Computer Vision · Cross-listed from eess.SP
Modular Retrieval-Augmented Generalization for Human Action Recognition
Peng Liao, Shangsong Liang, Lin Chen et al. (+1 more)
Inertial Measurement Unit (IMU)-based Human Activity Recognition (HAR) aims to interpret and classify user behaviors from temporal motion signals. Recently, deep learning frameworks have advanced this task by learning and extracting discriminative spatiotemporal representations, significantly improving recognition performance. However, IMU-based HAR still faces several critical challenges, particularly limited training samples and static knowledge utilization, both of which severely hinder its large-scale deployment. In this paper, we introduce MoRA, the first
Retrieval-Augmented Module specifically designed for motion series. It can be flexibly integrated into any existing HAR model, enhancing recognition performance while maintaining inference efficiency. To address issues such as information redundancy in
retrieval results and rigid fusion strategies, we propose an uncertainty-adaptive fusion unit within MoRA. This unit leverages previous physical knowledge from IMU signals to dynamically adjust the fusion strategy between original outputs and retrieved information, enabling more robust recognition. Extensive experiments on ten real-world
datasets demonstrate that MoRA significantly improves the performance of existing IMU-based HAR models, consistently delivering stable and effective gains. The source code of MoRA is available at: https://github.com/liavonpenn/mora.
Read the full abstract ▾
All authors Peng Liao, Shangsong Liang, Lin Chen, Peijia Zheng
Computer Vision
Robustness of Transformer-Based Fluence Map Prediction Under Clinically Realistic Perturbations
Ujunwa Mgboh, Rafi Ibn Sultan, Joshua Kim et al. (+2 more)
Learning-based fluence map prediction offers a fast alternative to iterative inverse planning in intensity-modulated radiation therapy (IMRT), but its robustness under realistic distribution shifts remains unclear. We study a two-stage
transformer pipeline that maps anatomy (CT and contours) to dose and then to beamlet fluence maps. We compare fluence-stage
transformer backbones with hierarchical, global, and hybrid
attention, trained with a physics-informed loss enforcing energy consistency. Robustness is evaluated under
geometric perturbations, radiometric noise, reduced training data, and domain shifts using a prostate IMRT
dataset, with additional evaluation of the dose stage on public
datasets. Results show smooth degradation under moderate perturbations but sharp failures under severe rotations and noise. Hierarchical
transformers (e.g., SwinUNETR) exhibit slower growth in upper-quartile energy error, indicating improved robustness. We further show that SSIM alone fails to capture clinically relevant errors, highlighting the need for physics-informed evaluation.
Read the full abstract ▾
All authors Ujunwa Mgboh, Rafi Ibn Sultan, Joshua Kim, Kundan Thind, Dongxiao Zhu
Computer Vision
A Light Weight Multi-Features-View Convolution Neural Network For Plant Disease Identification
Muhammad Kaleem Ullah Khan
Agriculture is a key sector of the economies of developing countries. It serves as a primary source of income and employment for rural populations. However, each year, a large portion of crops is wasted because of pests and diseases. Well-timed prediction of plant diseases is crucial to sustainable, high-quality agricultural production. Detection of plant diseases through conventional methods is both labour-intensive and time-consuming. Researchers have developed image classification based automated techniques for this purpose. Most accurate methods are based on deep convolutional neural networks, which are computationally intensive, with many layers and millions of trainable parameters. In resource-constrained settings, especially in rural areas, it is difficult to deploy deep convolutional neural network models for efficient plant disease identification. To address these issues, an efficient and light-weight Multi-View Convolutional Neural Network is proposed. These additional features aid the proposed model to identify the plant diseases accurately and efficiently with less number of parameters. The proposed model is tested on a
benchmark Plantvillage
dataset and achieves an improvement of $ 2.9\%$ in classification accuracy compared to the baseline convolutional neural network model, which was trained only on Red, Green, and Blue (RGB) plant images. Compared with state-of-the-art deep convolutional neural network models, the proposed model is less computationally expensive and achieves comparable accuracy for plant disease identification on the PlantVillage
dataset.
