Computer Vision
Multi-Modal Controlled Coherent Motion Generation
Yifei Liu, Qiong Cao, Hongwei Yi et al. (+2 more)
It is natural for humans to walk and talk simultaneously. This paper tackles the challenge of replicating such natural behaviors in 3D avatar motion generation driven by concurrent
multimodal inputs, such as a text description of a man walking alongside
speech audio. Existing methods, constrained by the scarcity of aligned
multimodal data, typically combine motions from individual modalities sequentially or through weighted sums. However, they often result in mismatched or unrealistic movements. To overcome these limitations, we propose MOCO, a novel
diffusion-based framework capable of processing multiple simultaneous inputs, including
speech audio, text descriptions, and trajectory data, to generate coherent and lifelike motions without requiring aligned
multimodal data. Our key innovation lies in decoupling the motion generation process. During each denoising step, the
diffusion model independently generates motions for each modality from the input noise and assembles the body parts according to predefined spatial rules. The resulting combined motion is then diffused and serves as the input noise for the subsequent denoising step. This iterative approach enables each modality to refine its contribution within the context of the overall motion, progressively harmonizing movements across modalities. Consequently, the generated motions become increasingly natural and fluid with each iteration, achieving coherent and synchronized behaviors. We evaluate our approach using a purpose-built
multimodal benchmark. Experimental results demonstrate that MOCO outperforms existing baselines, advancing the field of
multimodal motion generation for 3D avatars.
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All authors Yifei Liu, Qiong Cao, Hongwei Yi, Huaiguang Jiang, Changxing Ding
Computer Vision · Cross-listed from cs.GR
Hologram Representation via Quadratic Phase Gaussian Splatting
Haolong Wang, Yicheng Zhan, Kaan Akşit et al. (+1 more)
We introduce Complex-Valued Quadratic Phase Gaussian (CVQPG), a novel hologram representation method that augments each 2D Gaussian primitive with a quadratic phase profile controlled by a learnable
curvature parameter. Against the planar Gaussian baseline, CVQPG improves the average PSNR of
holographic reconstructions by 0.19 dB (RGB) and 0.33 dB (grayscale) at equal primitive counts, and by 0.05 dB (RGB) and 0.08 dB (grayscale) at equal parameter counts, where it still leads in all visual quality metrics. Our frequency-domain analysis shows that CVQPG better preserves the mid-to-high frequency band of natural images, where the reconstruction MSE drops by up to 11% (RGB) and 22% (grayscale), indicating that modulating primitive wavefronts is an effective and lightweight enhancement.
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All authors Haolong Wang, Yicheng Zhan, Kaan Akşit, Simeng Qiu
Computer Vision
DINO-Med: A Unified Patch-Based Adaptation Framework for Multi-Modal Medical Image Analysis Applied to Liver Fibrosis Staging
Boya Wang, Ruizhe Li, Chao Chen et al. (+1 more)
Adapting natural-image foundation models like DINOv3 to multi-modal medical imaging is challenging due to the significant domain gap between natural color images and multi-channel medical scans. We present a unified, patch-based framework that processes raw
multimodal imaging through training-free registration, automated localization, and mask-filtered patch extraction. This architecture culminates in a hierarchical strategy that aggregates patch-level insights into subject-level diagnostics. Using liver fibrosis staging as a case study, we evaluate four patch-level feature representations: handcrafted Radiomics features, learned ResNet features, pre-trained foundation model SAM-Med2D features, and frozen DINOv3 features. To ensure a controlled comparison, all models utilize the same lightweight MLP head and are evaluated across both rigid and deformable registration settings. Our training protocol focuses on mild fibrosis (S1) and cirrhosis (S4) classes only, enabling a single classifier to address both substantial fibrosis detection and cirrhosis staging. Evaluated via 10 random train (90%)/ test (10%) splits on 360 subjects from the CARE 2025 Liver Track 4 cohort, our DINOv3-based framework significantly outperforms all baselines, achieving the best classification accuracy of 78.4% for S1 and 75.8% for S4.
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All authors Boya Wang, Ruizhe Li, Chao Chen, Xin Chen
Computer Vision
Brain-PACE: A Deep Siamese MRI Framework for Modelling Longitudinal Brain Acceleration
Samuel Maddox, Jacob Newman, Saber Sami et al. (+4 more)
Brain age estimation has become a popular research proxy for assessing
brain health and disease, yet longitudinal trajectories of
brain ageing are still poorly defined, and clinical use is limited. Building on existing Siamese longitudinal frameworks, we develop
Brain-Predicted Age Acceleration (
Brain-PACE) to directly estimate the pace of structural
brain ageing from paired T1-weighted MRI.
Brain-PACE identified accelerated ageing in $42.6$% of participants with mild cognitive impairment. Faster
Brain-PACE was associated with greater functional and cognitive impairment (FAQ; $r=0.35$, ADAS13; $r=0.30$, CDR-SB; $r=0.32$) and greater regional tau burden in the posterior cingulate ($r=0.59$), precuneus ($r=0.47$), and entorhinal cortex ($r=0.37$). These associations were stronger than those observed when pace was calculated indirectly from repeated cross-sectional
brain age estimates, suggesting that direct longitudinal modelling captures complementary information relevant to ongoing pathological change. Methodologically,
Brain-PACE extends the LILAC framework by combining spatial
attention with soft label distribution learning and a Cramér distance objective, improving probabilistic performance and reducing prediction bias while providing measures of predictive uncertainty. Together, these findings support
Brain-PACE as a complementary longitudinal imaging phenotype with sensitivity to relevant clinical and biological changes in early neurodegeneration.
