Computer Vision
Resonance4D: Frequency-Domain Motion Supervision for Preset-Free Physical Parameter Learning in 4D Dynamic Physical Scene Simulation
Changshe Zhang, Jie Feng, Siyu Chen et al. (+3 more)
Physics-driven 4D dynamic simulation from static 3D scenes remains constrained by an overlooked contradiction: reliable motion supervision often relies on online
video diffusion or optical-flow pipelines whose computational cost exceeds that of the simulator itself. Existing methods further simplify inverse physical modeling by optimizing only partial material parameters, limiting realism in scenes with complex materials and dynamics. We present Resonance4D, a physics-driven 4D dynamic simulation framework that couples 3D Gaussian Splatting with the Material Point Method through lightweight yet physically expressive supervision. Our key insight is that dynamic consistency can be enforced without dense temporal generation by jointly constraining motion in complementary domains. To this end, we introduce Dual-domain Motion Supervision (DMS), which combines spatial structural consistency for local deformation with frequency-domain spectral consistency for oscillatory and global dynamic patterns, substantially reducing training cost and
memory overhead while preserving physically meaningful motion cues. To enable stable full-parameter physical recovery, we further combine zero-shot text-prompted
segmentation with simulation-guided initialization to automatically decompose Gaussians into object-part-level regions and support joint optimization of full material parameters. Experiments on both synthetic and real scenes show that Resonance4D achieves strong physical fidelity and motion consistency while reducing peak GPU
memory from over 35\,GB to around 20\,GB, enabling high-fidelity physics-driven 4D simulation on a single consumer-grade GPU.
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All authors Changshe Zhang, Jie Feng, Siyu Chen, Guanbin Li, Ronghua Shang, Junpeng Zhang
Computer Vision
Attention at Rest Stays at Rest: Breaking Visual Inertia to Mitigate Relation Hallucinations
Boyang Gong, Yu Zheng, Fanye Kong et al. (+2 more)
While
multimodal large
language models demonstrate strong entity-level perception, faithfully grounding relational interactions between objects remains a persistent challenge. Although conventional visual grounding techniques attempt to resolve
hallucinations by amplifying visual
attention, strengthening overall visual signals fails to reliably correct relational errors. Tracing visual
attention in relation descriptions reveals that correct responses tend to dynamically shift focus across regions, whereas hallucinated responses often linger on previously dominant evidence, exhibiting an undesirable visual inertia. Further analysis shows that relation-prediction performance steadily deteriorates as more previous-step visual
attention is carried into the current decoding step. We therefore introduce Inertia-aware Visual Excitation (IVE), an MLLM decoding method that dynamically recalibrates visual values using
token-level
attention history. By contrasting current
attention against recent moving averages, IVE separates emergent
tokens with rising relevance from persistently dominant inertia
tokens, selectively reinforcing newly needed evidence while mildly attenuating contributions from repeatedly attended regions. Across three MLLMs and decoding strategies, IVE reduces relation
hallucinations while preserving broader
multimodal performance.
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All authors Boyang Gong, Yu Zheng, Fanye Kong, Jie Zhou, Jiwen Lu
Computer Vision
Curia-2: Scaling Self-Supervised Learning for Radiology Foundation Models
Antoine Saporta, Baptiste Callard, Corentin Dancette et al. (+5 more)
The rapid growth of medical imaging has fueled the development of Foundation Models (FMs) to reduce the growing, unsustainable workload on radiologists. While recent FMs have shown the power of large-scale pre-training to CT and MRI analysis, there remains significant room to optimize how these models learn from complex radiological volumes. Building upon the Curia framework, this work introduces Curia-2, which significantly improves the original pre-training strategy and representation quality to better capture the specificities of radiological data. The proposed methodology enables scaling the architecture up to billion-parameter
Vision Transformers, marking a first for multi-modal CT and MRI FMs. Furthermore, we formalize the evaluation of these models by extending and restructuring CuriaBench into two distinct tracks: a 2D track tailored for slice-based
vision models and a 3D track for volumetric benchmarking. Our results demonstrate that Curia-2 outperforms all FMs on
vision-focused tasks and fairs competitively to
vision-
language models on clinically complex tasks such as finding detection. Weights will be made publicly available to foster further research.
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All authors Antoine Saporta, Baptiste Callard, Corentin Dancette, Julien Khlaut, Charles Corbière, Leo Butsanets, Amaury Prat, Pierre Manceron
Computer Vision
Interactive Tracking: A Human-in-the-Loop Paradigm with Memory-Augmented Adaptation
Yuqing Huang, Guotian Zeng, Zhenqiao Yuan et al. (+4 more)
Existing visual trackers mainly operate in a non-interactive, fire-and-forget manner, making them impractical for real-world scenarios that require human-in-the-loop adaptation. To overcome this limitation, we introduce Interactive Tracking, a new paradigm that allows users to guide the tracker at any time using natural
language commands. To support research in this direction, we make three main contributions. First, we present InteractTrack, the first large-scale
benchmark for interactive tracking, containing 150
videos with dense bounding box annotations and timestamped
language instructions. Second, we propose a comprehensive evaluation protocol and evaluate 25 representative trackers, showing that state-of-the-art methods fail in interactive scenarios; strong performance on conventional
benchmarks does not transfer. Third, we introduce Interactive
Memory-Augmented Tracking (IMAT), a new baseline that employs a dynamic
memory mechanism to learn from user feedback and update tracking behavior accordingly. Our
benchmark, protocol, and baseline establish a foundation for developing more intelligent, adaptive, and collaborative tracking systems, bridging the gap between automated perception and human guidance. The full
benchmark, tracking results, and analysis are available at https://github.com/NorahGreen/InteractTrack.git.