Read the full abstract ▾
Computer Vision
Validation of Whole-Slide Foundation Models for Image Retrieval in TCGA Data
Tianhao Lei, Parsa Esmaeilkhani, Saghir Alfasly et al. (+5 more)
Foundation models are reshaping computational histopathology, yet their value for whole-slide image
retrieval relative to strong patch-based and supervised aggregation baselines remains unclear. We benchmarked ten pipelines on 9,387 diagnostic slides spanning 17 organs and 60 diagnoses from The Cancer Genome Atlas (TCGA) using patient-level leave-one-patient-out evaluation. Methods included four pre-trained slide foundation models, a supervised
attention-based multiple instance learning (ABMIL) aggregator on patch
embeddings, and patch-level
retrieval across five sampling densities. Performance varied more across organs and diagnoses than across architectures. Although the slide foundation model TITAN achieved the strongest overall results, its advantage was modest; ABMIL and patch-based methods reached comparable Top-1 and Top-3 accuracy, with no model consistently dominant. Morphologically distinctive entities approached ceiling performance, while rare, heterogeneous, and closely related subtypes remained challenging. Misclassifications aligned with organs exhibiting known inter-observer variability, suggesting an intrinsic ceiling for morphology-only
retrieval. Performance was driven primarily by patch-level feature representations, with limited benefit from slide-level aggregation, indicating aggregation may be unnecessary in many settings. These findings argue against a universally optimal architecture and instead support organ-resolved benchmarking, diagnosis-aware or ensemble strategies, stronger feature representations, and
multimodal retrieval frameworks. Notably, even the best model achieved only $\approx 68\% \pm 21\%$
retrieval accuracy on TCGA, and some subtypes showed $0\%$ accuracy across all methods, highlighting fundamental limitations of morphology-based representations and the need for substantial progress before reliable clinical deployment.
Read the full abstract ▾
All authors Tianhao Lei, Parsa Esmaeilkhani, Saghir Alfasly, Wataru Uegami, Judy C. Boughey, Matthew P. Goetz, Krishna R. Kalari, H.R. Tizhoosh
Computer Vision
RA-CMF: Region-Adaptive Conditional MeanFlow for CT Image Reconstruction
Md Shifatul Ahsan Apurba, Md Selim and Jin Chen
The use of CT imaging is important for screening, diagnosis, therapy planning, and prognosis of lung cancers. Unfortunately, due to differences in imaging protocols and scanner models, CT images acquired by different means may show large differences in noise statistics, contrast, and texture. In this study, we develop a novel conditional MeanFlow pipeline for CT image reconstruction. We introduce a conditional MeanFlow network that models the reconstruction trajectory by predicting image-conditioned flow fields given intermediate image states. The image reconstruction network is trained with a MeanFlow consistency loss along with the image reconstruction loss. In order to provide a spatially adaptive refinement process, we integrate a regional reinforcement learning-driven policy network into our approach. The policy network receives information about the MeanFlow rollouts and provides predictions in terms of tile-wise refinement budgets, stopping criteria, and total budget allocation of refinement processes. Our policy network is trained through reinforcement learning in a policy gradient framework, where the goal of the training reward is to maximize reconstruction quality while minimizing unnecessary computations and avoiding instabilities. In this way, our approach combines conditional flow-based reconstruction with reinforcement learning-based spatial reconstruction control. Our results show high accuracy in the tumor ROI, with the average radiomic feature CCC being $0.93 \pm 0.09$, an average PSNR of $31.94 \pm 2.64$, and average SSIM of $0.97 \pm 0.03$. Moreover, there is an improvement in the overall quality of images, with an average PSNR of $34.23 \pm 1.71$ and average SSIM of $0.95 \pm 0.01$.
Read the full abstract ▾
Computer Vision
LatentDiff: Scaling Semantic Dataset Comparison to Millions of Images
James Flora, Kowshik Thopalli, Akshay R. Kulkarni et al. (+2 more)
We present LatentDiff, a scalable framework for semantic
dataset comparison that operates directly in the latent space of pretrained
vision encoders. By combining sparse autoencoder-based divergence testing with density ratio estimation, LatentDiff identifies interpretable semantic differences between
datasets at a fraction of the computational cost of caption-based alternatives. We also introduce Noisy-Diff, a
benchmark capturing realistic sparse distribution shifts that cause existing methods to struggle. Experiments demonstrate that LatentDiff achieves superior accuracy while remaining robust to settings where an extremely small fraction of images (from 5% to <1% ) differ semantically.
Read the full abstract ▾
All authors James Flora, Kowshik Thopalli, Akshay R. Kulkarni, Weng-Keen Wong, Shusen Liu
Computer Vision · Cross-listed from eess.SP
SPAT: A Semantic Port-Aware Adaptive-Rate Transmission Protocol for Semantic Communication
Yunhao Wang, Shuai Ma, Bin Shen et al. (+4 more)
With the
evolution of 6G, semantic communication has emerged as a promising paradigm by prioritizing the delivery of task-relevant meaning over strict bit-level correctness. However, existing transport mechanisms still rely on explicit port headers and bit-level validation, making them vulnerable to header corruption and the resulting packet loss. To address this issue, this paper proposes a Semantic Port-Aware Adaptive-Rate Transmission Protocol (SPAT) for semantic communication. The proposed framework jointly embeds source and destination port information into semantic representations, thereby reducing dependence on explicit port headers while enabling robust port-aware transmission. Furthermore, a differentiated semantic processing mechanism is developed for uplink and downlink scenarios, where port identification is introduced for uplink service recognition and destination-aware conditional gating is designed for downlink selective decoding. In addition, an adaptive-rate controller is incorporated to dynamically adjust the number of transmitted semantic channels according to channel conditions and feature importance, thereby improving both robustness and transmission efficiency. Experimental results on the AFHQ and ImageNet-10
datasets, together with real-world experimental measurements, demonstrate that SPAT consistently outperforms TCP, UDP, and SITP in reconstruction quality across different SNRs while maintaining low-latency transmission.