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All authors Samuel Maddox, Jacob Newman, Saber Sami, Michal Mackiewicz, for the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers, Lifestyle flagship study of ageing
Computer Vision
Vision Transformer-Based Multi-Level Feature Fusion for Multi-Label Sewer Defect Classification
Xu Fang, Zhuoran Wang, Qing Li et al. (+4 more)
Automated classification of sewer defects is essential for infrastructure condition assessment and maintenance decision-making, but existing deep learning methods struggle to balance classification accuracy and computational complexity in large-scale multi-label scenarios. This study develops Sewer-
Transformer-ML, a hierarchical
vision Transformer with multi-level feature fusion, together with two lightweight architectures, Sewer-MobileNet-ML and Sewer-Mobile-TransNet, for resource-constrained inspection scenarios. On the Sewer-ML test set, Sewer-
Transformer-ML-Base achieved an $F2_{\text{CIW}}$ of 65.68% and an $F1_{\text{Normal}}$ of 92.68%, ranking first on the public leaderboard and exceeding the second-ranked method by 7.6 percentage points in $F2_{\text{CIW}}$. Sewer-MobileNet-ML achieved an $F2_{\text{CIW}}$ of 65.73% with only 17 M parameters, representing an approximately 95% parameter reduction relative to the base model. Under the standard Sewer-Capsule data split, Sewer-Mobile-TransNet achieved 96.43% classification accuracy. When the training set was reduced to 1,177 images, pretraining on Sewer-ML consistently improved model performance. Ablation experiments further showed that direct concatenation was more effective for
Transformer features, whereas
attention-based fusion better supported multiscale CNN features. These findings provide a computational basis for automated sewer inspection, lightweight model design, and adaptation across civil infrastructure inspection platforms.
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All authors Xu Fang, Zhuoran Wang, Qing Li, Shengyu Zhang, Guanzhi Deng, Jianbiao He, Qingquan Li
Computer Vision
R4Tun: LLM-guided adaptive segmental tunnel lining segmentation in point clouds
Xinghui Tao, Zehao Ye, Guangming Wang et al. (+2 more)
Automated inspection of segmental tunnel linings requires adaptive
segmentation from 3D point clouds, yet expert-tuned pipelines often degrade when tunnel conditions vary. This paper presents R4Tun, a large
language model (LLM)-driven adaptation framework that extends an expert-designed pipeline (SAM4Tun) with bounded parameter tuning informed by structured context:
memory ($m$), state ($s$), and knowledge ($k$). Evaluated on 30 selected Seg2Tunnel subsets (13 regular, 17 complex) across three LLMs, the full $m+s+k$ design raised mean Intersection-over-Union (mIoU) from 0.18 to 0.43–0.48 and overall accuracy (OA) from 0.42 to 0.59–0.65 relative to the static SAM4Tun baseline, with the near-reference regular (staggered) subsets reaching mIoU 0.784–0.796 across LLMs. Across 270 (30 tunnels $\times$ 3 different LLMs $\times$ 3 context settings) runs, the LLMs showed similar parameter-adjustment trends (with overlapping 95% CIs on mean gains) and consistently adjusted a shared set of critical parameters. These results support R4Tun as a controlled, label-free, cross-LLM adaptation mechanism in the tested SAM4Tun–Seg2Tunnel setting, demonstrating consistent accuracy gains; we position R4Tun as a mechanism contribution rather than a deployable final-inspection system, in which each bounded parameter change is auditable via logged rationales.
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All authors Xinghui Tao, Zehao Ye, Guangming Wang, Jelena Ninić, Brian Sheil
Computer Vision
Predictive Multi-Landmark OCT Tracking for Increased Motion Robustness
Konrad Reuter, Suresh Guttikonda, Chaitali Uday Karekar et al. (+2 more)
Optical coherence
tomography is a promising modality for markerless motion tracking due to its high spatial resolution and inherent depth perception. However, existing OCT-based tracking approaches are limited in terms of trackable velocity, particularly when multiple landmarks are tracked sequentially for 6D pose estimation. In this work, we present a predictive tracking approach that propagates positional updates between multiple tracked landmarks to obtain a global pose prediction. This enables more robust tracking under high velocities. Our results demonstrate RMSEs below 1 mm for velocities up to 100 mm/s and up to nine consecutively tracked landmarks, highlighting the potential of global motion propagation and prediction for improving the robustness of OCT-based tracking.
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All authors Konrad Reuter, Suresh Guttikonda, Chaitali Uday Karekar, Christian Betz, Alexander Schlaefer
Computer Vision · Cross-listed from cs.CL
Mary Ogbuka Kenneth, Foaad Khosmood and Abbas Edalat
Computational recognition of verbal humour remains a challenging task, requiring an understanding of
language, delivery style,
emotions, and cultural context. Most existing approaches focus on binary classification and lack
datasets that capture psychological dimensions of humour alongside variations in expression. We introduce MultiHuSE, a
multimodal dataset comprising 2,407 high-definition
videos of 50 demographically diverse actors performing 1,463 text samples across four psychological humour styles (affiliative, aggressive, self-enhancing, and self-deprecating), as well as neutral content. A subset is additionally annotated for underlying
emotions. The
dataset uniquely captures multiple actor interpretations of the same texts, enabling systematic analysis of expressive diversity. Baseline experiments show that
multimodal fusion outperforms unimodal approaches (80.1% vs. 77.4% accuracy) in humour style classification, with particularly strong gains for affiliative humour (66% to 74%). While text provides the strongest individual signal, fusion models deliver meaningful improvements. We hope that MultiHuSE provides empirical support for psychological theories linking humour and
emotion, while also opening new avenues for research in human communication, well-being, and AI-driven interaction. The
dataset is available for academic use under an End-User Licence Agreement.
