Computer Vision
Double-Helix Vision (DH-V2): A Geometry-Based Visual Sampler for Bandwidth-Constrained Perception
Jinwen Wen
We present Double-Helix
Vision (DH), a geometry-based visual sampler that compresses 2D images into compact 1D signals using paired golden-ratio-inspired spiral trajectories. Rather than processing every pixel uniformly, DH employs two phase-shifted helices (Alpha and Beta, offset by 180 degrees) to sample the image with biologically-inspired foveation: high density at the center, sparse coverage at the periphery. At 4K resolution, DH achieves a 1,433x compression ratio (99.93% reduction) while preserving the
geometric structure of the scene. The full perception pipeline – including spatial mapping, temporal collision detection, and intra-frame structural disparity estimation – runs in 0.52 ms at 1080p on CPU-only hardware, with no neural network dependencies. On CIFAR-10 at extreme sampling budgets (K=128 points per helix), DH achieves a +6.03% accuracy gain over uniform random sampling. A JSON-serializable Robotics API is provided, delivering sub-millisecond spatial perception reports in 2.7 KB packets. Code and
benchmarks are available under the MIT License.
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Computer Vision
ScoutVLA: UAV-Centric Active Perception via a Dual-Expert VLA Model for Open-World Embodied Question Answering
Wenhao Lu, Zhengqiu Zhu, Xiaofeng Wang et al. (+7 more)
Aerial
Embodied Question Answering (EQA) requires Unmanned Aerial Vehicles (
UAVs) to actively perceive the environment and answer natural
language questions. Existing outdoor EQA systems usually stop once the target enters the
UAV's field of view, leaving the fine-grained viewpoint adjustment needed for evidence-seeking questions largely unresolved. To address this issue, we introduce FG-EQA, a fine-grained active perception EQA
benchmark with more than 40K simulated trajectories and 1K real-world trajectories. Drawing inspiration from the “waggle dance” of scout bees, which iteratively adjust their flight paths to verify target information, we propose ScoutVLA, an evidence-driven
Vision-
Language-Action model for outdoor EQA. To emulate this active exploration behavior, ScoutVLA features a decoupled dual-expert architecture: a
vision-
language expert infers the semantic intent to identify missing evidence, while an independent action expert employs high-DoF flow matching to generate continuous viewpoint-refinement trajectories. To balance the competing demands of continuous control and semantic
reasoning, we devise a decoupled training strategy with a knowledge insulation mechanism that prevents the action gradients from erasing the model's
multimodal reasoning ability. Extensive simulated experiments and a qualitative real-world field study both verify the superiority of ScoutVLA over the state-of-the-art baselines, demonstrating a 10.48$\boldsymbol{\times}$ higher average strict success rate and a 7.72$\boldsymbol{\times}$ higher average QA correctness.
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All authors Wenhao Lu, Zhengqiu Zhu, Xiaofeng Wang, Xiaoran Zhang, Yatai Ji, Yong Zhao, Yue Hu, Yingzhen Nie, Jinlong Zhu, Zheng Zhu
Computer Vision
An Empirical Analysis of Optimization Dynamics and Sparsity Boundaries in Large-Scale Pedestrian Attribute Recognition
Houssam El Mir
Pedestrian Attribute Recognition (PAR) is critical for
video surveillance, enabling forensic search and re-identification systems. Extreme class imbalance remains a fundamental obstacle when merging PETA and PA-100K into a 109,000-image composite corpus, where minority attributes have positive sample fractions below 1%. This causes standard BCE optimization to suppress rare traits, a phenomenon we term the majority negative class cheating trap. We present a systematic ablation of Multi-Label Focal Loss hyperparameters (alpha and gamma) on a ResNet-18 backbone. A calibrated configuration (alpha=0.50, gamma=2.0) achieves a Macro F1-score of 62.32%, matching BCE baseline while preserving superior hard-example mining and convergence dynamics. Our approach uses pure loss-function engineering with zero computational overhead for edge deployment. We identify the Sparsity Wall, a hard boundary where positive sample fractions below 0.1% make global loss reweighting ineffective, requiring instance-level intervention.
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Computer Vision
SceneMiner: Identity-Preserving Multi-Task Fine-Tuning for Unified BEV Scene Mining
Abdalmalek Aburaddaha, Venkatraman Narayanan, Keval Thaker et al. (+1 more)
Mining hard, safety-critical scenes from driving logs is bottlenecked by the absence of difficulty labels, and no single proxy, collision risk, trajectory ambiguity, or semantic rarity suffices to find such scenes on its own. We present SceneMiner, a unified, camera-only bird's-eye-view pipeline that emits complementary mining signals from a frozen
vision-
language backbone in a single forward pass, with no
LiDAR or radar: a
retrieval embedding for text-prompted scenario search, a multi-label scene-tag distribution, and a continuous physics-based risk score (a motion forecast is a byproduct, not a contribution). Building such a multi-head model exposes our central finding, a failure mode we term cross-task interference: adding or upgrading one head shifts a shared activation stream and degrades weight-frozen sibling heads, so freezing parameters alone is insufficient. Our contribution, identity-preserving multi-task fine-tuning, removes this interference by zero-initializing every new sub-module and freezing every parameter that feeds the shared stream. The mining heads are thereby preserved bit-identically while training only ~102k parameters. The tagging head reaches mAP 0.4614 (micro-F1 0.5557) on 20 scene tags by pooling each scene into 32 visual
tokens, and the
embedding head supports text-prompted
retrieval, validated qualitatively. Code is available at: https://anonymous.4open.science/r/sceneminer_anonymous-64E5
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All authors Abdalmalek Aburaddaha, Venkatraman Narayanan, Keval Thaker, Samir A. Rawashdeh
Computer Vision
On the Study of Biometric Spoofing Detection using Deep Learning
Kumar Kartikey and Nikos Komninos
Biometric systems are increasingly deployed in security applications; however, they remain vulnerable to spoofing attacks, in which attackers exploit counterfeit biometric data to gain unauthorized access. This research evaluates the effectiveness of state-of-the-art machine learning models, MobileNetV2, DenseNet-121, Inception-v3, and Spoof Trace Disentanglement (STD) in detecting spoofing attacks within facial recognition systems. Using the CelebA-Spoof
dataset, the study evaluates model effectiveness using metrics such as accuracy, precision, recall, and F1 Score. Cross-
dataset validation is carried out on the MSU-MFSD
dataset to assess generalizability. The results show MobileNetV2 as the most efficient model, achieving 92% accuracy while balancing computational effectiveness, making it appropriate for real-life applications. Inception-v3 shows moderate robustness, while DenseNet-121 and STD struggle with generalization. The findings highlight the need for advances in domain adaptation and hybrid architectures to enhance biometric security systems.
