aims at filling this gap. More specifically, we develop a method for face-voice association that imposes maximum class separation among
multimodal representations of different speakers as an inductive bias. Through quantitative experiments we demonstrate the effectiveness of our approach, showing that it achieves SOTA performance on two task formulation of face-voice association. Furthermore, we carry out an ablation study to show that imposing inductive bias is most effective when combined with losses for inter-class orthogonality. To the best of our knowledge, this work is the first that applies and demonstrates the effectiveness of maximum class separation as an inductive bias in
multimodal learning; it hence paves the way to establish a new paradigm.
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All authors Marta Moscati, Oleksandr Kats, Mubashir Noman, Muhammad Zaigham Zaheer, Yufang Hou, Markus Schedl, Shah Nawaz
Computer Vision · Cross-listed from cs.LG
Quadratic Upper Bound for Boosting Robustness
Euijin You and Hyang-Won Lee
Fast
adversarial training (FAT) aims to enhance the robustness of models against
adversarial attacks with reduced training time, however, FAT often suffers from compromised robustness due to insufficient exploration of
adversarial space. In this paper, we develop a loss function to mitigate the problem of degraded robustness under FAT. Specifically, we derive a quadratic upper bound (QUB) on the
adversarial training (AT) loss function and propose to utilize the bound with existing FAT methods. Our experimental results show that applying QUB loss to the existing methods yields significant improvement of robustness. Furthermore, using various metrics, we demonstrate that this improvement is likely to result from the smoothened loss landscape of the resulting model.
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Computer Vision
EGM: Efficient Visual Grounding Language Models
Guanqi Zhan, Changye Li, Zhijian Liu et al. (+4 more)
Visual grounding is an essential capability of Visual
Language Models (VLMs) to understand the real physical world. Previous state-of-the-art grounding visual
language models usually have large model sizes, making them heavy for deployment and slow for inference. However, we notice that the sizes of visual encoders are nearly the same for small and large VLMs and the major difference is the sizes of the
language models. Small VLMs fall behind larger VLMs in grounding because of the difference in
language understanding capability rather than visual information handling. To mitigate the gap, we introduce 'Efficient visual Grounding
language Models' (EGM): generate many mid-quality
tokens (from small models) to match the performance of large VLMs with few high-quality but expensive
tokens. This method is deployment-friendly, and yields better end-to-end latency: On the RefCOCO
benchmark, our EGM-Qwen3-VL-8B demonstrates 91.4 IoU with an average of 737ms (5.9x faster) latency while Qwen3-VL-235B demands 4,320ms to reach 90.5 IoU. To validate our approach's generality, we further set up a new amodal grounding setting that requires the model to predict both the visible and occluded parts of the objects. Experiments show our method consistently improves both vanilla and amodal grounding capabilities of small models to match or outperform larger models, thereby improving efficiency for visual grounding.
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All authors Guanqi Zhan, Changye Li, Zhijian Liu, Yao Lu, Yi Wu, Song Han, Ligeng Zhu
Computer Vision
CARPE: Context-Aware Image Representation Prioritization via Ensemble for Large Vision-Language Models
Donghee Lee, Rui Cai and Zhe Zhao
Large
vision-
language models (LVLMs) are typically trained using autoregressive
language modeling objectives, which align visual representations with linguistic space. While effective for
multimodal reasoning, this
alignment can weaken
vision-centric capabilities, causing LVLMs to underperform their base
vision encoders on tasks such as image classification. To address this limitation, we propose Context-Aware Image Representation Prioritization via Ensemble (CARPE), a lightweight framework that integrates raw
vision features with aligned LLM representations through
vision-integration layers and a context-aware ensemble mechanism. This design enhances the model's ability to adaptively weight visual and textual modalities and enables the model to capture various aspects of image representations. Extensive experiments demonstrate that CARPE improves performance on both image classification and diverse
vision-
language benchmarks. Our results suggest that modality balancing plays a critical role in
multimodal generalization by improving representation utilization within autoregressive LVLMs.
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Computer Vision
ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch
Zheng Liu, Honglin Lin, Chonghan Qin et al. (+13 more)
Chart
reasoning is a critical capability for
Vision Language Models (VLMs). However, the development of open-source models is severely hindered by the lack of high-quality training data. Existing
datasets suffer from a dual challenge: synthetic charts are often simplistic and repetitive, while the associated QA pairs are prone to
hallucinations and lack the
reasoning depth required for complex tasks. To bridge this gap, we propose ChartVerse, a scalable framework designed to synthesize complex charts and reliable
reasoning data from scratch. (1) To address the bottleneck of simple patterns, we first introduce Rollout Posterior
Entropy (RPE), a novel metric that quantifies chart complexity. Guided by RPE, we develop complexity-aware chart coder to autonomously synthesize diverse, high-complexity charts via executable programs. (2) To guarantee
reasoning rigor, we develop truth-anchored inverse QA synthesis. Diverging from standard generation, we adopt an answer-first paradigm: we extract deterministic answers directly from the source code, generate questions conditional on these anchors, and enforce strict consistency verification. To further elevate difficulty and
reasoning depth, we filter samples based on model fail-rate and distill high-quality Chain-of-Thought (CoT)
reasoning. We curate ChartVerse-SFT-600K and ChartVerse-RL-40K using Qwen3-VL-30B-A3B-Thinking as the teacher. Experimental results demonstrate that ChartVerse-8B achieves state-of-the-art performance, notably surpassing its teacher and rivaling the stronger Qwen3-VL-32B-Thinking. We release our code, model weights, and
datasets in https://chartverse.github.io.
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All authors Zheng Liu, Honglin Lin, Chonghan Qin, Xiaoyang Wang, Xin Gao, Yu Li, Mengzhang Cai, Yun Zhu, Zhanping Zhong, Qizhi Pei, Zhuoshi Pan, Xiaoran Shang, Bin Cui, Conghui He, Wentao Zhang, Lijun Wu
Computer Vision · Cross-listed from cs.LG
FG-OrIU: Towards Better Forgetting via Feature-Gradient Orthogonality for Incremental Unlearning
Qian Feng, JiaHang Tu, Mintong Kang et al. (+3 more)
Incremental unlearning (IU) is critical for pre-trained models to comply with sequential data deletion requests, yet existing methods primarily suppress parameters or confuse knowledge without explicit constraints on both feature and gradient level, resulting in superficial forgetting where residual information remains recoverable. This incomplete forgetting risks security breaches and disrupts retention balance, especially in IU scenarios. We propose FG-OrIU (Feature-Gradient Orthogonality for Incremental Unlearning), the first framework unifying orthogonal constraints on both features and gradients level to achieve deep forgetting, where the forgetting effect is irreversible. FG-OrIU decomposes feature spaces via Singular Value Decomposition (SVD), separating forgetting and remaining class features into distinct subspaces. It then enforces dual constraints: feature orthogonal projection on both forgetting and remaining classes, while gradient orthogonal projection prevents the reintroduction of forgotten knowledge and disruption to remaining classes during updates. Additionally, dynamic subspace adaptation merges newly forgetting subspaces and contracts remaining subspaces, ensuring a stable balance between removal and retention across sequential unlearning tasks. Extensive experiments demonstrate the effectiveness of our method.
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All authors Qian Feng, JiaHang Tu, Mintong Kang, Hanbin Zhao, Chao Zhang, Hui Qian