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Computer Vision
Mi-Ripple: Restoring Images Degraded by Iterative AI Editing
Jiayin Chen, Yicheng Xu and Muting Wang
Iterative reference-conditioned image editing can introduce grid-like and granular textures, commonly described as digital ripple. We present Mi-Ripple, a diagnosis-guided restoration workflow that suppresses this digital ripple while protecting image structure. Mi-Ripple separates periodic
lattice artifacts from content-entangled granular texture, then combines selective spectral notching, structure-aware smoothing, and cleaned-reference regeneration. This separation enables low-distortion filtering when artifacts are spectrally isolated and visual reconstruction when filtering would erase legitimate detail. Across fourteen notch-only executions, whole-image residual standard deviation is 0.08–0.44 in CIELAB lightness units. In a paired regeneration example, reference cleaning reduces output debris density by 45%. Mi-Ripple links measurable artifact reduction to visibly cleaner generated images, rather than optimizing a spectral score alone.
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Computer Vision
GRIPNet: Gaussian Radial Intensity Prior Guided Architecture for Pulmonary Nodule Detection in CT
Haojie Yang and Ran Su
Lung cancer causes more deaths than any other malignancy, and low-dose CT screening is the main pathway to early diagnosis. That pathway hinges on the smallest lesions, yet nodules below six millimeters remain hard to detect, because most methods treat a nodule as a generic object and ignore the imaging physics behind its appearance. We show that this appearance is highly regular. Intensity peaks at the
geometric center of a nodule and decays radially in a Gaussian pattern, and a fit to 18,218 annotated lesions from three public
benchmarks yields a mean radial coefficient of determination above 0.86 in every
dataset and size stratum. A square convolution samples both axes uniformly and is mismatched to this radial signal, most severely for small nodules. Guided by this evidence, we propose GRIPNet (Gaussian Radial Intensity Prior Network), a detector in which every module maps to a measurable property of the intensity distribution. Pinwheel convolutions decompose radial gradients, a dual-frequency module separates boundary detail from structural context, dilated masked
attention matches the decay extent, and an adaptive loss reweights samples by conspicuity. GRIPNet raises mAP@0.5 to 95.3, 91.6 and 97.9 percent on KanserSet, LUNA16 and Lung-PET-CT-Dx while sharpening high-IoU localization at real-time speed.
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Computer Vision
Your Model Already Knows Don't Teach It, Learn to Ask It: Soft Prompting for Few-Shot Adaptation of Vision-Language Models
Gautam Rajendrakumar Gare, Siyi Li, Hewei Wang et al. (+5 more)
We address few-shot object detection with
vision-
language models (VLMs) in out-of-domain settings such as aerial, industrial, and medical imagery, using only ten annotated images for supervision. Existing adaptation methods are discrete
prompt optimization and LoRA fine-tuning. We revisit a third option: soft prompting, where a small number of continuous
prompt tokens are optimized while the pretrained backbone remains frozen. We identify two key design choices. First, placing
prompt tokens at the cross-modal boundary between visual and text
tokens outperforms other placements (10.0 vs. 8.4 mAP). Second, initializing
prompts from the empty space
token outperforms semantic and random initialization. With these choices, one to three learned
tokens (7,168 parameters on average) match the best LoRA configuration on Roboflow20-VL (14.2 mAP, 10-shot) while training over 20,000x fewer parameters. Soft prompting remains harder to optimize, exhibiting higher variance across random seeds. Unlike LoRA, however, it causes no forgetting: the LoRA rank matching our accuracy reduces NaturalBench VQA accuracy by 35% relative, rising to 56% at the largest rank, whereas soft prompting leaves pretrained performance unchanged. The learned
tokens behave like
prompts rather than weights. They transfer to a newer model without retraining (+0.8 mAP on Qwen3.5-9B) and can be verbalized into readable
prompts competitive with
prompt-search methods (matching DetPO and outperforming GEPA). The approach also extends beyond detection. On RoboCasa manipulation tasks, the frozen $\pi_{0.5}$
vision-
language-action policy benefits from soft prompting, matching the LoRA baseline on two of three tasks when
tokens are placed at the gradient bottleneck. These results suggest modern VLMs already encode much of what is needed for specialized domains; the challenge is learning how to ask.
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All authors Gautam Rajendrakumar Gare, Siyi Li, Hewei Wang, Cesar Daniel Hernandez, Wei Zhao, Wolfgang M. Pauli, John Galeotti, Deva Ramanan
Computer Vision
SAMV-DUSt3R: Instance-Centric 3D Scene Decoupling from Sparse Multi-Views
Langxu Zhao, Zuan Gu, Yingdan Zhang et al. (+2 more)
With the rising demand to decouple objects from 3D scenes, we propose SAMV-
DUSt3R, an end-to-end model that injects SAM2 2D masks into MV-
DUSt3R reconstruction. A Cross Flow Mask Block uses these masks to steer the network toward the target instance, jointly improving shape accuracy and achieving object-level disentanglement without multi-stage pipelines. To ensure reconstruction stability, a lightweight Spatial RankGNN selects the optimal reference view with a selection accuracy of 73.5%. Extensive experiments demonstrate that our method boosts average reconstruction precision by 11% across various metrics compared to state-of-the-art baselines. These results reveal a strong instance-disentanglement capability and clear benefits for driving, robotics, AR/VR, and heritage digitisation.