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All authors Yuqing Huang, Guotian Zeng, Zhenqiao Yuan, Zhenyu He, Xin Li, Yaowei Wang, Ming-Hsuan Yang
Computer Vision
NearID: Identity Representation Learning via Near-identity Distractors
Aleksandar Cvejic, Rameen Abdal, Abdelrahman Eldesokey et al. (+2 more)
When evaluating identity-focused tasks such as personalized generation and image editing, existing
vision encoders entangle object identity with background context, leading to unreliable representations and metrics. We introduce the first principled framework to address this vulnerability using Near-identity (NearID) distractors, where semantically similar but distinct instances are placed on the exact same background as a reference image, eliminating contextual shortcuts and isolating identity as the sole discriminative signal. Based on this principle, we present the NearID
dataset (19K identities, 316K matched-context distractors) together with a strict margin-based evaluation protocol. Under this setting, pre-trained encoders perform poorly, achieving Sample Success Rates (SSR), a strict margin-based identity discrimination metric, as low as 30.7% and often ranking distractors above true cross-view matches. We address this by learning identity-aware representations on a frozen backbone using a two-tier contrastive objective enforcing the hierarchy: same identity > NearID distractor > random negative. This improves SSR to 99.2%, enhances part-level discrimination by 28.0%, and yields stronger
alignment with human judgments on DreamBench++, a human-aligned
benchmark for personalization. Project page: https://gorluxor.github.io/NearID/
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All authors Aleksandar Cvejic, Rameen Abdal, Abdelrahman Eldesokey, Bernard Ghanem, Peter Wonka
Computer Vision
SDesc3D: Towards Layout-Aware 3D Indoor Scene Generation from Short Descriptions
Jie Feng, Jiawei Shen, Junjia Huang et al. (+4 more)
3D indoor scene generation conditioned on short textual descriptions provides a promising avenue for interactive 3D environment construction without the need for labor-intensive layout specification. Despite recent progress in text-conditioned 3D scene generation, existing works suffer from poor physical plausibility and insufficient detail richness in such semantic condensation cases, largely due to their reliance on explicit semantic cues about compositional objects and their spatial relationships. This limitation highlights the need for enhanced 3D
reasoning capabilities, particularly in terms of prior integration and spatial anchoring. Motivated by this, we propose SDesc3D, a short-text conditioned 3D indoor scene generation framework, that leverages multi-view structural priors and regional functionality implications to enable 3D layout
reasoning under sparse textual guidance. Specifically, we introduce a Multi-view scene prior augmentation that enriches underspecified textual inputs with aggregated multi-view structural knowledge, shifting from inaccessible semantic relation cues to multi-view relational prior aggregation. Building on this, we design a Functionality-aware layout grounding, employing regional functionality grounding for implicit spatial anchors and conducting hierarchical layout
reasoning to enhance scene organization and semantic plausibility. Furthermore, an Iterative reflection-rectification scheme is employed for progressive structural plausibility refinement via self-rectification. Extensive experiments show that our method outperforms existing approaches on short-text conditioned 3D indoor scene generation. Code will be publicly available.
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All authors Jie Feng, Jiawei Shen, Junjia Huang, Junpeng Zhang, Mingtao Feng, Weisheng Dong, Guanbin Li
Computer Vision
Ego-Grounding for Personalized Question-Answering in Egocentric Videos
Junbin Xiao, Shenglang Zhang, Pengxiang Zhu et al. (+1 more)
We present the first systematic analysis of
multimodal large
language models (MLLMs) in personalized question-answering requiring ego-grounding - the ability to understand the camera-wearer in
egocentric videos. To this end, we introduce MyEgo, the first
egocentric VideoQA
dataset designed to evaluate MLLMs' ability to understand, remember, and reason about the camera wearer. MyEgo comprises 541 long
videos and 5K personalized questions asking about "my things", "my activities", and "my past". Benchmarking reveals that competitive MLLMs across variants, including open-source vs. proprietary, thinking vs. non-thinking, small vs. large scales all struggle on MyEgo. Top closed- and open-source models (e.g., GPT-5 and Qwen3-VL) achieve only~46% and 36% accuracy, trailing human performance by near 40% and 50% respectively. Surprisingly, neither explicit
reasoning nor model scaling yield consistent improvements. Models improve when relevant evidence is explicitly provided, but gains drop over time, indicating limitations in tracking and remembering "me" and "my past". These findings collectively highlight the crucial role of ego-grounding and long-range
memory in enabling personalized QA in
egocentric videos. We hope MyEgo and our analyses catalyze further progress in these areas for
egocentric personalized assistance. Data and code are available at https://github.com/Ryougetsu3606/MyEgo
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All authors Junbin Xiao, Shenglang Zhang, Pengxiang Zhu, Angela Yao
Computer Vision
Automated Prostate Gland Segmentation in MRI Using nnU-Net
Pablo Rodriguez-Belenguer, Gloria Ribas, Javier Aquerreta Escribano et al. (+3 more)
Accurate
segmentation of the prostate gland in multiparametric MRI (mpMRI) is a fundamental step for a wide range of clinical and research applications, including image registration, volume estimation, and radiomic analysis. However, manual delineation is time-consuming and subject to inter-observer variability, while general-purpose
segmentation tools often fail to provide sufficient accuracy for prostate-specific tasks. In this work, we propose a dedicated deep learning-based approach for automatic prostate gland
segmentation using the nnU-Net v2 framework. The model leverages
multimodal mpMRI data, including T2-weighted imaging,
diffusion-weighted imaging (DWI), and apparent
diffusion coefficient (ADC) maps, to exploit complementary tissue information. Training was performed on 981 cases from the PI-CAI
dataset using whole-gland annotations, and model performance was assessed through 5-fold cross-validation and external validation on an independent cohort of 54 patients from Hospital La Fe. The proposed model achieved a mean Dice score of 0.96 +/- 0.00 in cross-validation and 0.82 on the external test set, demonstrating strong generalization despite domain shift. In comparison, a general-purpose approach (TotalSegmentator) showed substantially lower performance, with a Dice score of 0.15, primarily due to under-
segmentation of the gland. These results highlight the importance of task-specific,
multimodal segmentation strategies and demonstrate the potential of the proposed approach for reliable integration into clinical research workflows. To facilitate reproducibility and deployment, the model has been fully containerized and is available as a ready-to-use inference tool.