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Computer Vision
Towards Fully Automated Exam Grading: Fairness-Aware Recognition of Handwritten Answers with Foundation Models
Hartwig Grabowski
Correcting handwritten exams by hand is time-consuming and error-prone, particularly for large cohorts, while fully digital exams tend to force a didactic narrowing towards closed question formats. A practical middle ground keeps paper-based, problem-oriented tasks but records the assessment-relevant answers as single capital letters in a table that a machine can read. The open question is whether this reading can be made accurate and, above all, fair enough for unsupervised grading. Earlier automated approaches reached only about 88%–91% recognition – too low – and failed on the cases that matter most: answers placed outside the
cell, crossed out, or written in cursive. We show that general-purpose
vision-
language foundation models (VLMs), which interpret the page rather than match pixel templates, close this gap. On a
benchmark of 61 anonymised exams (3141 answer positions) the best model reaches 98.4% accuracy, well above the previous baseline. Crucially, we centre the evaluation on
fairness: we distinguish false negatives (a correct answer marked wrong, which disadvantages the student) from false positives, and a lightweight
prompt that supplies the reference solution as context lowers the false-negative rate to 0.58%. Under an exemplary grading scheme only three of the 61 exams would be graded worse, all caught by a student self-review step. Fully automated,
fairness-aware exam grading at scale is therefore defensible; we release the anonymised
benchmark to support reproducibility.
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Computer Vision
PT-WNO: Point Transformer with Wavelet Neural Operator for 3D Point Cloud Semantic Segmentation
Nhut Le and Maryam Rahnemoonfar
Point cloud semantic
segmentation requires architectures that capture both fine-grained local geometry and broad global scene structure.
Transformer-based networks have demonstrated strong performance by focusing on detailed local feature aggregation; however, global context is conveyed primarily through skip connections across encoder-decoder stages, which we argue is insufficient for full scene understanding. We hypothesize that augmenting skip connections with a learnable global feature extraction module allows the network to acquire scene-level knowledge before descending into local detail, leading to richer and more contextually grounded representations. To this end, we propose Point
Transformer with Wavelet Neural Operato (PT-WNO), which integrates a shared Wavelet Neural Operator (WNO) branch alongside the skip connections of a point cloud
transformer backbone. At each encoder-decoder transition, point features are projected onto a dense 3D volumetric grid where the WNO captures multi-scale global spectral context through learnable wavelet decomposition and reconstruction. These global features are fused back into the network via lightweight adapters, complementing rather than replacing the existing skip connections. Experiments on four large-scale 3D point cloud
benchmarks demonstrate the effectiveness of PT-WNO. On S3DIS (Area 5), PT-WNO achieves 71.59% mIoU, outperforming the Point
Transformer v3 (PTv3) baseline by +1.03 points. On DALES it achieves 81.05% mIoU (+1.47 over the baseline). On ScanNet~v2, PT-WNO obtains 76.19% mIoU, remaining competitive with the baseline (76.36%).
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Computer Vision
Exploring Adaptive Masked Reconstruction for Self-Supervised Skeleton-Based Action Recognition
Shengkai Sun, Zhiyong Cheng, Zefan Zhang et al. (+3 more)
Recently, masked skeleton reconstruction models have emerged as strong action representation learners, driving significant progress in self-supervised skeleton-based action recognition. However, existing state-of-the-art methods must predict an exceedingly large number of spatiotemporal patches, significantly prolonging training time. Besides, by treating all spatiotemporal regions equally during reconstruction, these models are distracted from learning the critical motion patterns that underlie action semantics. To address these challenges, we propose Adaptive Masked Reconstruction (AMR), a faster and stronger pre-training framework. We first decouple the decoder from the encoder, enabling flexible prediction of larger spatiotemporal patches and dramatically reducing reconstruction complexity. Given that larger patches contain more complex information, which is challenging to predict and consequently degrades performance, we accordingly introduce an adaptive guidance module. This module identifies regions of high motion informativeness, guiding the model to focus on the most discriminative parts of each patch and alleviating reconstruction difficulty. Experiments on NTU RGB+D 60, NTU RGB+D 120, and PKU-MMD
datasets demonstrate that AMR not only accelerates pre-training substantially but also improves downstream recognition accuracy, surpassing current state-of-the-art approaches.