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All authors Langxu Zhao, Zuan Gu, Yingdan Zhang, Pengfei Zhao, Tianhan Gao
Computer Vision
Order-Aware 2.5D Multiple Instance Learning for Preoperative MRI-Based Perineural Invasion Risk Assessment in Intrahepatic Cholangiocarcinoma
Hyunsu Go, Youngung Han, Kyeonghun Kim et al. (+11 more)
Perineural invasion (PNI) is an adverse histopathologic marker in intrahepatic cholangiocarcinoma (ICC), but it is usually confirmed only after resection. Preoperative T2-weighted MRI may provide noninvasive imaging cues predictive of PNI, although labels are available only at the patient level without slice- or voxel-level annotations. We propose Order-Aware Slab Multiple Instance Learning (OAS-MIL), a weakly supervised framework for patient-level PNI prediction. Each tumor-centered MRI crop is represented as an ordered sequence of overlapping 2.5D slabs formed from contiguous axial slices. A shared encoder extracts slab-level features, which are aggregated by a permutation-invariant set-
attention branch and a bidirectional sequence-
attention branch. Using five-fold label-stratified cross-validation at the patient level, OAS-MIL achieved a mean AUROC of 0.770, outperforming the evaluated volumetric and MIL baselines. These results suggest that axial order provides a useful inductive bias for weakly supervised PNI prediction from MRI.
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All authors Hyunsu Go, Youngung Han, Kyeonghun Kim, Jinyong Jun, Junbeom Lee, Dohyun Kweon, Yului Jeong, Suah Park, Sungha Park, Anna Jung, Woo Kyoung Jeong, Ken Ying-Kai Liao, Hyuk-Jae Lee, Nam-Joon Kim
Computer Vision
Improving Faint Object Detection for Space Situational Awareness with Variational Autoencoders
Angela Cratere, Luca Ghilardi, Vishnu Reddy et al. (+3 more)
We present a deep-learning pipeline for enhancing the detection of faint moving objects in optical space situational awareness (SSA) imagery through automated
star removal and background reconstruction. Detecting low signal-to-noise ratio (SNR) objects remains extremely challenging in optical observations, particularly in the cislunar (X-GEO) environment, where structured sky backgrounds, dense
stellar fields, and scattered moonlight significantly degrade the performance of classical detection
algorithms. To address this problem, the proposed pipeline combines a lightweight
segmentation network (Tiny-U-Net) to generate
stellar masks with a partial-convolution variational autoencoder (astro-VAE), designed to learn the statistical distribution of astronomical backgrounds and perform context-aware inpainting of masked regions. The reconstructed background maps can then be used as a preprocessing step to suppress fixed sources and background inhomogeneities prior to detection. As a proof of concept, the approach is integrated with a shift-and-stack scheme and evaluated on real ground-based
telescope observations targeting the X-GEO region. Results demonstrate that the method reconstructs
star-free backgrounds with high fidelity, while preserving moving targets and significantly enhancing detectability, thereby providing an effective data-driven preprocessing strategy for faint moving-object detection in optical SSA scenarios.
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All authors Angela Cratere, Luca Ghilardi, Vishnu Reddy, Francesco Dell'Olio, Charalampos S. Kouzinopoulos, Roberto Furfaro
Computer Vision
Uncertainty DMD: Restoring Diversity in Few-Step Autoregressive Video Distillation
Zixuan Duan, Xunzhi Xiang, Yabo Chen et al. (+6 more)
Few-step
distillation improves the efficiency of autoregressive (AR)
video generation, but often causes diversity collapse: under the same
prompt, different noise samples tend to produce highly similar
videos with weakened motion dynamics. We analyze this degradation in Distribution Matching
Distillation (DMD)-distilled AR
video generators and find that, in the autoregressive setting, it takes the form of a structured uncertainty collapse: the mode-seeking bias of DMD maps different noise samples to nearly identical first chunks, and the deterministic AR cache then propagates this collapsed state to all subsequent chunks, turning a local loss of stochasticity at the rollout root into a global suppression of temporal variation. Based on this analysis, we propose Uncertainty DMD, a simple uncertainty-injection framework that restores stochasticity at two key stages of AR generation: a timestep perturbation for the first chunk to increase first-chunk diversity, and a
stochastic cache-writing mechanism for later chunks to preserve uncertainty in autoregressive conditioning. The method requires no architectural changes and introduces only lightweight perturbation operations. The same perturbation mechanisms are used during both training and inference. Experiments show that Uncertainty DMD consistently improves diversity and motion dynamics while maintaining comparable per-sample visual quality.
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All authors Zixuan Duan, Xunzhi Xiang, Yabo Chen, Xin Zhang, Changhan Liu, Haibin Huang, Chi Zhang, Qi Fan, Xuelong Li
Computer Vision · Cross-listed from cs.AI
AI-Powered Flare Combustion Efficiency Estimation
Afeefa Azam, Iyyakutti Iyappan Ganapathi, Fares Ossama Abdelhafez et al. (+5 more)
Achieving high combustion efficiency in
flare stacks is crucial for adhering to regulatory standards and controlling the release of hydrocarbons into the environment. Traditional instruments like gas analyzers and hyperspectral cameras are expensive, fragile, and require frequent calibration, which makes them impractical for remote or budget constrained industrial sites. We propose an innovative solution that combines a lightweight
vision-
language encoder with a compact multi-layer perceptron to predict combustion efficiency directly from low-cost thermal
video footage. The fully trained model is integrated into an easy-to-deploy graphical user interface. This interface overlays predicted combustion efficiency values on each
video frame, displays real-time trends in combustion efficiency, shows the distribution of combustion efficiency across all frames in the
video, and allows users to export CSV reports. Over a six-month period, the system achieved 99% uptime and required less than 15 minutes of maintenance per week.