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All authors Pablo Rodriguez-Belenguer, Gloria Ribas, Javier Aquerreta Escribano, Rafael Moreno-Calatayud, Leonor Cerda-Alberich, Luis Marti-Bonmati
Computer Vision
MAVFusion: Efficient Infrared and Visible Video Fusion via Motion-Aware Sparse Interaction
Xilai Li, Weijun Jiang, Xiaosong Li et al. (+5 more)
Infrared and visible
video fusion combines the object saliency from infrared images with the texture details from visible images to produce semantically rich fusion results. However, most existing methods are designed for static image fusion and cannot effectively handle frame-to-frame motion in
videos. Current
video fusion methods improve temporal consistency by introducing interactions across frames, but they often require high computational cost. To mitigate these challenges, we propose MAVFusion, an end-to-end
video fusion framework featuring a motion-aware sparse interaction mechanism that enhances efficiency while maintaining superior fusion quality. Specifically, we leverage optical flow to identify dynamic regions in multi-modal sequences, adaptively allocating computationally intensive cross-modal
attention to these sparse areas to capture salient transitions and facilitate inter-modal information exchange. For static background regions, a lightweight weak interaction module is employed to maintain structural and appearance integrity. By decoupling the processing of dynamic and static regions, MAVFusion simultaneously preserves temporal consistency and fine-grained details while significantly accelerating inference. Extensive experiments demonstrate that MAVFusion achieves state-of-the-art performance on multiple infrared and visible
video benchmarks, achieving a speed of 14.16 FPS at $640 \times 480$ resolution. The source code will be available at https://github.com/ixilai/MAVFusion.
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All authors Xilai Li, Weijun Jiang, Xiaosong Li, Yang Liu, Hongbin Wang, Tao Ye, Huafeng Li, Haishu Tan
Computer Vision
A Self supervised learning framework for imbalanced medical imaging datasets
Yash Kumar Sharma, Charan Ramtej Kodi and Vineet Padmanabhan
Two problems often plague medical imaging analysis: 1) Non-availability of large quantities of labeled training data, and 2) Dealing with imbalanced data, i.e., abundant data are available for frequent classes, whereas data are highly limited for the rare class. Self supervised learning (SSL) methods have been proposed to deal with the first problem to a certain extent, but the issue of investigating the robustness of SSL to imbalanced data has rarely been addressed in the domain of medical image classification. In this work, we make the following contributions: 1) The MIMV method proposed by us in an earlier work is extended with a new augmentation strategy to construct asymmetric multi-image, multi-view (AMIMV) pairs to address both data scarcity and
dataset imbalance in medical image classification. 2) We carry out a data analysis to evaluate the robustness of AMIMV under varying degrees of class imbalance in medical imaging . 3) We evaluate eight representative SSL methods in 11 medical imaging
datasets (MedMNIST) under long-tailed distributions and limited supervision. Our experimental results on the MedMNIST
dataset show an improvement of 4.25% on retinaMNIST, 1.88% on tissueMNIST, and 3.1% on DermaMNIST.
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Computer Vision
Captioning Daily Activity Images in Early Childhood Education: Benchmark and Algorithm
Sixing Li, Zhibin Gu, Ziqi Zhang et al. (+4 more)
Image captioning for Early Childhood Education (ECE) is essential for automated activity understanding and educational assessment. However, existing methods face two key challenges. First, the lack of large-scale, domain-specific
datasets limits the model's ability to capture fine-grained semantic concepts unique to ECE scenarios, resulting in generic and imprecise descriptions. Second, conventional training paradigms exhibit limitations in enhancing professional object description capability, as supervised learning tends to favor high-frequency expressions, while reinforcement learning may suffer from unstable optimization on difficult samples. To address these limitations, we introduce ECAC, a large-scale
benchmark for ECE daily activity image captioning, comprising 256,121 real-world images annotated with expert-level captions and fine-grained labels. ECAC is further equipped with a domain-oriented evaluation protocol, the Teaching Toy Recognition Score (TTS), to explicitly measure professional object naming accuracy. Furthermore, we propose RSRS (Reward-Conditional Switch of Reinforcement Learning and Supervised Fine-Tuning), a hybrid training framework that dynamically alternates between RL and supervised optimization. By rerouting hard samples with zero rewards to supervised fine-tuning, RSRS effectively mitigates advantage collapse and enables stable optimization for fine-grained recognition. Leveraging ECAC and RSRS, we develop KinderMM-Cap-3B, a domain-adapted
multimodal large
language model. Extensive experiments demonstrate that our model achieves a TTS of 51.06, substantially outperforming state-of-the-art baselines while maintaining superior caption quality, highlighting its potential for specialized educational applications.