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All authors Shengkai Sun, Zhiyong Cheng, Zefan Zhang, Jianfeng Dong, Zhihui Li, Meng Wang
Computer Vision
3D-CBM: A Framework for Concept-Based Interpretability in Generative 3D Modeling
Ahmad Al-Kabbany
This research introduces a framework for incorporating Concept Bottleneck Models (CBMs) into 3D generative architectures to address the inherent 'semantic gap' in deep
geometric learning. As deep models become central to 3D content creation, explainability shifts from a peripheral feature to a fundamental requirement for trust and accountability in safety-critical domains such as healthcare and manufacturing. CBMs provide an intrinsic interpretability solution by constraining latent representations to align with human-defined concepts, yet their application to unstructured 3D data remains largely unexplored. We design, implement, and validate a formal 3D-CBM architecture that maps raw
geometric inputs, including point clouds and meshes, into a multi-tiered taxonomy of interpretable primitives and functional attributes. The framework further identifies strategic
datasets, such as PartNet and ShapeNet, specialized for concept-based supervision. Experimental results from a 3D part-manipulation proof-of-concept experiment demonstrate the framework's efficacy, achieving a concept prediction accuracy of 88.8% and a Chamfer Distance of 0.0115. Critically, the model enables precise test-time intervention, allowing for the interactive correction of structural errors. This work establishes a foundation for semantically-steerable 3D generation and invites further exploration into collaborative human-in-the-loop design systems.
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Computer Vision
A Scalable PyTorch Abstraction for Multi-GPU Gaussian Splatting
Matthew Cong, Francis Williams, Jonathan Swartz et al. (+3 more)
Gaussian splatting methods have become increasingly popular for neural reconstruction of the real world. However, they are often limited in scale and resolution due to compute and
memory constraints. We present a multi-GPU Gaussian splatting approach that scales reconstruction to higher resolutions and larger scenes while abstracting away the code complexity typically associated with distributing a model. To accomplish this, we propose a PyTorch backend that distributes the Gaussian parameters and splatting operators across GPUs via CUDA unified
memory and NVLink. Because distribution occurs at the operator level, the model code requires no explicit cross-device communication. More broadly, the backend exposes multiple GPUs as an aggregate PyTorch device and supports other PyTorch operators. We demonstrate city-scale reconstructions with street-level detail consisting of over 1 billion Gaussian splats, more than 25 times as many as the current state of the art.
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All authors Matthew Cong, Francis Williams, Jonathan Swartz, Mark Harris, Sanja Fidler, Ken Museth
Computer Vision
DeceptionX: From Multimodal Evidence to Explainable Deception Detection
Jiayu Zhang, Shuo Ye, Jiajian Huang et al. (+8 more)
Deception detection is a critical and highly challenging task within affective computing and behavioral analysis. Existing deep learning methods typically treat this task as a straightforward classification problem; however, this black-box approach lacks interpretability and fails to capture the complex logical deduction processes utilized by human experts when identifying lies. While
Multimodal Large
Language Models (MLLMs) have shown potential, applying them effectively requires a bridge between low-level audiovisual cues and high-level logical
reasoning. In this paper, we propose DeceptionX, a novel MLLM framework that shifts the paradigm of deception detection from black-box classification to an interpretable Observe-Think-Summarize
reasoning process. To address the scarcity of high-quality
reasoning data, we first constructed DeceptChain, a high-quality
dataset developed through a human-in-the-loop process. This
dataset synthesizes fine-grained visual and auditory evidence (such as micro-expressions and vocal tremors) into structured chain-of-thought
reasoning data. Furthermore, we propose a three-stage training pipeline and a Discrepancy-Aware Redundancy Elimination~(DARE) strategy for DeceptionX to further enhance the model's generalization capabilities. Extensive experiments demonstrate that DeceptionX not only outperforms existing MLLM baselines and state-of-the-art methods on standard real-world
benchmarks but also provides transparent, expert-level
reasoning paths, bridging the critical gap between accuracy and interpretability in
multimodal deception detection.
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All authors Jiayu Zhang, Shuo Ye, Jiajian Huang, Yawen Cui, Taorui Wang, Wei Xia, Zeheng Wang, Haowen Tang, Yelin Wang, Hui Ma, Zitong Yu
Computer Vision
From Simulation to the Real-World: An In-Field 6D Pose Dataset and Baseline for Robotic Strawberry Harvesting
Woojung Son, Won Suk Lee, Zijing Huang et al. (+4 more)
Robotic strawberry harvesting requires precise 6D pose estimation; however, collecting 6D pose ground truth in real agricultural fields is inherently challenging. Existing strawberry 6D pose estimation studies have therefore relied mainly on synthetic data, leaving their in-field performance unquantified. In this work, we obtain ground truth indirectly, by recovering camera poses via PnP, reconstructing each scene at metric scale, and annotating a single 3D bounding box per strawberry that is propagated across all frames, yielding, to the best of our knowledge, the first real-world 6D pose ground-truth
dataset of red-stage strawberries collected at an actual strawberry farm (12,040 images). We also introduce a synthetic
dataset rendered in NVIDIA Isaac Sim, featuring scene-level realism and domain randomization. Despite this improved simulation setup, models trained on synthetic data alone fail to transfer to in-field images, while introducing a small amount of real data improves both translation and rotation accuracy across all backbones. Under the
monocular RGB setting evaluated here, rotation is largely recovered once real data is used, and depth is what limits pose accuracy. These baselines across backbone encoders serve as a reference for future work. The real-world
dataset is publicly available at https://huggingface.co/
datasets/WoojungSon/FieldStraw6D, and the data-collection pipeline is available at https://github.com/wjson2435/FieldStraw6D-pipeline.