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All authors Afeefa Azam, Iyyakutti Iyappan Ganapathi, Fares Ossama Abdelhafez, Divya Velayudhan, Maregu Assefa Habtie, Hamad Karki, Khalid Yousef Al Awadhi, Naoufel Werghi
Computer Vision · Cross-listed from cs.CL
OmniHallu: Unified Hallucination Detection for Cross-Modal Comprehension and Generation in Multimodal Large Language Models
Jianjiang Yang, Peihang Li, Shanqing Xu et al. (+3 more)
While
Multimodal Large
Language Models (MLLMs) have achieved remarkable progress across diverse tasks, they suffer from
hallucinations where generated outputs contradict or misrepresent input semantics. Existing research typically addresses
hallucination detection within a single modality or task type, limiting generalizability. We introduce OmniHallu, a unified
hallucination detection framework spanning both comprehension and generation tasks across image,
video, and audio modalities. We contribute OmniHallu-Bench, a 10,000-sample
benchmark with claim-level human annotations covering six cross-modal tasks: image-to-text (I2T),
video-to-text (V2T), audio-to-text (A2T), text-to-image (T2I), text-to-
video (T2V), and text-to-audio (T2A). Our multi-
agent architecture decomposes model outputs into atomic claims, verifies them through modality-specific experts, and aggregates evidence via structured
reasoning. We further propose a preference-optimized trainable verifier that approximates the multi-
agent decision boundary, reducing expert calls by 66% with minimal performance loss. Extensive experiments reveal a consistent modality-dependent performance gradient and provide fine-grained insights into cross-modal
hallucination patterns.
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All authors Jianjiang Yang, Peihang Li, Shanqing Xu, Mengchen Qian, Lu Zhang, Meng Luo
Computer Vision
From Evaluation to Enhancement: Benchmarking and Improving Think-with-Video Reasoning for Video Generative Models
Meng Luo, Yicheng Liu, Jiahao Wang et al. (+5 more)
Video generation has advanced to produce visually compelling and temporally coherent results. Yet, whether these models can genuinely think with
video–executing symbolic rules, respecting physical laws, and pursuing intentional goals–remains an open question. Existing
benchmarks only partially address this, often conflating visual quality with cognitive correctness. We introduce VWG-Bench (
Video World Generalist
Benchmark), a comprehensive
benchmark spanning 9
reasoning dimensions and 38 fine-grained tasks. To enable precise diagnosis, we design a three-level VLM-as-Judge protocol that independently assesses
video-level fluency, task-level rule adherence, and sample-level goal realization. Evaluations of leading models reveal a striking gap: while models achieve strong rendering scores, they consistently fail on logic-heavy and rule-constrained tasks. To address this, we propose Vid-PRE (
Video Prompt Reasoner and Enhancer), a model-agnostic
prompt rewriter that offloads the cognitive burden of
reasoning to a dedicated VLM. Trained via reinforcement learning with purely text-based rewards, Vid-PRE produces concise, constraint-aware
prompts without the instability of
video-level reward signals. Experiments show that Vid-PRE yields substantial
reasoning improvements across multiple generators without architectural modifications. Together, VWG-Bench and Vid-PRE offer a rigorous diagnostic lens and a scalable path toward true think-with-
video capabilities. All data and code are publicly available at https://huggingface.co/
datasets/KlingTeam/VWG-Bench.
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All authors Meng Luo, Yicheng Liu, Jiahao Wang, Yuanxing Zhang, Xin Tao, Pengfei Wan, Kun Gai, Hao Fei
Computer Vision
Fast and Accurate Monomodal 3D High Resolution Deep Registration of Drosophila Larval Brain Volumes
Daniel Reisenbüchler, Yousef Sadegheih, Michael Dittrich et al. (+3 more)
The larval stage of Drosophila melanogaster is a compact model system for neuroscience whose genetic toolkit allows fluorescent markers to be expressed in defined neural populations, and comparing the resulting expression patterns across animals requires every
brain to be registered into a shared anatomical reference space. Existing pipelines for this task are predominantly based on classical registration methods, which perform a new optimization for each volume, often require per-case parameter tuning, and can take minutes per
brain, limiting their use as a routine preprocessing step. We present a trained deep registration pipeline that deformably aligns a larval
brain to a reference template in a single forward pass at high spatial resolution, on volumes that hold several times more voxels than those learned 3D registration is normally reported on, together with the preprocessing and anatomy-anchored evaluation pipeline required to apply it. Against eleven classical and seven further learned baselines on a held-out collection acquired with different acquisition and quality strata, the proposed pipeline is the most accurate, improving on the strongest classical baseline by 23 percentage points of anatomical landmark-local mutual information. It registers a volume one to two orders of magnitude faster than the classical deformable pipelines, and it retains more of its accuracy than any other method as acquisition quality degrades. The network, its trained weights and the full pipeline are released as the open-source deep larval
brain registration framework: https://github.com/agentdr1/deep-larval-
brain-reg
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All authors Daniel Reisenbüchler, Yousef Sadegheih, Michael Dittrich, Pratibha Kumari, Muhammad Usman, Dorit Merhof
Computer Vision
SCINTILLA-SNN: A Spiking Multi-Scale Selective Aggregation Network for Perineural Invasion Prediction
Youngung Han, Yului Jeong, Kyeonghun Kim et al. (+11 more)
Preoperative prediction of perineural invasion (PNI) in cholangiocarcinoma (CCA) is clinically valuable but remains challenging because PNI-related cues on
magnetic resonance imaging (MRI) are subtle, sparse, and spatially localized around the tumor boundary. Standard 3D CNN and
transformer architectures process volumetric data in a dense or spatially uniform manner, which can dilute subtle PNI-related evidence while requiring a large number of multiply-accumulate operations over 3D feature grids. To address these limitations, we propose SCINTILLA-SNN, a 3D spiking network composed of a four-stage hierarchical backbone and a Multi-Scale Spike Aggregation (MSSA) module for PNI prediction. The backbone extracts hierarchical volumetric representations through spiking convolutional stages and local spike window modulation stages. Given the resulting stage-wise representations, MSSA maps each spatial
token to a learnable content value and modulates it with a spike-dynamics gate derived from firing rate and timestep-wise membrane-potential variability. The resulting score, referred to as the diagnostic
token score, is used to selectively aggregate sparse PNI-related evidence. Experiments on a 10-year retrospective cohort of 182 CCA patients show that SCINTILLA-SNN achieves an AUROC of 0.748 under 5-fold cross-validation, while reducing the estimated inference energy by 23.18$\times$ compared with dense MAC-only computation of the same network.