Read the full abstract ▾
All authors Yunhao Wang, Shuai Ma, Bin Shen, Shouhan Shi, Youlong Wu, Guangming Shi, Xiang Cheng
Computer Vision
When Less Is More: Simplicity Beats Complexity for Physics-Constrained InSAR Phase Unwrapping
Prabhjot Singh and Manmeet Singh
Operational phase unwrapping is the primary computational bottleneck in InSAR-based volcanic and seismic monitoring. We challenge the industry trend of adopting high-complexity computer
vision architectures, such as
attention mechanisms, without validating their suitability for physics-constrained geophysical regression. We present the first large-scale architectural ablation study on a global LiCSAR
benchmark (20 frames, 39,724 patches, 651M pixels). Our results reveal a significant "complexity penalty": a vanilla U-Net (7.76M parameters) achieves $R^2=0.834$ and RMSE $= 1.01$ cm, outperforming 11.37M-parameter
attention-based models by 34% in $R^2$ and 51% in RMSE. Power Spectral Density (PSD) analysis provides the physical justification: while
attention excels at capturing sharp semantic edges in natural images, it injects unphysical high-frequency artifacts ($>0.3$ cycles/pixel) into geophysical fields, violating the fundamental smoothness constraints of elastic surface deformation. With a 2.92ms inference latency (a $2.5\times$ speedup), the vanilla U-Net is the only candidate to comfortably meet the sub-100ms requirement for operational early-warning systems. This work bridges the "publication-to-practice" gap by proving that convolutional locality outperforms modern complexity for smooth-field regression, advocating for physics-informed simplicity in ML4RS. Code available at https://github.com/prabhjotschugh/When-Less-is-More-InSAR-Phase-Unwrapping
Read the full abstract ▾
Computer Vision
Lifting Embodied World Models for Planning and Control
Alex N. Wang, Trevor Darrell, Pavel Izmailov et al. (+2 more)
World models of
embodied agents predict future observations conditioned on an action taken by the
agent. For complex embodiments, action spaces are high-dimensional and difficult to specify: for example, precisely controlling a human
agent requires specifying the motion of each joint. This makes the world model hard to control and expensive to plan with as search-based methods like CEM scale poorly with action dimensionality. To address this issue, we train a lightweight policy that maps high-level actions to sequences of low-level joint actions. Composing this policy with the frozen world model produces a lifted world model that predicts a sequence of future observations from a single high-level action. We instantiate this framework for a human-like embodiment, defining the high-level action space as a small set of 2D waypoints annotated on the current observation frame, each specifying a near-term goal position for a leaf joint (pelvis, head, hands). Waypoints are low-dimensional, visually interpretable, and easy to specify manually or to search over. We show that the lifted world model substantially outperforms searching directly in low-level joint space ($3.8\times$ lower mean joint error to the goal pose), while remaining more compute-efficient and generalizing to environments unseen by the policy.
Read the full abstract ▾
All authors Alex N. Wang, Trevor Darrell, Pavel Izmailov, Yutong Bai, Amir Bar
Computer Vision
Why Domain Matters: A Preliminary Study of Domain Effects in Underwater Object Detection
Melanie Wille, Dimity Miller, Tobias Fischer et al. (+1 more)
Domain shift, where deviations between training and deployment data distributions degrade model performance, is a key challenge in
underwater environments. Existing
benchmarks testing performance for
underwater domain shift simulate variability through synthetic style transfer. This fails to capture intrinsic scene factors such as visibility, illumination, scene composition, or acquisition factors, limiting analysis of real-world effects. We propose a labeling framework that defines
underwater domains using measurable image, scene, and acquisition characteristics. Unlike prior
benchmarks, it captures physically meaningful factors, enabling semantically consistent image grouping and supporting domain-specific evaluation of detection performance including failure analysis. We validate this on public
datasets, showing systematic variations across domain factors and revealing hidden failure modes.