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All authors Woojung Son, Won Suk Lee, Zijing Huang, Daeun Choi, Catia Silva, Yu She, Yan Gu
Computer Vision
NSVQ: Mitigating Codebook Collapse by Stabilizing Encoder Drift in Vector Quantization
Hao Lu, Yongxin Guo, Onur Koyun et al. (+3 more)
Vector quantization is central to modern generative modeling pipelines, but large-codebook VQ models often suffer from codebook collapse. We identify encoder drift as a key driver of this failure: as the encoder moves the latent distribution, sparsely updated code vectors can lag behind, lose assignments, and increase quantization error, creating a feedback loop through the straight-through
estimator. We propose NSVQ, a non-stationary-aware VQ training strategy that combines a dense non-stationary
embedding loss, codebook replacement, and stage-wise encoder freezing. NSVQ first helps the codebook track encoder drift during early training, then freezes the encoder to consolidate the codebook under a fixed latent geometry, and finally reintroduces
adversarial refinement. Experiments on ImageNet-1k show that NSVQ improves reconstruction quality while maintaining full codebook utilization. On ImageNet-1k at 128$\times$128 with 65,536 codes, NSVQ reduces rFID from 2.39 to 2.10 compared with SimVQ, while both methods maintain 100% utilization. Additional latent
diffusion experiments show that NSVQ also improves downstream ImageNet generation FID.
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All authors Hao Lu, Yongxin Guo, Onur Koyun, Zhengjie Zhu, Abbas Alili, Metin N. Gurcan
Computer Vision
DarkVGGT: Seeing Through Darkness Using Thermal Geometry
Minseong Kweon, Wenyuan Zhao, Nuo Chen et al. (+6 more)
Recent feed-forward 3D reconstruction methods have demonstrated strong performance and flexibility in efficient end-to-end scene geometry estimation from image streams. However, their reliance on visible-light appearance makes them vulnerable in dark and low-visibility environments, where RGB cues are severely degraded and
geometric evidence becomes ambiguous. To address this challenge, we propose DarkVGGT, a structured RGB-T adaptation of pretrained VGGT for robust 3D estimation in low-light scenes. DarkVGGT introduces two complementary modules. First, complementary thermal feature factorization processes thermal
tokens through content-preserving and high-frequency residual pathways controlled by learned complementary allocation weights. Second, geometry-shared thermal routing aligns RGB and thermal representations in a shared latent space and selectively injects a gated feature correction into the RGB stream. Together, these components enable accurate thermal-informed geometry estimation under degraded RGB conditions while largely preserving performance in well-lit environments. Experiments on low-visibility RGB-T
benchmarks demonstrate consistent improvements in both depth and camera pose estimation over existing feed-forward geometry baselines.
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All authors Minseong Kweon, Wenyuan Zhao, Nuo Chen, Lulin Liu, Huiwen Han, Zihao Zhu, Srinivas Shakkottai, Chao Tian, Zhiwen Fan
Computer Vision
Semantic Segmentation of Node and Edge Diagrams for Assistive Technology
Michael Cormier, Yichun Zhao, Laura Paul et al. (+3 more)
In this paper, we present a novel set of related models for semantic
segmentation of node-link diagrams. These diagrams are frequently used to represent mathematical graphs, relationships between concepts, and flowcharts. Such diagrams are difficult to access non-visually; while some assistive interfaces have been designed for node-link diagrams, they rely upon a machine-readable representation of the diagram, whereas such diagrams will generally be made available as bitmap images. Our compact deep learning models show excellent quantitative and qualitative performance on a large synthetic
dataset of node-link diagrams, reaching per-pixel accuracy over 93%.
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All authors Michael Cormier, Yichun Zhao, Laura Paul, Cameron Swift, Duc Tri Dang, Miguel Nacenta
Computer Vision
TRON: Tracing Rays to Orchestrate a Neural Renderer for 3D Gaussian Reconstructions
Or Perel, Hassan Abu Alhaija, Zian Wang et al. (+4 more)
We introduce TRON, a rendering framework that combines 3D Gaussian ray tracing with neural rendering to enable realistic and controllable rendering of real-world 3D scenes under novel lighting, dynamic object motion, object insertion, and material editing. Prior approaches that rely solely on physically based rendering (PBR) of Gaussian representations struggle to achieve realistic relighting due to imperfections in reconstructed geometry, material estimates, and light transport estimation. At the same time, neural rendering methods often lack an explicit scene representation, limiting their ability to support interactive editing with fine-grained manipulation. TRON bridges these two paradigms. We use intrinsic decomposition priors from a learned inverse rendering model to regularize the material properties of a Gaussian field, and repurpose a ray tracer to provide radiometric guidance rather than final pixels. By treating this output as a structured 3D scaffold, we empower a lightweight neural renderer to bridge the domain gap between shading-model constrained estimates and photorealistic output. Our key insight is that the combination of explicit 3D knowledge with robust material priors provides speed and controllability, while neural rendering enables the synthesis of photorealistic images. To support real-world scenarios, we train our neural renderer with a multi-stage strategy consisting of large-scale pretraining and targeted fine-tuning on a newly constructed
dataset of 2.1M rendered synthetic and real-world frames from 3D reconstructions. TRON outperforms Gaussian-based relighting methods in realism, and prior neural renderers in editability and speed. To the best of our knowledge, TRON is the first method to enable practical interactive applications in captured 3D environments, offering realistic appearance under dynamic
geometric, lighting and material conditions.