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All authors Youngung Han, Yului Jeong, Kyeonghun Kim, Dohyun Kweon, Suah Park, Hyunsu Go, Sungha Park, Anna Jung, Jinyong Jun, Yunho Choe, Yunjin Seo, Ken Ying-Kai Liao, Hyuk-Jae Lee, Nam-Joon Kim
Computer Vision
HALDETECT at ImageEval 2026 Shared Tasks: Answer-First Contrastive Grounding with QLoRA
Syed Mohaiminul Hoque and Md Sakhawat Hossain
Large
multimodal models tend to hallucinate visual detail fluently, which limits their deployment for fine-grained interpretation. We present HALDETECT, our system for the English
hallucination-detection track (Task 1b) of ImageEval 2026, in which a system must identify, from an image and three culturally plausible statements, the single visually grounded one. We frame the item as one contrastive decision, emit the answer before its explanation, and structure
reasoning around colour/texture, shape/form, and context. Our best submitted adapter fine-tunes Qwen2.5-VL-7B-Instruct with 4-bit QLoRA while freezing the
vision encoder and reaches Contrastive Instability (CI) 0.035 on the 1,000-item test set; we placed third of eight teams. Development experiments show that answer order can matter more than model scale and that adaptation beats prompting alone. Retrospective paired analysis of the released gold labels confirms the QLoRA gain over the best
prompt but not the small gap between the devtest-selected and best-test adapters, and reseeding all four training sizes shows that the apparent data-scaling curve does not survive a seed change. The 35 residual errors are culturally plausible function, material, and recognition distinctions; naive adapter voting does not help.
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Computer Vision
When is Test-Time Adaptation Identifiable From Unlabeled Evidence?
Kartik Jhawar and Lipo Wang
Test-time adaptation (TTA) offers many ways to update a deployed model without labels, but choosing the wrong update can make a strong source model worse. Recent methods therefore try to predict which adaptation will work from unlabeled test data. We ask a prior question: does the evidence given to the selector contain enough information to determine the best action at all? We show that this is not guaranteed, even with a perfect selector. If an observation channel makes two deployments look the same while their TTA rankings differ, reliable selection is impossible from that channel; richer evidence can restore the decision only when it resolves the relevant ambiguity. We make this boundary exact in a finite-batch Gaussian TTA model, where doing nothing beats mean recentering for small shifts, recentering wins beyond a unique critical shift, and the boundary shrinks as $1/\sqrt n$. Public
benchmark studies on CIFAR-100-C and DomainNet-126 show the same failure mode with modern TTA methods: changing only deployment structure can reverse the oracle action while global order-blind evidence remains unchanged. The result is a practical way to separate two failure modes that are usually mixed together: a weak selector versus an information channel that cannot support the desired decision in the first place.
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Computer Vision
Tri-DehazeGS: Scene–Medium Decoupled Gaussian Splatting with Transmittance-Aware Optimization
Kui Jiang, Yang Gu, Jiacheng Liu et al. (+3 more)
Recovering clean 3D scenes from hazy multi-view images is challenging because haze attenuates scene radiance and introduces atmospheric scattering. Recent scattering-aware Gaussian Splatting methods introduce physical haze models into reconstruction, but they often apply degradation in image space or bind medium-related variables to Gaussian primitives, which can entangle clean scene radiance with atmospheric effects. Moreover, low-transmittance regions provide weakened supervision for Gaussian optimization, causing distant or dense-haze areas to be under-reconstructed. We argue that clean reconstruction under haze requires both scene–medium disentanglement and transmittance-aware optimization rebalancing. To this end, we propose Tri-DehazeGS, a scene–medium decoupled Gaussian Splatting framework. It represents the clean scene with Gaussian primitives, models the participating medium using an independent view-shared tri-plane field, and composes hazy observations through a physical scattering model. We further introduce Medium-Decoupled Transmittance Gradient Compensation (MD-TGC), which compensates haze-suppressed gradients after medium freezing without altering forward rendering. Experiments on real and synthetic haze
benchmarks show that Tri-DehazeGS improves clean novel-view reconstruction. Code is available at https://github.com/aptx46/Tri-DehazeGS.