Read the full abstract ▾
All authors Melanie Wille, Dimity Miller, Tobias Fischer, Scarlett Raine
Computer Vision
A Data-Centric Framework for Intraoperative Fluorescence Lifetime Imaging for Glioma Surgical Guidance
Silvia Noble Anbunesan, Mohamed Abul Hassan, Jinyi Qi et al. (+4 more)
Accurate intraoperative assessment of glioma infiltration is essential for maximizing tumor resection while preserving functional
brain tissue. Fluorescence lifetime imaging (FLIm) offers real-time, label-free biochemical contrast, but its clinical utility is challenged by biological heterogeneity, class imbalance, and variability in histopathological labeling. We present a data-centric AI (DC-AI) framework that integrates confident learning (CL), class refinement, and targeted label evaluation to develop a robust multi-class FLIm classifier for glioblastoma (GBM) resection margins. FLIm data were collected from 192 tissue margins across 31 newly diagnosed IDH-wildtype GBM patients and initially labeled into seven tumor cellularity classes by an expert neuropathologist. CL was applied to quantify FLIm point-level confidence, identify label inconsistencies, and guide iterative class merging into a three-class scheme ("low", "moderate", "high"). The resulting high-fidelity
dataset enabled training a model that achieved 96% accuracy in the three-class task. SHAP analysis revealed class-specific FLIm feature importance, highlighting distinct optical signatures across the infiltration spectrum. Targeted FLIm analysis further identified biological (e.g., gray matter composition) and acquisition-related (e.g., blood contamination) contributors to low-confidence predictions. Blinded re-evaluation of margins flagged by CL demonstrated intra-pathologist variability, underscoring the value of selective relabeling rather than exhaustive review. Together, these findings demonstrate that a DC-AI framework can systematically improve data reliability, enhance model robustness, and refine biological interpretation of FLIm signals, supporting the development of clinically actionable optical tools for real-time glioma margin assessment.
Read the full abstract ▾
All authors Silvia Noble Anbunesan, Mohamed Abul Hassan, Jinyi Qi, Lisanne Kraft, Han Sung Lee, Orin Bloch, Laura Marcu
Computer Vision
MixerCA: An Efficient and Accurate Model for High-Performance Hyperspectral Image Classification
Mohammed Q. Alkhatib and Ali Jamali
Over the past decade, hyperspectral image (HSI) classification has drawn considerable interest due to HSIs' ability to effectively distinguish terrestrial objects by capturing detailed, continuous spectral information. The strong performance of recent deep learning techniques in tasks like image classification and semantic
segmentation has led to their growing use in HSI classification, due to their ability to capture complex spatial and spectral features more effectively than traditional methods. This paper presents MixerCA, a novel lightweight model for HSI classification that leverages depthwise convolution and a self-
attention mechanism. MixerCA integrates depth-wise convolutions,
token and channel mixing, and coordinate
attention into a unified structure to decouple spatial and channel interactions, maintain consistent resolution throughout the network, and directly process HSI patches. Extensive experiments on four hyperspectral
benchmark datasets reveal MixerCA's clear advantages over several competing
algorithms, including 2D-CNN, 3D-CNN, Tri-CNN, HybridSN, ViT, and Swin
Transformer. The source code is publicly available at https://github.com/mqalkhatib/MixerCA.
Read the full abstract ▾
Computer Vision
Sample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Data
Emre Ard{\i}ç and Yakup Genç
Federated learning is a machine learning paradigm in which multiple devices collaboratively train a model under the supervision of a central server while ensuring data
privacy. However, its performance is often hindered by redundant, malicious, or abnormal samples, leading to model degradation and inefficiency. To overcome these issues, we propose novel sample selection methods for image classification, employing a multitask autoencoder to estimate sample contributions through loss and feature analysis. Our approach incorporates unsupervised outlier detection, using one-class support vector machine (OCSVM), isolation forest (IF), and adaptive loss threshold (AT) methods managed by a central server to filter noisy samples on clients. We also propose a multi-class deep support vector data description (SVDD) loss controlled by a central server to enhance feature-based sample selection. We validate our methods on CIFAR10 and MNIST
datasets across varying numbers of clients, non-IID distributions, and noise levels up to 40%. The results show significant accuracy improvements with loss-based sample selection, achieving gains of up to 7.02% on CIFAR10 with OCSVM and 1.83% on MNIST with AT. Additionally, our
federated SVDD loss further improves feature-based sample selection, yielding accuracy gains of up to 0.99% on CIFAR10 with OCSVM. These results show the effectiveness of our methods in improving model accuracy across various client counts and noise conditions.