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All authors Sixing Li, Zhibin Gu, Ziqi Zhang, Weiguo Pan, Bing Li, Ying Wang, Hongzhe Liu
Computer Vision
Rethinking Representations for Cross-Domain Infrared Small Target Detection: A Generalizable Perspective from the Frequency Domain
Yimin Fu, Songbo Wang, Feiyan Wu et al. (+3 more)
The accurate target-background separation in infrared small target detection (IRSTD) highly depends on the discriminability of extracted representations. However, most existing methods are confined to domain-consistent settings, while overlooking whether such discriminability can generalize to unseen domains. In practice, distribution shifts between training and testing data are inevitable due to variations in observational conditions and environmental factors. Meanwhile, the intrinsic indistinctiveness of infrared small targets aggravates overfitting to domain-specific patterns. Consequently, the detection performance of models trained on source domains can be severely degraded when deployed in unseen domains. To address this challenge, we propose a spatial-spectral collaborative perception network (S$^2$CPNet) for cross-domain IRSTD. Moving beyond conventional spatial learning pipelines, we rethink IRSTD representations from a frequency perspective and reveal inconsistencies in spectral phase as the primary manifestation of domain discrepancies. Based on this insight, we develop a phase rectification module (PRM) to derive generalizable target awareness. Then, we employ an orthogonal
attention mechanism (OAM) in skip connections to preserve positional information while refining informative representations. Moreover, the bias toward domain-specific patterns is further mitigated through selective style recomposition (SSR). Extensive experiments have been conducted on three IRSTD
datasets, and the proposed method consistently achieves state-of-the-art performance under diverse cross-domain settings.
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All authors Yimin Fu, Songbo Wang, Feiyan Wu, Jialin Lyu, Zhunga Liu, Michael K. Ng
Computer Vision
On Learning Spatial Structure from Pre-Beamforming Per-Antenna Range-Doppler Radar Measurements
George Sebastian, Philipp Berthold, Bianca Forkel et al. (+2 more)
Automotive radar perception pipelines commonly construct angle-domain representations via beamforming before applying learning-based models. This work instead investigates a representational question: can meaningful spatial structure be learned directly from pre-beamforming per-
antenna range-Doppler (RD) measurements? Experiments are conducted on a 6-TX x 8-RX (48 virtual
antennas) commodity automotive radar employing an A/B chirp-sequence frequency-modulated continuous-wave (CS-FMCW) transmit scheme, in which the effective transmit aperture varies between chirps (single-TX vs multi-TX), enabling controlled analyses of chirp-dependent transmit configurations. We operate on pre-beamforming per-
antenna RD tensors using a dual-chirp shared-weight encoder trained in an end-to-end, fully data-driven manner, and evaluate spatial recoverability using bird's-eye-view (BEV) occupancy as a
geometric probe rather than a performance-driven objective. Supervision is visibility-aware and cross-modal, derived from
LiDAR with explicit modeling of the radar field-of-view and occlusion-aware
LiDAR observability via ray-based visibility. Through analyses of signal properties, transmit configurations (A-only, B-only, and A+B), receive aperture, and range-Doppler structure, together with physics-aligned baselines, we investigate the factors influencing spatial recoverability. The results indicate that meaningful spatial structure is recoverable from pre-beamforming per-
antenna RD tensors under the studied A/B CS-FMCW radar configuration through learned spatial mixing, without relying on hand-crafted signal-processing stages.
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All authors George Sebastian, Philipp Berthold, Bianca Forkel, Leon Pohl, Mirko Maehlisch
Computer Vision
Enhancing Medical Visual Grounding via Knowledge-guided Spatial Prompts
Yifan Gao, Tao Zhou, Yi Zhou et al. (+3 more)
Medical Visual Grounding (MVG) aims to identify diagnostically relevant phrases from free-text radiology reports and localize their corresponding regions in medical images, providing interpretable visual evidence to support clinical decision-making. Although recent
Vision-
Language Models (VLMs) exhibit promising
multimodal reasoning ability, their grounding remains insufficient spatial precision, largely due to a lack of explicit localization priors when relying solely on latent
embeddings. In this work, we analyze this limitation from an
attention perspective and propose KnowMVG, a Knowledge-prior and global-local
attention enhancement framework for MVG in VLMs that explicitly strengthens spatial awareness during decoding. Specifically, we present a knowledge-enhanced prompting strategy that encodes phrase related medical knowledge into compact
embeddings, together with a global-local
attention that jointly leverages coarse global information and refined local cues to guide precise region localization. localization. This design bridges high-level semantic understanding and fine-grained visual perception without introducing extra textual
reasoning overhead. Extensive experiments on four MVG
benchmarks demonstrate that our KnowMVG consistently outperforms existing approaches, achieving gains of 3.0% in AP50 and 2.6% in mIoU over prior state-of-the-art methods. Qualitative and ablation studies further validate the effectiveness of each component.