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All authors Or Perel, Hassan Abu Alhaija, Zian Wang, Jacob Munkberg, Matan Atzmon, Sanja Fidler, Masha Shugrina
Computer Vision
i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models
Boya Zeng, Tianze Luo, Shu Pu et al. (+4 more)
Diffusion models have consistently driven progress in text-to-image generation. However, it is challenging to attribute recent progress to specific modeling and data choices: state-of-the-art open-weight models provide limited ablations, and do not disclose their training data and full training details. The research community needs fully open (weights, data, and code) models as a foundation for further research; yet existing fully open models still fall significantly short of leading models in performance. In this project, we conduct a systematic investigation of the modeling and data design choices in text-to-image
diffusion training and inference with 300+ controlled experiments totaling 700K+ TPU v6e hours. Our experiments highlight several empirical findings (e.g., equal weighting is a strong default for mixing curated
datasets) and simple design decisions (e.g., larger text encoder adapters improve performance with minimal added parameters) for training strong models. Guided by these insights, we train i1, a 3B-parameter text-to-image
diffusion model using only publicly available
datasets. i1 is competitive with leading models on five representative
benchmarks (GenEval, DPG, PRISM, CVTG-2K, and LongText), and outperforms the best existing fully open model by 29.5 absolute percentage points on average. We provide the i1 checkpoints, training and inference code, and the data processing pipeline. Together, our findings and the i1 recipe establish a practical foundation for future open research in text-to-image
diffusion models. Our code is available at https://github.com/zlab-princeton/i1.
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All authors Boya Zeng, Tianze Luo, Shu Pu, Jucheng Shen, Taiming Lu, Gabriel Sarch, Zhuang Liu
Computer Vision · Cross-listed from eess.IV
Intelligent Skin Cancer Detection Using a Multispectral Metasurface and a Hybrid
Afsane Saee Arezoomand
Skin cancer is among the most prevalent malignancies worldwiAdbe satnradcitts early detection is essential for improving patient survival and reducing treatment costs Conventional dermoscopic and visual imaging techniques are primarily limited to the visible spectrum and often fail to capture subtle spectral signatures associated with early stage malignancies This study proposes an innovative framework that integrates a multispectral metasurface for imaging with a hybrid deep learning architecture based on Convolutional Neural Networks and
Vision Transformers The designed metasurface enables noninvasive acquisition of rich spectral information highly sensitive to tissue alterations while the hybrid CNN ViT model simultaneously extracts local and global features to robustly classify skin lesions Simulation-based evaluations demonstrate that the proposed method achieves approximately 98 accuracy 95 percentages sensitivity and 99 perentage specificity surpassing conventional RGB-based and single-architecture approaches Qualitative analyses using
attention maps reveal that the model focuses on clinically relevant lesion regions improving interpretability Overall the results indicate that combining metasurface based multispectral imaging with hybrid deep learning can introduce a new generation of diagnostic tools in dermatology and pave the way for portable fast and highly accurate clinical systems
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Computer Vision
EventRadar: Long-Range Visual UAV Discovery through Spatiotemporal Event Sensing
Zhiting Zhou, Xingchen Liu, Xinglin Yu et al. (+5 more)
Unauthorized unmanned aerial vehicle (
UAV) activity around airports, public venues, and other sensitive sites has made protected-airspace monitoring increasingly important. A practical sensing system must search a wide angular region, find small long-range targets, and return both bearing support and
UAV-specific evidence before a restricted perimeter is breached. Existing
UAV detection paths often rely on spatially organized evidence, such as body extent, silhouette, or track continuity. At long range, however, these cues become difficult to preserve and verify as the target footprint weakens and its image-plane support shrinks. EventRadar follows a complementary cue: propeller-induced temporal periodicity, which recent event-camera sensing studies have shown can reveal
UAV-specific motion after appearance becomes weak. We extend this cue to kilometer-scale active sensing with an event-camera prototype. Scene-Anchored Geometry Evidence (SAGE) fuses scanning events with IMU pose to maintain a bearing-indexed scene
memory, separating transient candidate support from persistent background clutter. Comb-guided Harmonic-Group Learned Iterative Shrinkage and Thresholding
Algorithm (CHG) then treats each candidate as a weak high-rate timing signal and recovers phase-insensitive harmonic evidence with fixed compute. Compared with related event-camera baselines on 700-1500 m
UAV event recordings, EventRadar achieves 0.990 mAP$_{.3}$ and 0.949 F1$_{.3}$, reduces FN$_{.3}$ to 0.009, and shows real-time feasibility in prototype profiling.