Read the full abstract ▾
Computer Vision
FruitProM-V2: Robust Probabilistic Maturity Estimation and Detection of Fruits and Vegetables
Rahul Harsha Cheppally, Sidharth Rai, Sudan Baral et al. (+2 more)
Accurate fruit maturity identification is essential for determining harvest timing, as incorrect assessment directly affects yield and post-harvest quality. Although ripening is a continuous biological process,
vision-based maturity estimation is typically formulated as a multi-class classification task, which imposes sharp boundaries between visually similar stages. To examine this limitation, we perform an annotation reliability study with two independent annotators on a held-out tomato
dataset and observe disagreement concentrated near adjacent maturity stages. Motivated by this observation, we model maturity as a latent continuous variable and predict it probabilistically using a distributional detection head, converting the distribution into class probabilities through the cumulative distribution function (CDF). The proposed formulation maintains comparable performance to a standard detector under clean labels while better representing uncertainty. Furthermore, when controlled label noise is introduced during training, the probabilistic model demonstrates improved robustness relative to the baseline, indicating that explicitly modeling maturity uncertainty leads to more reliable visual maturity estimation.
Read the full abstract ▾
All authors Rahul Harsha Cheppally, Sidharth Rai, Sudan Baral, Benjamin Vail, Ajay Sharda
Computer Vision
RADIO-ViPE: Online Tightly Coupled Multi-Modal Fusion for Open-Vocabulary Semantic SLAM in Dynamic Environments
Zaid Nasser, Mikhail Iumanov, Tianhao Li et al. (+3 more)
We present
RADIO-ViPE (Reduce All Domains Into One –
Video Pose Engine), an online semantic SLAM system that enables geometry-aware open-vocabulary grounding, associating arbitrary natural
language queries with localized 3D regions and objects in dynamic environments. Unlike existing approaches that require calibrated, posed RGB-D input,
RADIO-ViPE operates directly on raw
monocular RGB
video streams, requiring no prior camera intrinsics, depth sensors, or pose initialization. The system tightly couples multi-modal
embeddings – spanning
vision and
language – derived from agglomerative foundation models (e.g.,
RADIO) with
geometric scene information. This coupling takes place in initialization, optimization and factor graph connections to improve the consistency of the map from multiple modalities. The optimization is wrapped within adaptive robust kernels, designed to handle both actively moving objects and
agent-displaced scene elements (e.g., furniture rearranged during ego-centric session). Experiments demonstrate that
RADIO-ViPE achieves state-of-the-art results on the dynamic TUM-RGBD
benchmark while maintaining competitive performance against offline open-vocabulary methods that rely on calibrated data and static scene assumptions.
RADIO-ViPE bridges a critical gap in real-world deployment, enabling robust open-vocabulary semantic grounding for autonomous robotics and unconstrained in-the-wild
video streams. Project page: https://be2rlab.github.io/
radio_vipe
Read the full abstract ▾
All authors Zaid Nasser, Mikhail Iumanov, Tianhao Li, Maxim Popov, Jaafar Mahmoud, Sergey Kolyubin
Computer Vision
Evaluating the Alignment Between GeoAI Explanations and Domain Knowledge in Satellite-Based Flood Mapping
Hyunho Lee and Wenwen Li
The increasing number of satellites has improved the temporal resolution of Earth observation, making satellite-based flood mapping a promising approach for operational flood monitoring. Deep learning-based approaches for flood mapping using satellite imagery, an important application within Geospatial Artificial Intelligence (GeoAI), have shown improved predictive performance by learning complex spatial and spectral patterns from large volumes of remote sensing data. However, the opaque decision-making processes of deep learning models remain a major barrier to their integration into critical scientific and operational workflows. This highlights the need for a systematic assessment of whether model explanations align with established domain knowledge in remote sensing. To address this research gap, this study introduces the ADAGE (
Alignment between Domain Knowledge and GeoAI Explanation Evaluation) framework. The proposed framework is designed to systematically evaluate how well explanations of deep learning models align with established remote sensing knowledge, particularly regarding the distinctive spectral properties of the Earth's surface. The ADAGE framework employs Channel-Group SHAP (SHapley Additive exPlanations) method to estimate the contributions of grouped input channels to pixel-level predictions. Experiments on two satellite-based flood mapping tasks demonstrate that the ADAGE framework can (1) quantitatively assess the
alignment between model explanations and reference explanations derived from domain knowledge, and (2) help domain experts identify misaligned explanations through the proposed
alignment scores. This study contributes to bridging the gap between explainability and domain knowledge in GeoAI for Earth observation, enhancing the applicability of GeoAI models in scientific and operational workflows.