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All authors Yifan Gao, Tao Zhou, Yi Zhou, Ke Zou, Yizhe Zhang, Huazhu Fu
Computer Vision
Night Eyes: A Reproducible Framework for Constellation-Based Corneal Reflection Matching
Virmarie Maquiling, Yasmeen Abdrabou and Enkelejda Kasneci
Corneal reflection (glint) detection plays an important role in pupil-corneal reflection (P-CR) eye tracking, but in practice it is often handled as heuristics embedded within larger systems, making reproducibility difficult across hardware setups. We introduce a 2D geometry-driven, constellation-based pipeline for mulit-glint detection and matching, focusing on reproducibility and clear evaluation. Inspired by lost-in-space
star identification, we treat glints as structured constellations rather than independent blobs. We propose a Similarity-Layout
Alignment (SLA) procedure which adapts constellation matching to the specific constraints of multi-LED eye tracking. The framework brings together controlled over-detection, adaptive candidate fallback, appearance-aware scoring, and optional semantic layout priors while keeping detection and correspondence explicitly separated. Evaluated on a public multi-LED
dataset, the system provides stable identity-preserving correspondence under noisy conditions. We release code, presets, and evaluation scripts to enable transparent replication, comparison, and
dataset annotation.
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Computer Vision
Lifting Unlabeled Internet-level Data for 3D Scene Understanding
Yixin Chen, Yaowei Zhang, Huangyue Yu et al. (+9 more)
Annotated 3D scene data is scarce and expensive to acquire, while abundant unlabeled
videos are readily available on the internet. In this paper, we demonstrate that carefully designed data engines can leverage web-curated, unlabeled
videos to automatically generate training data, to facilitate end-to-end models in 3D scene understanding alongside human-annotated
datasets. We identify and analyze bottlenecks in automated data generation, revealing critical factors that determine the efficiency and effectiveness of learning from unlabeled data. To validate our approach across different perception granularities, we evaluate on three tasks spanning low-level perception, i.e., 3D object detection and instance
segmentation, to high-evel
reasoning, i.e., 3D spatial Visual Question Answering (VQA) and
Vision-Lanugage
Navigation (VLN). Models trained on our generated data demonstrate strong zero-shot performance and show further improvement after finetuning. This demonstrates the viability of leveraging readily available web data as a path toward more capable scene understanding systems.
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All authors Yixin Chen, Yaowei Zhang, Huangyue Yu, Junchao He, Yan Wang, Jiangyong Huang, Hongyu Shen, Junfeng Ni, Shaofei Wang, Baoxiong Jia, Song-Chun Zhu, Siyuan Huang
Computer Vision
Light-ResKAN: A Parameter-Sharing Lightweight KAN with Gram Polynomials for Efficient SAR Image Recognition
Pan Yi, Weijie Li, Xiaodong Chen et al. (+3 more)
Synthetic Aperture Radar (SAR) image recognition is vital for disaster monitoring, military reconnaissance, and ocean observation. However, large SAR image sizes hinder deep learning deployment on resource-constrained edge devices, and existing lightweight models struggle to balance high-precision feature extraction with low computational requirements. The emerging Kolmogorov-Arnold Network (KAN) enhances fitting by replacing fixed activations with learnable ones, reducing parameters and computation. Inspired by KAN, we propose Light-ResKAN to achieve a better balance between precision and efficiency. First, Light-ResKAN modifies ResNet by replacing convolutions with KAN convolutions, enabling adaptive feature extraction for SAR images. Second, we use Gram
Polynomials as activations, which are well-suited for SAR data to capture complex non-linear relationships. Third, we employ a parameter-sharing strategy: each kernel shares parameters per channel, preserving unique features while reducing parameters and FLOPs. Our model achieves 99.09%, 93.01%, and 97.26% accuracy on MSTAR, FUSAR-Ship, and SAR-ACD
datasets, respectively. Experiments on MSTAR resized to $1024 \times 1024$ show that compared to VGG16, our model reduces FLOPs by $82.90 \times$ and parameters by $163.78 \times$. This work establishes an efficient solution for edge SAR image recognition.
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All authors Pan Yi, Weijie Li, Xiaodong Chen, Jiehua Zhang, Li Liu, Yongxiang Liu
Computer Vision
FTPFusion: Frequency-Aware Infrared and Visible Video Fusion with Temporal Perturbation
Xilai Li, Chusheng Fang and Xiaosong Li
Infrared and visible
video fusion plays a critical role in intelligent surveillance and low-light monitoring. However, maintaining temporal stability while preserving spatial detail remains a fundamental challenge. Existing methods either focus on frame-wise enhancement with limited temporal modeling or rely on heavy spatio-temporal aggregation that often sacrifices high-frequency details. In this paper, we propose FTPFusion, a frequency-aware infrared and visible
video fusion method based on temporal perturbation and sparse cross-modal interaction. Specifically, FTPFusion decomposes the feature representations into high-frequency and low-frequency components for collaborative modeling. The high-frequency branch performs sparse cross-modal spatio-temporal interaction to capture motion-related context and complementary details. The low-frequency branch introduces a temporal perturbation strategy to enhance robustness against complex
video variations, such as flickering, jitter, and local misalignment. Furthermore, we design an offset-aware temporal consistency constraint to explicitly stabilize cross-frame representations under temporal disturbances. Extensive experiments on multiple public
benchmarks demonstrate that FTPFusion consistently outperforms state-of-the-art methods across multiple metrics in both spatial fidelity and temporal consistency. The source code will be available at https://github.com/ixilai/FTPFusion.