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All authors Kui Jiang, Yang Gu, Jiacheng Liu, Shiyu Liu, Youyu Chen, Hui Liu
Computer Vision
CEM-TUDASR: Computationally efficient multi-modality transformer based unsupervised domain adaptive super-resolution approach
Anjali Sarvaiya, Jay Kadel, Kishor Upla et al. (+1 more)
Wireless Capsule Endoscopy (WCE) enables non-invasive visualization of the gastrointestinal tract, but its miniaturized optics, sensor limitations, and wireless transmission constraints result in low-resolution images with reduced visibility of diagnostically important structures. This paper proposes CEM-TUDASR, a computationally efficient unsupervised
Transformer-based super-resolution framework for WCE image enhancement without paired low-resolution (LR) and high-resolution (HR) training data. A domain-adaptive degradation network synthesizes realistic WCE-like LR images from HR conventional endoscopy images, reducing the domain gap and enabling effective unpaired learning. The SR generator integrates Deep
Attention Blocks (DABs) and a Fusion
Attention Block (FAB) to capture long-range contextual dependencies and fine local structures while preserving perceptual and structural fidelity. The model is trained on a curated
dataset derived from Kvasir Capsule and evaluated on KID and GIANA for cross-
dataset generalization. No-reference quality metrics, including BRISQUE, PIQE, NIQE, and the domain-specific EndoQM, show that CEM-TUDASR consistently outperforms existing unsupervised SR methods. Qualitative results further demonstrate improved restoration of mucosal textures, vascular patterns, and clinically relevant anatomical details. Cross-domain experiments on retinal images additionally demonstrate the adaptability of the framework. With only 2.67 million parameters and 169.94 GFLOPs, CEM-TUDASR achieves high-quality reconstruction while maintaining computational efficiency, making it suitable for resource-constrained clinical and embedded endoscopic applications.
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All authors Anjali Sarvaiya, Jay Kadel, Kishor Upla, Kiran Raja
Computer Vision
A Multi-View and Confusion-Guided Ensemble Framework for Robust Synthetic Image Attribution
Zuomin Qu
Synthetic image attribution (SIA) has become increasingly important with the rapid advancement of text-to-image generation models. However, accurately identifying the source model of a generated image remains challenging due to the growing similarity among modern
diffusion-based generators and the presence of diverse post-processing operations. In this report, we present a multi-view and confusion-guided ensemble framework for the Synthetic Image Attribution Challenge of the DLMMDD Workshop at ICANN 2026. Our approach integrates multiple complementary architectures, including FFT-ConvNeXt, DINOv2, CLIP, and Xception, to capture diverse attribution cues from frequency, semantic, and forensic perspectives. To improve robustness against unknown degradations and image manipulations, extensive data augmentation strategies are employed during training, simulating realistic post-processing operations such as compression, resizing, grayscale conversion, and blur. Furthermore, we analyze the confusion patterns of the ensemble model and observe severe ambiguity between Stable
Diffusion 3 and Stable
Diffusion 3.5. To address this issue, we introduce a dedicated binary expert classifier that is selectively activated under low-confidence conditions. We additionally apply class-adaptive confidence calibration to improve the discrimination of challenging classes such as Tencent Hunyuan. The proposed framework achieved 99.53% on the public leaderboard and 99.20% on the private leaderboard. The source code and implementation details are publicly available at https://github.com/ZOMIN28/SIA.
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Computer Vision
Beyond Visual Quality: Evaluating Physical Consistency under Ego-Motion with EgoGenEval
Yilin Long, Chenming Zhu, Zitang Gou et al. (+2 more)
Recent visual generators produce high-fidelity images yet often violate physical consistency under ego-motion, limiting their use for spatial
reasoning and
embodied planning. Existing
benchmarks largely focus on isolated images or single-step quality, leaving this challenge underexplored. We introduce EgoGenEval, a geometry-grounded, pose-free
benchmark designed to evaluate the physical consistency of visual generators under ego-motion, and organize our study into two parts. (1) EgoGenEval contains 1,400 cases and 2,360 target views spanning single-step and multi-step ego-motion. It separately measures Camera Motion Grounding (CMG) and Scene State Preservation (SSP), with both metrics validated against blinded human judgments. Evaluating 16 pose-free generators together with two pose-conditioned references reveals that current models struggle to execute camera motion while maintaining scene state, and that no system performs well on both axes at once. (2) To examine whether
benchmark-derived data can improve these capabilities, we build EgoGen-Train from the same geometry-grounded pipeline and run controlled SFT studies. These show that pairwise supervision does not reliably improve camera-motion grounding and scene-state preservation together: even at the full training pool and the longest budget, scene preservation gains a fraction of what camera motion does. This points to the pairwise teacher-forced objective itself as the binding constraint, motivating a trajectory-centric paradigm that couples self-conditioned rollouts with explicit pose and visibility supervision.