Read the full abstract ▾
Computer Vision
Report of the 5th PVUW Challenge: Towards More Diverse Modalities in Pixel-Level Understanding
Chang Liu, Henghui Ding, Nikhila Ravi et al. (+40 more)
This report summarizes the objectives,
datasets, and top-performing methodologies of the 2026 Pixel-level
Video Understanding in the Wild (PVUW) Challenge, hosted at CVPR 2026, which evaluates state-of-the-art models under highly unconstrained conditions. To provide a comprehensive assessment, the 2026 edition features three specialized tracks: the MOSE track for tracking objects within densely cluttered and severely occluded scenarios; the MeViS-Text track for localizing targets via motion-focused linguistic expressions; and the newly inaugurated MeViS-Audio track, which pioneers acoustic-driven object
segmentation. By introducing previously unreleased challenging data and analyzing the cutting-edge,
multimodal solutions submitted by participants, this report highlights the community's latest technical advancements and charts promising future directions for robust
video scene comprehension.
Read the full abstract ▾
All authors Chang Liu, Henghui Ding, Nikhila Ravi, Yunchao Wei, Shuting He, Song Bai, Philip Torr, Leilei Cao, Jinrong Zhang, Deshui Miao, Xusheng He, Dengxian Gong, Zhiyu Wang, Mingqi Gao, Jihwan Hong, Canyang Wu, Weili Guan, Jianlong Wu, Liqiang Nie, Xingsen Huang, Yameng Gu, Xiaogang Yu, Xin Li, Ming-Hsuan Yang, Sijie Li, Jungong Han, Quanzhu Niu, Shihao Chen, Yuanzheng Wu, Yikang Zhou, Tao Zhang, Haobo Yuan, Lu Qi, Shunping Ji, Chao Yang, Chao Tian, Guoqing Zhu, Kai Yang, Zhifan Mo, Haijun Zhang, Xudong Kang, Shutao Li, Jaeyoung Do
Computer Vision
Generalized Disguise Makeup Presentation Attack Detection Using an Attention-Guided Patch-Based Framework
Fateme Taraghi, Atefe Aghaei and Mohsen Ebrahimi Moghaddam
Despite significant advances in facial recognition systems, they remain vulnerable to face presentation attacks. Among them, disguise makeup attacks are particularly challenging, as they use advanced cosmetics, prosthetic components, and artificial materials to realistically alter facial appearance, often making detection difficult even for humans. Despite their importance, this problem remains underexplored, and publicly available
datasets are limited. To address this, we propose a generalized disguise makeup presentation attack detection framework. The method adopts a two-phase design in which a style-invariant full-face model, trained with metric learning and enhanced by a whitening transformation, extracts region
attention scores via Grad-CAM. These scores guide a patch-based phase that performs localized analysis using region-specific subnetworks trained with metric learning for fine-grained discrimination. We also construct a new, diverse
dataset of live and disguise makeup faces collected under real-world conditions, covering variations in subjects, environments, and disguise materials. Experimental results demonstrate strong generalization across both the collected
dataset and SIW-Mv2, achieving 8.97% ACER and 9.76% EER on the collected
dataset, and 0% ACER on Obfuscation and Impersonation and 1.34% on Cosmetics attacks of SIW-Mv2. The proposed method consistently outperforms prior works while maintaining robust performance across other spoof types.
Read the full abstract ▾
Computer Vision
Robust Deepfake Detection: Mitigating Spatial Attention Drift via Calibrated Complementary Ensembles
Minh-Khoa Le-Phan, Minh-Hoang Le, Trong-Le Do et al. (+1 more)
Current deepfake detection models achieve state-of-the-art performance on pristine academic
datasets but suffer severe spatial
attention drift under real-world compound degradations, such as blurring and severe lossy compression. To address this vulnerability, we propose a foundation-driven forensic framework that integrates an extreme compound degradation engine with a structurally constrained, multi-stream architecture. During training, our degradation pipeline systematically destroys high-frequency artifacts, optimizing the DINOv2-
Giant backbone to extract invariant
geometric and semantic priors. We then process images through three specialized pathways: a Global Texture stream, a Localized Facial stream, and a Hybrid Semantic Fusion stream incorporating CLIP. Through analyzing spatial attribution via Score-CAM and feature stability using Cosine Similarity, we quantitatively demonstrate that these streams extract non-redundant, complementary feature representations and stabilize
attention entropy. By aggregating these predictions via a calibrated, discretized voting mechanism, our ensemble successfully suppresses background
attention drift while acting as a robust
geometric anchor. Our approach yields highly stable zero-shot generalization, achieving Fourth Place in the NTIRE 2026 Robust Deepfake Detection Challenge at CVPR. Code is available at https://github.com/khoalephanminh/ntire26-deepfake-challenge.