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Computer Vision
SHARC: Reference point driven Spherical Harmonic Representation for Complex Shapes
Panagiotis Sapoutzoglou, George Terzakis and Maria Pateraki
We propose SHARC, a novel framework that synthesizes arbitrary, genus-agnostic shapes by means of a collection of Spherical Harmonic (SH) representations of distance fields. These distance fields are anchored at optimally placed reference points in the interior volume of the surface in a way that maximizes learning of the finer details of the surface. To achieve this, we employ a cost function that jointly maximizes sparsity and centrality in terms of positioning, as well as visibility of the surface from their location. For each selected reference point, we sample the visible distance field to the surface geometry via ray-casting and compute the SH coefficients using the Fast Spherical Harmonic Transform (FSHT). To enhance
geometric fidelity, we apply a configurable low-pass filter to the coefficients and refine the output using a local consistency constraint based on proximity. Evaluation of SHARC against state-of-the-art methods demonstrates that the proposed method outperforms existing approaches in both reconstruction accuracy and time efficiency without sacrificing model parsimony. The source code is available at https://github.com/POSE-Lab/SHARC.
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Computer Vision
ProVG: Progressive Visual Grounding via Language Decoupling for Remote Sensing Imagery
Ke Li, Ting Wang, Di Wang et al. (+4 more)
Remote sensing visual grounding (RSVG) aims to localize objects in remote sensing imagery according to natural
language expressions. Previous methods typically rely on sentence-level
vision-
language alignment, which struggles to exploit fine-grained linguistic cues, such as spatial relations and object attributes, that are crucial for distinguishing objects with similar characteristics. Importantly, these cues play distinct roles across different grounding stages and should be leveraged accordingly to provide more explicit guidance. In this work, we propose ProVG, a novel RSVG framework that improves localization accuracy by decoupling
language expressions into global context, spatial relations, and object attributes. To integrate these linguistic cues, ProVG employs a simple yet effective progressive cross-modal modulator, which dynamically modulates visual
attention through a survey-locate-verify scheme, enabling coarse-to-fine
vision-
language alignment. In addition, ProVG incorporates a cross-scale fusion module to mitigate the large-scale variations in remote sensing imagery, along with a
language-guided calibration decoder to refine cross-modal
alignment during prediction. A unified multi-task head further enables ProVG to support both referring expression comprehension and
segmentation tasks. Extensive experiments on two
benchmarks, i.e., RRSIS-D and RISBench, demonstrate that ProVG consistently outperforms existing methods, achieving new state-of-the-art performance.
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All authors Ke Li, Ting Wang, Di Wang, Yongshan Zhu, Yiming Zhang, Tao Lei, Quan Wang
Computer Vision
Low-Effort Jailbreak Attacks Against Text-to-Image Safety Filters
Ahmed B Mustafa, Zihan Ye, Yang Lu et al. (+2 more)
Text-to-image generative models are widely deployed in creative tools and online platforms. To mitigate misuse, these systems rely on safety filters and moderation pipelines that aim to block harmful or policy violating content. In this work we show that modern text-to-image models remain vulnerable to low-effort jailbreak attacks that require only natural
language prompts. We present a systematic study of
prompt-based strategies that bypass safety filters without model access, optimization, or
adversarial training. We introduce a taxonomy of visual jailbreak techniques including artistic reframing, material substitution, pseudo-educational framing, lifestyle aesthetic camouflage, and ambiguous action substitution. These strategies exploit weaknesses in
prompt moderation and visual safety filtering by masking unsafe intent within benign semantic contexts. We evaluate these attacks across several state-of-the-art text-to-image systems and demonstrate that simple linguistic modifications can reliably evade existing safeguards and produce restricted imagery. Our findings highlight a critical gap between surface-level
prompt filtering and the semantic understanding required to detect
adversarial intent in generative media systems. Across all tested models and attack categories we observe an attack success rate (ASR) of up to 74.47%.
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All authors Ahmed B Mustafa, Zihan Ye, Yang Lu, Michael P Pound, Shreyank N Gowda
Computer Vision
GS^2: Graph-based Spatial Distribution Optimization for Compact 3D Gaussian Splatting
Xianben Yang, Tao Wang, Yuxuan Li et al. (+2 more)
3D Gaussian Splatting (3DGS) has demonstrated breakthrough performance in novel view synthesis and real-time rendering. Nevertheless, its practicality is constrained by the high
memory cost due to a huge number of Gaussian points. Many
pruning-based 3DGS variants have been proposed for
memory saving, but often compromise spatial consistency and may lead to rendering artifacts. To address this issue, we propose graph-based spatial distribution optimization for compact 3D Gaussian Splatting (GS\textasciicircum2), which enhances reconstruction quality by optimizing the spatial distribution of Gaussian points. Specifically, we introduce an evidence lower bound (ELBO)-based adaptive densification strategy that automatically controls the densification process. In addition, an opacity-aware progressive
pruning strategy is proposed to further reduce
memory consumption by dynamically removing low-opacity Gaussian points. Furthermore, we propose a graph-based feature encoding module to adjust the spatial distribution via feature-guided point shifting. Extensive experiments validate that GS\textasciicircum2 achieves a compact Gaussian representation while delivering superior rendering quality. Compared with 3DGS, it achieves higher PSNR with only about 12.5% Gaussian points. Furthermore, it outperforms all compared baselines in both rendering quality and
memory efficiency.