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All authors Zhiting Zhou, Xingchen Liu, Xinglin Yu, Jiashen Chen, Haoyang Wang, Jingao Xu, Yunhao Liu, Xinlei Chen
Computer Vision
Traits Run Deeper: Trait-Specific Asymmetric Fusion for Multimodal Personality Assessment
Jia Li, Qian Chen, Wei Wang et al. (+5 more)
Personality assessment aims to infer stable traits from dynamic behaviors across modalities like
language, voice, and facial expressions. Existing approaches often adopt a uniform
multimodal fusion strategy for all personality dimensions, overlooking trait-specific modality preferences and causing cross-modal interference. To address this, we propose Traits Run Deeper, a novel personality assessment framework consisting of three components. First, the
Multimodal Foundation Representation (MFR) module constructs personality-oriented inputs and incorporates psychology-informed semantic templates as anchors, enabling foundation models to capture trait-relevant behaviors. Second, the Trait-Specific Modality Fusion (TSMF) module employs an asymmetric fusion mechanism, allowing each dimension to selectively exploit different modality pathways to capture heterogeneous preferences while reducing cross-modal contamination. Third, the Distribution-Calibrated Personality Regression (DCPR) module mitigates label imbalance and central tendency bias through target distribution calibration, improving robustness and stability. Experimental results on the AVI Challenge 2026 validation set show that our framework reduces mean squared error (MSE) by approximately 25% compared with the baseline. Consistent improvements on the official test set demonstrate that our method achieves the best performance and ranks first in the AVI Challenge 2026 Personality Assessment Track. The source code will be made available at [https://github.com/MSA-LMC/TraitsRunDeeper](https://github.com/MSA-LMC/TraitsRunDeeper).
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All authors Jia Li, Qian Chen, Wei Wang, Xinyu Li, Zhenzhen Hu, Dongsheng Shao, Richang Hong, Meng Wang
Computer Vision
ARM: An AutoRegressive Large Multimodal Model with Unified Discrete Representations
Junke Wang, Xiao Wang, Jiacheng Pan et al. (+16 more)
This paper introduces ARM, a discrete representation-based AutoRegressive Model that unifies image understanding, generation, and editing within a next-
token prediction framework. ARM is built on three efforts: first, we train a discrete semantic visual tokenizer that maps images into compact
token sequences. Our tokenizer is supervised with multiple objectives that jointly promote semantic discriminability,
language alignment and faithful reconstruction, thereby supporting diverse tasks in a shared latent space. With this, we train a 7B autoregressive model over large-scale text and image
token sequences, seamlessly developing
vision-
language perception and generation capabilities. Finally, to further improve preference-aligned behavior for text-to-image generation and instruction-guided editing, ARM applies reinforcement learning (RL) to optimize task-level objectives such as visual quality, instruction adherence, and edit consistency. Surprisingly, the results show that RL not only substantially improves performance on the target tasks (e.g., raising WISE overall from 0.50 to 0.56, GEdit-Bench-EN G_O from 5.75 to 6.68), but also induces cross-task synergy between text-to-image generation and editing. Collectively, these findings highlight autoregressive modeling, when paired with strong representations and preference optimization, as a scalable foundation for
multimodal intelligence. Code: https://github.com/wdrink/ARM.
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All authors Junke Wang, Xiao Wang, Jiacheng Pan, Xuefeng Hu, Feng Li, Jingxiang Sun, Chaorui Deng, Zilong Chen, Yunpeng Chen, Kaibin Tian, Matthew Gwilliam, Hao Chen, Danhui Guan, Kun Xu, Weilin Huang, Zuxuan Wu, Haoqi Fan, Yu-Gang Jiang, Zhenheng Yang
Computer Vision
Next Forcing: Causal World Modeling with Multi-Chunk Prediction
Gangwei Xu, Qihang Zhang, Jiaming Zhou et al. (+4 more)
Autoregressive
video generation has emerged as a powerful paradigm for World Action Models (WAMs). However, existing approaches suffer from slow training convergence and limited converged accuracy, particularly at high frame rates, as the training supervision is confined to the current chunk without explicit signals about future dynamics; they also suffer from slow inference due to iterative
video denoising. In this paper, we present Next Forcing, a multi-chunk prediction (MCP) framework for causal world modeling that enables faster training, higher accuracy, and accelerated inference. Inspired by multi-
token prediction in large
language models, Next Forcing introduces an MCP training objective that augments the main model with lightweight auxiliary MCP modules to simultaneously denoise
video chunks at multiple future temporal horizons (next$^1$, next$^2$, next$^3$ chunks). These MCP modules form a causal chain across prediction depths, where intermediate features fused from multiple layers of the main model are leveraged to predict future dynamics, allowing near-future predictions to inform farther-future ones and providing dense multi-scale temporal supervision back to the main model. During training, the MCP modules significantly accelerate convergence and improve converged accuracy, especially at high frame rates: at 50 fps, Next Forcing achieves a 93.1% relative improvement over LingBot-VA at 5k training steps and 2.3x faster convergence, and establishes new state-of-the-art results on the RoboTwin
benchmark (94.1/93.5% on Clean/Random). At inference, the MCP modules can be retained to predict the next
video chunk in parallel with the current one, achieving 2x inference acceleration. Next Forcing also demonstrates significant improvements on PhyWorld, a
benchmark evaluating adherence to physical laws in
video generation, and over 50% FVD reduction on general
video pretraining.
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All authors Gangwei Xu, Qihang Zhang, Jiaming Zhou, Xing Zhu, Yujun Shen, Xin Yang, Yinghao Xu
Computer Vision
AnyMod-LLVE: Low-Light Video Enhancement with Modality-Agnostic Inference
Hangfeng Liang, Yutao Hu, Yanhan Hu et al. (+3 more)
Low-light
video enhancement (LLVE) remains a challenging task due to severe information degradation under low-illumination conditions. Recent
multimodal approaches have significantly improved enhancement performance by incorporating auxiliary modalities, such as event streams and infrared images. However, these methods typically assume the availability of these modalities at inference, which is often not feasible in real-world scenarios. To solve this problem, in this work, we propose AMNet, a unified
multimodal framework for LLVE, to support flexible modality-agnostic inference, where auxiliary modalities may be unavailable. To address the issue of modality absence, we introduce a Spatial-Spectral Dual-Gated Translator that learns the correspondence between auxiliary modalities and RGB inputs, producing implicit auxiliary representations to support the robust enhancement. Additionally, to fully facilitate the learning of cross-modal correspondence, we conduct large-scale
multimodal pretraining based on the RGB-only
dataset with synthetic auxiliary modalities. Extensive experiments demonstrate that AMNet could handle arbitrary inference-time modality combinations and exhibits superior performance for LLVE under modality absence conditions. Code and models are available on the project page.