Read the full abstract ▾
All authors Minh-Khoa Le-Phan, Minh-Hoang Le, Trong-Le Do, Minh-Triet Tran
Computer Vision
No Pedestrian Left Behind: Real-Time Detection and Tracking of Vulnerable Road Users for Adaptive Traffic Signal Control
Anas Gamal Aly and Hala ElAarag
Current pedestrian crossing signals operate on fixed timing without adjustment to pedestrian behavior, which can leave vulnerable road users (VRUs) such as the elderly, disabled, or distracted pedestrians stranded when the light changes. We introduce No Pedestrian Left Behind (NPLB), a real-time adaptive traffic signal system that monitors VRUs in crosswalks and automatically extends signal timing when needed. We evaluated five state-of-the-art object detection models on the BGVP
dataset, with YOLOv12 achieving the highest mean Average Precision at 50% (mAP@0.5) of 0.756. NPLB integrates our fine-tuned YOLOv12 with ByteTrack multi-object tracking and an adaptive controller that extends pedestrian phases when remaining time falls below a critical threshold. Through 10,000 Monte Carlo simulations, we demonstrate that NPLB improves VRU safety by 71.4%, reducing stranding rates from 9.10% to 2.60%, while requiring signal extensions in only 12.1% of crossing cycles.
Read the full abstract ▾
Computer Vision · Cross-listed from quant-ph
QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understanding
Shuxiang Cao, Zijian Zhang, Abhishek Agarwal et al. (+29 more)
Quantum computing calibration depends on interpreting experimental data, and calibration plots provide the most universal human-readable representation for this task, yet no systematic evaluation exists of how well
vision-
language models (VLMs) interpret them. We introduce QCalEval, the first VLM
benchmark for
quantum calibration plots: 243 samples across 87 scenario types from 22 experiment families, spanning superconducting
qubits and neutral atoms, evaluated on six question types in both zero-shot and in-context learning settings. The best general-purpose zero-shot model reaches a mean score of 72.3, and many open-weight models degrade under multi-image in-context learning, whereas frontier closed models improve substantially. A supervised fine-tuning ablation at the 9-billion-parameter scale shows that SFT improves zero-shot performance but cannot close the
multimodal in-context learning gap. As a reference case study, we release NVIDIA Ising Calibration 1, an open-weight model based on Qwen3.5-35B-A3B that reaches 74.7 zero-shot average score.
Read the full abstract ▾
All authors Shuxiang Cao, Zijian Zhang, Abhishek Agarwal, Grace Bratrud, Niyaz R. Beysengulov, Daniel C. Cole, Alejandro Gómez Frieiro, Elena O. Glen, Hao Hsu, Gang Huang, Raymond Jow, Greshma Shaji, Tom Lubowe, Ligeng Zhu, Luis Mantilla Calderón, Nicola Pancotti, Joel Pendleton, Brandon Severin, Charles Etienne Staub, Sara Sussman, Antti Vepsäläinen, Neel Rajeshbhai Vora, Yilun Xu, Varinia Bernales, Daniel Bowring, Elica Kyoseva, Ivan Rungger, Giulia Semeghini, Sam Stanwyck, Timothy Costa, Alán Aspuru-Guzik, Krysta Svore
Computer Vision
SIEVES: Selective Prediction Generalizes through Visual Evidence Scoring
Hector G. Rodriguez and Marcus Rohrbach
Multimodal large
language models (MLLMs) achieve ever-stronger performance on visual-
language tasks. Even as traditional visual question answering (VQA)
benchmarks approach saturation, reliable deployment requires satisfying low error tolerances in real-world, out-of-distribution (OOD) scenarios. Precisely, selective prediction aims to improve coverage, i.e. the share of inputs the system answers, while adhering to a user-defined risk level. This is typically achieved by assigning a confidence score to each answer and abstaining on those that fall below a certain threshold. Existing selective prediction methods estimate implicit confidence scores, relying on model internal signals like logits or hidden representations, which are not available for frontier closed-sourced models. To enable reliable generalization in VQA, we require reasoner models to produce localized visual evidence while answering, and design a selector that explicitly learns to estimate the quality of the localization provided by the reasoner using only model inputs and outputs. We show that SIEVES (Selective Prediction through Visual Evidence Scoring) improves coverage by up to three times on challenging OOD
benchmarks (V* Bench, HR-Bench-8k, MME-RealWorld-Lite, VizWiz, and AdVQA), compared to non-grounding baselines. Beyond better generalization to OOD tasks, the design of the SIEVES selector enables transfer to proprietary reasoners without access to their weights or logits, such as o3 and Gemini-3-Pro, providing coverage boosts beyond those attributable to accuracy alone. We highlight that SIEVES generalizes across all tested OOD
benchmarks and reasoner models (Pixel-Reasoner, o3, and Gemini-3-Pro), without
benchmark- or reasoner-specific training or adaptation. Code is publicly available at https://github.com/hector-gr/SIEVES .