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All authors Xianben Yang, Tao Wang, Yuxuan Li, Yi Jin, Haibin Ling
Computer Vision
A3R: Agentic Affordance Reasoning via Cross-Dimensional Evidence in 3D Gaussian Scenes
Di Li, Jie Feng, Guanbin Li et al. (+4 more)
Affordance
reasoning in 3D Gaussian scenes aims to identify the region that supports the action specified by a given text instruction in complex environments. Existing methods typically cast this problem as one-shot prediction from static scene observations, assuming sufficient evidence is already available for
reasoning. However, in complex 3D scenes, many failure cases arise not from weak prediction capacity, but from incomplete task-relevant evidence under fixed observations. To address this limitation, we reformulate fine-grained affordance
reasoning as a sequential evidence acquisition process, where ambiguity is progressively reduced through complementary 3D
geometric and 2D semantic evidence. Building on this formulation, we propose A3R, an agentic affordance
reasoning framework that enables an MLLM-based policy to iteratively select evidence acquisition actions and update the affordance belief through cross-dimensional evidence acquisition. To optimize such sequential decision making, we further introduce a GRPO-based policy learning strategy that improves evidence acquisition efficiency and
reasoning accuracy. Extensive experiments on scene-level
benchmarks show that A3R consistently surpasses static one-shot baselines, demonstrating the advantage of agentic cross-dimensional evidence acquisition for fine-grained affordance
reasoning in complex 3D Gaussian scenes.
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All authors Di Li, Jie Feng, Guanbin Li, Ronghua Shang, Yuhui Zheng, Weisheng Dong, Guangming Shi
Computer Vision
Yansong Guo, Chaoyang Zhu, Jiayi Ji et al. (+2 more)
Video Large
Language Models (VideoLLMs) have demonstrated impressive capabilities in
video understanding, yet the massive number of input
video tokens incurs a significant computational burden for deployment. Existing methods mainly prune
video tokens at input level while neglecting the inherent information structure embedded in
videos and large
language models (LLMs). To address this, we propose HieraVid, a hierarchical
pruning framework that progressively and dynamically reduces visual redundancy. Based on two observations that
videos possess the segment-frame structure and LLMs internally propagate multi-modal information unidirectionally, we decompose
pruning into three levels: 1) segment-level, where
video tokens are first temporally segmented and spatially merged; 2) frame-level, where similar frames within the same segment are jointly pruned to preserve diversity; 3) layer-level, redundancy gradually shrinks as LLM layer increases w/o compromising performance. We conduct extensive experiments on four widely used
video understanding
benchmarks to comprehensively evaluate the effectiveness of HieraVid. Remarkably, with only 30% of
tokens retained, HieraVid achieves new state-of-the-art performance, while maintaining over 98% and 99% of the performance of LLaVA-
Video-7B and LLaVA-OneVision-7B, respectively.
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All authors Yansong Guo, Chaoyang Zhu, Jiayi Ji, Jianghang Lin, Liujuan Cao
Computer Vision
GeoAI Agency Primitives
Akram Zaytar, Rohan Sawahn, Caleb Robinson et al. (+5 more)
We present ongoing research on agency primitives for GeoAI assistants – core capabilities that connect Foundation models to the artifact-centric, human-in-the-loop workflows where GIS practitioners actually work. Despite advances in satellite image captioning, visual question answering, and promptable
segmentation, these capabilities have not translated into productivity gains for practitioners who spend most of their time producing vector layers, raster maps, and cartographic products. The gap is not model capability alone but the absence of an agency layer that supports iterative collaboration. We propose a vocabulary of $9$ primitives for such a layer – including
navigation, perception, geo-referenced
memory, and dual modeling – along with a
benchmark that measures human productivity. Our goal is a vocabulary that makes agentic assistance in GIS implementable, testable, and comparable.
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All authors Akram Zaytar, Rohan Sawahn, Caleb Robinson, Gilles Q. Hacheme, Girmaw A. Tadesse, Inbal Becker-Reshef, Rahul Dodhia, Juan Lavista Ferres
Computer Vision
MAR-MAER: Metric-Aware and Ambiguity-Adaptive Autoregressive Image Generation
Kai Dong and Tingting Bai
Autoregressive (AR) models have demonstrated significant success in the realm of text-to-image generation. However, they usually face two major challenges. Firstly, the generated images may not always meet the quality standards expected by humans. Furthermore, these models face difficulty when dealing with ambiguous
prompts that could be interpreted in several valid ways. To address these issues, we introduce MAR-MAER, an innovative hierarchical autoregressive framework. It combines two main components. It is a metric-aware
embedding regularization method. The other one is a probabilistic latent model used for handling ambiguous semantics. Our method utilizes a lightweight projection head, which is trained with an adaptive kernel regression loss function. This aligns the model's internal representations with human-preferred quality metrics, such as CLIPScore and HPSv2. As a result, the
embedding space that is learned more accurately reflects human judgment. We are also introducing a conditional variational module. This approach incorporates an aspect of controlled randomness within the hierarchical
token generation process. This capability allows the model to produce a diverse array of coherent images based on ambiguous or open-ended
prompts. We conducted extensive experiments using COCO and a newly developed Ambiguous-
Prompt Benchmark. The results show that MAR-MAER achieves excellent performance in both metric consistency and semantic flexibility. It exceeds the baseline Hi-MAR model's performance, showing an improvement of +1.6 in CLIPScore and +5.3 in HPSv2. For unclear inputs, it produces a notably wider range of outputs. These findings have been confirmed through both human evaluation and automated metrics.