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All authors Hangfeng Liang, Yutao Hu, Yanhan Hu, Xiaohan Wu, Wenqi Shao, Ying Fu
Computer Vision
Lip Forcing: Few-Step Autoregressive Diffusion for Real-time Lip Synchronization
Paul Hyunbin Cho, Jinhyuk Jang, SeokYoung Lee et al. (+7 more)
Diffusion-based lip synchronization models achieve strong visual quality and audio-visual
alignment, but full-sequence bidirectional
attention and many denoising steps make them impractical for real-time inference. We present Lip Forcing, to our knowledge the first autoregressive
diffusion method for
video-to-
video (V2V) lip synchronization, which distills a 14B audio-conditioned bidirectional
video diffusion teacher into causal students. At inference, the students generate each chunk in only two denoising steps without inference-time CFG, enabling real-time lip synchronization. A lip-sync-specific teacher-trajectory analysis reveals a CFG fidelity-sync tradeoff: no-CFG predictions favor reference fidelity, whereas CFG-guided predictions favor synchronization within a mid-trajectory band. Lip Forcing translates this finding into three analysis-derived components: Sync-Window DMD, a two-step inference schedule, and a SyncNet-based reward. We validate Lip Forcing at two student scales, both distilled from the 14B teacher. The 1.3B student crosses into real-time streaming at 31 FPS, $17.6\times$ faster than its same-scale bidirectional model. The 14B student, the largest
diffusion model reported for V2V lip synchronization, runs $39.8\times$ faster than its teacher at comparable reference fidelity. Time-to-first-frame is sub-millisecond at both scales, far below every
diffusion baseline.
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All authors Paul Hyunbin Cho, Jinhyuk Jang, SeokYoung Lee, Joungbin Lee, Siyoon Jin, Heeseong Shin, Jung Yi, Yunjin Park, Chulmin Park, Seungryong Kim
Computer Vision
Data Journalist Agent: Transforming Data into Verifiable Multimodal Stories
Kevin Qinghong Lin, Batu EI, Yuhong Shi et al. (+5 more)
Data tells stories that shape society; the data journalist's job is to turn raw information into stories non-experts can trust. A high-quality news feature takes a newsroom team weeks: hunting for context, running statistics, choosing an angle, and designing visuals. Recent
agents handle individual steps well: data-science
agents close the analysis loop, while design
agents synthesize beautiful websites. But can an
agent serve as a data journalist end to end? We introduce Data Journalist
Agent (Data2Story), a multi-
agent framework that orchestrates specialized roles into a single virtual newsroom. Data2Story contributes two innovations. (i) Claims are evidence-grounded: an Inspector links every number, angle, and asset back to data, code, or an external reference. (ii) Articles are multimodally generative: rather than defaulting to plain text and static charts, Data2Story reasons about what readers will want to see, then deploys
multimodal tools, such as interactive maps for geography and audio for music. We evaluate Data2Story on 18 articles, each paired with the originally published expert piece, along four axes: (a) human-
agent angle coverage; (b) rubric evaluation with 53 participants across five dimensions; (c) computer-use
agents as judges, a cost-saving proxy for how readers navigate interactive articles; and (d) verifiability, where a coding verifier re-executes statements against the data and checks claims against references. Data2Story produces competitive, evidence-traceable multimedia stories, with particular strength in transparency and auditability. Human articles retain an edge in editorial angle, creative design, and presentation. We position Data2Story as a collaborator for journalists, enabling more evidence-based, transparent, and verifiable reporting. Code and demos are available at https://data2story.github.io.
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All authors Kevin Qinghong Lin, Batu EI, Yuhong Shi, Pan Lu, Juil Sock, Djordje Padejski, Philip Torr, James Zou
Computer Vision
Mean Flow Distillation: Robust and Stable Distillation for Flow Matching Models
An Zhao, Shengyuan Zhang, Zhongjian Sun et al. (+5 more)
Flow Matching models have demonstrated strong performance across a wide range of generative tasks. However, their reliance on ODE-based iterative sampling incurs substantial computational overhead in inference, which limits their applicability in real-time scenes. While
distillation is a promising solution, existing approaches largely borrow from
diffusion-based score matching, often failing to exploit the intrinsic
geometric structure of flows and suffering from training instability, high variance, and degraded generation quality. In this paper, we propose Mean Flow
Distillation (MFD), a novel
distillation framework tailored for flow matching models. We theoretically demonstrate that MFD acts as a temporal low-pass filter, effectively suppressing the high-frequency optimization noise inherent in variational score
distillation (VSD) while ensuring global trajectory consistency. We further prove the Mean Flow Matching
Theorem, establishing that matching expected average velocities is sufficient for strict distribution
alignment. Empirically, on challenging tasks of high-dimensional
manifolds including 4D occupancy
forecasting and text-to-image generation, MFD achieves state-of-the-art performance, enabling high-fidelity single-step generation.