Read the full abstract ▾
Computer Vision
Mutual Forcing: Dual-Mode Self-Evolution for Fast Autoregressive Audio-Video Character Generation
Yupeng Zhou, Lianghua Huang, Zhifan Wu et al. (+7 more)
In this work, we propose Mutual Forcing, a framework for fast autoregressive audio-
video generation with long-horizon audio-
video synchronization. Our approach addresses two key challenges: joint audio-
video modeling and fast autoregressive generation. To ease joint audio-
video optimization, we adopt a two-stage training strategy: we first train uni-modal generators and then couple them into a unified audio-
video model for joint training on paired data. For streaming generation, we ask whether a native fast causal audio-
video model can be trained directly, instead of following existing streaming
distillation pipelines that typically train a bidirectional model first and then convert it into a causal generator through multiple
distillation stages. Our answer is Mutual Forcing, which builds directly on native autoregressive model and integrates few-step and multi-step generation within a single weight-shared model, enabling self-
distillation and improved training-inference consistency. The multi-step mode improves the few-step mode via self-
distillation, while the few-step mode generates historical context during training to improve training-inference consistency; because the two modes share parameters, these two effects reinforce each other within a single model. Compared with prior approaches such as Self-Forcing, Mutual Forcing removes the need for an additional bidirectional teacher model, supports more flexible training sequence lengths, reduces training overhead, and allows the model to improve directly from real paired data rather than a fixed teacher. Experiments show that Mutual Forcing matches or surpasses strong baselines that require around 50 sampling steps while using only 4 to 8 steps, demonstrating substantial advantages in both efficiency and quality. The project page is available at https://mutualforcing.github.io.
Read the full abstract ▾
All authors Yupeng Zhou, Lianghua Huang, Zhifan Wu, Jiabao Wang, Yupeng Shi, Biao Jiang, Daquan Zhou, Yu Liu, Ming-Ming Cheng, Qibin Hou
Computer Vision
Magnification-Invariant Image Classification via Domain Generalization and Stable Sparse Embedding Signatures
Ifeanyi Ezuma and Olusiji Medaiyese
Magnification shift is a major obstacle to robust histopathology classification, because models trained on one imaging scale often generalize poorly to another. Here, we evaluated this problem on the BreaKHis
dataset using a strict patient-disjoint leave-one-magnification-out protocol, comparing supervised baseline, baseline augmented with DCGAN-generated patches, and a gradient-reversal domain-general model designed to preserve discriminative information while suppressing magnification-specific variation. Across held-out magnifications, the domain-general model achieved the strongest overall discrimination and its clearest gain was observed when 200X was held out. By contrast, GAN augmentation produced inconsistent effects, improving some folds but degrading others, particularly at 400X. The domain-general model also yielded the lowest Brier score at 0.063 vs 0.089 at baseline. Sparse
embedding analysis further revealed that domain-general training reduced average signature size more than three-fold (306 versus 1,074 dimensions) while preserving equivalent predictive performance (AUC: 0.967 vs 0.965; F1: 0.930 vs 0.931). It also increased cross-fold signature reproducibility from near-zero Jaccard overlap in the baseline to 0.99 between the 100X and 200X folds. These findings show that calibrated, compact, and transferable representations can be learned without added architectural complexity, with clear implications for the reliable deployment of computational pathology models across heterogeneous acquisition settings.
Read the full abstract ▾
Computer Vision
Instruction-Evidence Contrastive Dual-Stream Decoding for Grounded Vision-Language Reasoning
Yashwant Pravinrao Bangde and Debaditya Roy
Vision-
Language Models (VLMs) exhibit strong performance in instruction following and open-ended
vision-
language reasoning, yet they frequently generate fluent outputs that are weakly grounded in visual evidence. Prior works have shown that instruction prompting further worsens this issue by amplifying
language priors, especially when the visual signal is uncertain or ambiguous. To address this challenge, we propose a decoding framework that explicitly balances linguistic informativeness and visual faithfulness during generation. Our method, Instruction-Evidence Contrastive Dual-Stream Decoding (IECD$^2$), maintains two parallel probability distribution of
tokens at each decoding step: an instruction-driven stream that promotes expressive and informative responses, and an evidence-driven stream that enforces strict grounding in the image. These two streams are adaptively fused using a symmetric KL-based contrastive gate, which suppresses
tokens favored by
language priors but unsupported by visual evidence, while preserving them when both distributions agree. We evaluate IECD$^2$ on multiple
datasets spanning various generative
vision-
language reasoning tasks such as captioning and visual question answering on multiple
datasets such as, POPE, MME, VQAv2, AMBER, and MSCOCO. IECD$^2$ demonstrates consistent improvements in task accuracy and
reasoning performance with substantial reduction in
hallucination compared to state-of-the-art decoding approaches.
Read the full abstract ▾