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Computer Vision
Combining Boundary Supervision and Segment-Level Regularization for Fine-Grained Action Segmentation
Hinako Mitsuoka and Kazuhiro Hotta
Recent progress in Temporal Action
Segmentation (TAS) has increasingly relied on complex architectures, which can hinder practical deployment. We present a lightweight dual-loss training framework that improves fine-grained
segmentation quality with only one additional output channel and two auxiliary loss terms, requiring minimal architectural modification. Our approach combines a boundary-regression loss that promotes accurate temporal localization via a single-channel boundary prediction and a CDF-based segment-level regularization loss that encourages coherent within-segment structure by matching cumulative distributions over predicted and ground-truth segments. The framework is architecture-agnostic and can be integrated into existing TAS models (e.g., MS-TCN, C2F-TCN, FACT) as a training-time loss function. Across three
benchmark datasets, the proposed method improves segment-level consistency and boundary quality, yielding higher F1 and Edit scores across three different models. Frame-wise accuracy remains largely unchanged, highlighting that precise
segmentation can be achieved through simple loss design rather than heavier architectures or inference-time refinements.
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Computer Vision · Cross-listed from physics.optics
Enhanced Polarization Locking in VCSELs
Zifeng Yuan, Dewen Zhang, Lei Shi et al. (+2 more)
While optical injection locking (OIL) of vertical-cavity surface-emitting
lasers (VCSELs) has been widely studied in the past, the polarization dynamics of OIL have received far less
attention. Recent studies suggest that polarization locking via OIL could enable novel computational applications such as polarization-encoded Ising computers. However, the inherent polarization preference and limited polarization switchability of VCSELs hinder their use for such purposes. To address these challenges, we fabricate VCSELs with tailored
oxide aperture designs and combine these with bias current tuning to study the overall impact on polarization locking. Experimental results demonstrate that this approach reduces the required injection power (to as low as 3.6 {\mu}W) and expands the locking range. To investigate the impact of the approach, the
spin-flip model (SFM) is used to analyze the effects of amplitude anisotropy and bias current on polarization locking, demonstrating strong coherence with experimental results.
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All authors Zifeng Yuan, Dewen Zhang, Lei Shi, Yutong Liu, Aaron Danner
Computer Vision
Semantic Richness or Geometric Reasoning? The Fragility of VLM's Visual Invariance
Jason Qiu, Zachary Meurer, Xavier Thomas et al. (+1 more)
This work investigates the fundamental fragility of state-of-the-art
Vision-
Language Models (VLMs) under basic
geometric transformations. While modern VLMs excel at semantic tasks such as recognizing objects in canonical orientations and describing complex scenes, they exhibit systematic failures at a more fundamental level: lack of robust spatial invariance and equivariance required to reliably determine object identity under simple rotations, scaling, and identity transformations. We demonstrate this limitation through a systematic evaluation across diverse visual domains, including symbolic sketches, natural photographs, and abstract art. Performance drops sharply as semantic content becomes sparse, and this behavior is observed across architectures, model capacities, and prompting strategies. Overall, our results reveal a systematic gap between semantic understanding and spatial
reasoning in current VLMs, highlighting the need for stronger
geometric grounding in future
multimodal systems.
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All authors Jason Qiu, Zachary Meurer, Xavier Thomas, Deepti Ghadiyaram
Computer Vision
FaCT-GS: Fast and Scalable CT Reconstruction with Gaussian Splatting
Pawel Tomasz Pieta, Rasmus Juul Pedersen, Sina Borgi et al. (+3 more)
Gaussian Splatting (GS) has emerged as a dominating technique for image rendering and has quickly been adapted for the X-ray Computed
Tomography (CT) reconstruction task. However, despite its growing popularity, the benefits of GS are typically not substantial enough to motivate a transition from well-established reconstruction
algorithms. This paper addresses the most significant remaining limitations of the GS-based approach by introducing FaCT-GS, a framework for fast and flexible CT reconstruction. Enabled by an in-depth optimization of the voxelization and rasterization pipelines, our new method is significantly faster than its predecessors and scales well with projection and output volume size. Furthermore, the improved voxelization enables rapid fitting of Gaussians to pre-existing volumes, which can serve as a prior for warm-starting the reconstruction, or simply as an alternative, compressed representation. FaCT-GS is over 4X faster than the State of the Art GS CT reconstruction on standard 512x512 projections, and over 13X faster on 2k projections. Implementation and data available through: https://github.com/PaPieta/fact-gs.
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All authors Pawel Tomasz Pieta, Rasmus Juul Pedersen, Sina Borgi, Jakob Sauer Jørgensen, Jens Wenzel Andreasen, Vedrana Andersen Dahl