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All authors An Zhao, Shengyuan Zhang, Zhongjian Sun, Yixiang Zhou, Zejian Li, Ling Yang, Tianrun Chen, Lingyun Sun
Computer Vision
P3D-Bench: Benchmarking MLLMs for Parametric 3D Generation and Structural Reasoning
Yikang Yang, Zhanpeng Hu, Youtian Lin et al. (+5 more)
Multimodal large
language models can write code to produce complex programs as well as use programs to do 3D modeling, which opens up a new avenue for 3D generation powered by their priors, world knowledge and
reasoning. Yet existing
benchmarks rarely evaluate 3D modeling through code. Such modeling demands more than runnable code: from a text or visual specification, a model must generate a parametric 3D program that is geometrically precise, semantically aligned and assembly-consistent. We introduce P3D-Bench, a
benchmark for parametric 3D generation. Unlike a 3D mesh, a parametric 3D program exposes explicit dimensions, construction operations and part relations, revealing whether a model recovers a design's structure, not just its appearance. Under a unified protocol, P3D-Bench covers three task families (Text-to-3D, Image-to-3D and Assembly-3D) and scores each output for executability,
geometric fidelity,
topology, text-grounded constraints, multiview semantic
alignment and part-level structure. We construct P3D-
Dataset, comprising 400 text cases, 400 image cases, and 203 annotated assemblies. Our evaluation on 100 cases from each task family yields three key findings. First, multi-part generation is substantially more challenging than single-part modeling, with models struggling to compose individual parts into a coherent structure. Second, models can often recover the global shape and semantic identity of the target object, yet fail to reproduce the precise parametric geometry specified by the input. Third, part-level modeling remains weak on assemblies, where models recover neither the geometry of each part nor the right number of parts. These results position P3D-Bench as a
benchmark for evaluating precise parametric geometry and part-level structure in parametric 3D generation.
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All authors Yikang Yang, Zhanpeng Hu, Youtian Lin, Mengqi Zhou, Jingxi Xu, Feihu Zhang, Jiaheng Liu, Yao Yao
Computer Vision
MOFA-VTON: More Fashion Possibilities with Fine-Grained Adaptations in Virtual Try-On
Xiaoyu Han, Chenyang Wang, Jing Wang et al. (+3 more)
Virtual try-on aims to fit an in-shop clothing image onto a specific human body. An optimal virtual try-on method should provide diverse and flexible dressing options, accurately reflecting the varied wearing styles encountered in real-life scenarios, tailored to individual preferences and fashion aspirations. However, current methods predominantly perform a direct replacement of the original clothing with the target clothing, following the same dressing pattern. This limited control over clothing adaptation may result in fixed and monotonous try-on outputs. To delve into More Fashion Possibilities with Fine-Grained Adaptations in Virtual Try-On, we propose a novel virtual try-on method, termed MOFA-VTON, which allows adjustment for clothing adaptations in try-on results through simple sketches by users. Specifically, we first design a mask construction strategy that transforms user-drawn curve sketches into a dual-region mask, replacing the traditional clothing-agnostic mask and providing fine-grained layout guidance for the subsequent generation process. Further, we propose layout adjustment blocks that utilize the cross-
attention mechanism to independently learn layout correspondences for upper and lower regions of the human body, refining the spatial arrangement of the two regions. With these implementations, our method enables flexible and fine-grained adaptations of target clothing, overcoming the constraints of a fixed layout. Extensive experiments on VITON-HD and DressCode
datasets demonstrate that our proposed MOFA-VTON outperforms previous state-of-the-art methods and provides more fashion possibilities for virtual try-on.
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All authors Xiaoyu Han, Chenyang Wang, Jing Wang, Shunyuan Zheng, Quanling Meng, Shengping Zhang
Computer Vision
UniPET: a universal network for high-quality PET image denoising across varied dose reduction factors
Zhiwen Yang, Yang Zhou, Haowei Chen et al. (+4 more)
Most existing deep learning-based PET image denoising methods assume a fixed and known dose reduction factor (DRF) for low-dose PET images. However, these methods encounter significant performance degradation when the DRF varies beyond the assumed one in practical applications. To address the challenge posed by varied DRFs, several preliminary studies focus on the task of universal PET image denoising, aiming to train a universal model over low-dose data across DRFs. Nonetheless, these vanilla universal models often struggle with misaligned styles present in different DRF data, leading to the style elimination issue with a significant over-smoothing effect. To deal with this issue, we innovatively introduce domain generalization to PET image denoising and propose a universal PET image denoising network (UniPET) to achieve high-quality PET image denoising across diverse DRFs. UniPET comprises two primary innovations: a style
alignment network (SAN) and a region-aware learning strategy (RALS). Specifically, SAN utilizes style
alignment techniques derived from domain generalization to align and recover styles across different DRFs, ensuring the model's generalizability across various DRFs while effectively preserving styles. Furthermore, to enhance style recovery, RALS distinguishes between flat and stylized regions, exclusively conducting
adversarial learning on the latter, thereby more effectively guiding the model's focus towards learning stylized regions. It is demonstrated that our proposed UniPET can adaptively recover different DRF styles and achieve high-quality PET image denoising across DRFs. Comprehensive experiments show that UniPET exhibits comparable performance to individual DRF-specific models at specific DRFs and realizes state-of-the-art performance in universal PET image denoising quantitatively, perceptually, and clinically.
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All authors Zhiwen Yang, Yang Zhou, Haowei Chen, Hui Zhang, Dan Zhao, Bingzheng Wei, Yan Xu