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All authors Yilin Long, Chenming Zhu, Zitang Gou, Jingli Lin, Tai Wang
Computer Vision
UniH$^3$: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration
Zhiwen Yang, Jiayin Li, Chengyu Liu et al. (+3 more)
All-in-One medical image restoration (MedIR) aims to address diverse tasks across modalities and degradation types using a single universal model. Existing methods typically prioritize modeling inter-task heterogeneity (e.g., distinct data distributions and degradation types). However, they largely neglect the inherent homogeneity present in medical images, such as widely shared anatomical structures within and across modalities, which can be leveraged to ease model training and improve generalization. To this end, we propose UniH3, a novel framework that Unifies Hierarchical Homogeneity and Heterogeneity for all-in-one medical image restoration. Specifically, to comprehensively exploit homogeneity, we introduce a Hierarchical Homogeneity
Memory (H2M) module that progressively distills intra- and inter-task homogeneity priors from high-quality images during training, and adaptively retrieves the most relevant priors tailored to the input for guided restoration. These retrieved priors are then injected into the restoration pipeline via an efficient Homogeneity-Guided
Attention (HGA) mechanism. Furthermore, to comprehensively address heterogeneity, we design a Hierarchical Heterogeneity Balancer (H2B) that mitigates both inter- and intra-task conflicts during optimization, facilitating balanced and effective multi-task learning. Extensive experiments on two large-scale
benchmarks, MedIR-2D-500K and MedIR-3D-3K, demonstrate that UniH3 achieves state-of-the-art performance on both all-in-one and single-task medical image restoration. We hope this work establishes a strong
benchmark and advances the development of general-purpose medical image restoration models. Code is available at https://github.com/Yaziwel/UniH3.
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All authors Zhiwen Yang, Jiayin Li, Chengyu Liu, Hui Zhang, Bingzheng Wei, Yan Xu
Computer Vision
LAION-Mobile: Evaluating Deepfake Detectors On One Million Smartphone Photos
Achim von Stryk and Janis Keuper
Most Deepfake detectors report near-perfect AUC scores on their reference
benchmarks. However, a recent ICML position paper argues that these evaluations collectively neglect the impact of modern smartphone photography: the widely used on-device neural image-signal processing pipelines (like multi-sensor fusion or noise and motion-blur suppression) increasingly shift the imaging paradigm from simple lens projections towards computational photography. Hence, devices actually generate, rather than record photos. This increases the risk that deepfake detectors may flag ordinary phone photos as fake. Due to the lack of large-scale
datasets containing images from modern smartphones, this hypothesis has so far only been tested in small proof-of-concept studies. The aim of this paper is to close this gap. We introduce LAION-Mobile, an open
dataset containing about 1 million smartphone images with EXIF metadata distilled from re-LAION-5B. Evaluating twelve state-of-the-art deepfake detectors with their original paper checkpoints on a 9,115-image evaluation sample of this pool (DIRE on 738), we report three key findings: (i) On modern AI content no detector exceeds AUC 0.624, and five of twelve fall below chance. (ii) Real-photo false-alarm rates are an artefact of threshold calibration: thresholds fitted on legacy GAN data make several detectors look deployable (less than 11 percent FPR), yet the same detectors flag 17-91 percent of real photos once the identical criterion is refit on modern content. (iii) Consequently, no detector both beats chance on modern AI content and keeps a deployable real-photo false-alarm rate. Mirroring the device mix of web collections, the corpus probes the first neural-ISP generation (2018-2020); current flagships are essentially absent, leaving the modern-ISP regime as the open gap.
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Computer Vision
ReconPlusGen: Injecting Reconstruction Prior into Multi-view 3D Generation through Noise Inversion and Modulation
Jiarui Liu, Heng Li, Weiyu Li et al. (+7 more)
Qualitative results and an illustration of our core idea. Top left: reconstruction results on
benchmark images. Top right: reconstruction results on real-world images. Bottom: illustration of reconstruction-guided noise initialization and modulation. Given multiple input images, we predict a point cloud in canonical space, deterministically inject the predicted geometry into the
diffusion process through noise inversion, and modulate the resulting noise to preserve the generative flexibility required to complete unobserved regions and refine visible geometry.
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All authors Jiarui Liu, Heng Li, Weiyu Li, Keng Deng, Junyuan Deng, Zheng Zhongxing, Junyu Huang, Jiahao Chang, Xiaoguang Han, Ping Tan
Computer Vision
Beyond Benchmarks: Using VLMs to Reveal Systematic Classification Failures Under Real World Conditions
Dieuwertje Alblas, Alma M. Liezenga, Jan Erik van Woerden et al. (+3 more)
Verification and validation (V&V) of classification models is crucial to enable a wide range of sensor processing applications. Currently, the V&V process relies on time-consuming manual inspection of erroneous samples to find meaningful patterns. This work explores the use of
Vision Language Models (VLMs) to speed up this laborious process. VLMs are trained to embed images into a semantically meaningful vector representation, from which human-interpretable systematic errors can be distilled. Deploying such VLM-based methods in a defence context introduces two major challenges: (1) the defence domain is underrepresented in the training data of VLMs, and (2) surroundings and context are less diverse than for other domains. This study provides an initial assessment of the suitability of VLM-based methods for V&V of defence applications. We propose a VLM-based error slice detection (ESD) method that independently groups and labels systematic errors made by a classification model. We demonstrate that this method is able to identify operationally-relevant artificially added perturbations in a non-military
dataset. In a military context, our method clusters and describes images based on their surroundings, but also exhibits overlap between cluster descriptions. We further investigate the difference in
embedding variation between our military and non-military
dataset, which remains a topic of interest. Although the results do not yet warrant fully automated V&V through VLM-based ESD, they show that VLMs could be used to accelerate V&V processes in the future.
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All authors Dieuwertje Alblas, Alma M. Liezenga, Jan Erik van Woerden, Fedor Taggenbrock, Dalia Aljawaheri, Klamer Schutte