Computer Vision
Healthy Harvests: A Comparative Look at Guava Disease Classification Using InceptionV3
Samanta Ghosh, Shaila Afroz Anika, Umma Habiba Ahmed et al. (+3 more)
Guava fruits often suffer from many diseases. This can harm fruit quality and fruit crop yield. Early identification is important for minimizing damage and ensuring fruit health. This study focuses on 3 different categories for classifying diseases. These are Anthracnose, Fruit flies, and Healthy fruit. The data set used in this study is collected from Mendeley Data. This
dataset contains 473 original images of Guava. These images vary in size and format. The original
dataset was resized to 256x256 pixels with RGB color mode for better consistency. After this, the Data augmentation process is applied to improve the
dataset by generating variations of the original images. The augmented
dataset consists of 3784 images using advanced preprocessing techniques. Two deep learning models were implemented to classify the images. The InceptionV3 model is well known for its advanced framework. These apply multiple convolutional filters for obtaining different features effectively. On the other hand, the ResNet50 model helps to train deeper networks by using residual learning. The InceptionV3 model achieved the impressive accuracy of 98.15%, and ResNet50got 94.46% accuracy. Data mixing methods such as CutMix and MixUp were applied to enhance the model's robustness. The confusion
matrix was used to evaluate the overall model performance of both InceptionV3 and Resnet50. Additionally, SHAP analysis is used to improve interpretability, which helps to find the significant parts of the image for the model prediction. This study purposes to highlight how advanced models enhan
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All authors Samanta Ghosh, Shaila Afroz Anika, Umma Habiba Ahmed, B. M. Shahria Alam, Mohammad Tahmid Noor, Nishat Tasnim Niloy
Computer Vision
Towards Learning a Generalizable 3D Scene Representation from 2D Observations
Martin Gromniak, Jan-Gerrit Habekost, Sebastian Kamp et al. (+2 more)
We introduce a Generalizable Neural Radiance Field approach for predicting 3D workspace occupancy from
egocentric robot observations. Unlike prior methods operating in camera-centric coordinates, our model constructs occupancy representations in a global workspace frame, making it directly applicable to robotic manipulation. The model integrates flexible source views and generalizes to unseen object arrangements without scene-specific finetuning. We demonstrate the approach on a
humanoid robot and evaluate predicted geometry against 3D sensor ground truth. Trained on 40 real scenes, our model achieves 26mm reconstruction error, including occluded regions, validating its ability to infer complete 3D occupancy beyond traditional stereo
vision methods.
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All authors Martin Gromniak, Jan-Gerrit Habekost, Sebastian Kamp, Sven Magg, Stefan Wermter
Computer Vision
FastUSP: A Multi-Level Collaborative Acceleration Framework for Distributed Diffusion Model Inference
Guandong Li
Large-scale
diffusion models such as FLUX (12B parameters) and Stable
Diffusion 3 (8B parameters) require multi-GPU parallelism for efficient inference. Unified Sequence Parallelism (USP), which combines Ulysses and Ring
attention mechanisms, has emerged as the state-of-the-art approach for distributed
attention computation. However, existing USP implementations suffer from significant inefficiencies including excessive kernel launch overhead and suboptimal computation-communication scheduling. In this paper, we propose FastUSP, a multi-level optimization framework that integrates compile-level optimization (graph compilation with CUDA Graphs and computation-communication reordering), communication-level optimization (FP8 quantized collective communication), and operator-level optimization (pipelined Ring
attention with double buffering). We evaluate FastUSP on FLUX (12B) and Qwen-Image models across 2, 4, and 8 NVIDIA RTX 5090 GPUs. On FLUX, FastUSP achieves consistent \textbf{1.12$\times$–1.16$\times$} end-to-end speedup over baseline USP, with compile-level optimization contributing the dominant improvement. On Qwen-Image, FastUSP achieves \textbf{1.09$\times$} speedup on 2 GPUs; on 4–8 GPUs, we identify a PyTorch Inductor compatibility limitation with Ring
attention that prevents compile optimization, while baseline USP scales to 1.30$\times$–1.46$\times$ of 2-GPU performance. We further provide a detailed analysis of the performance characteristics of distributed
diffusion inference, revealing that kernel launch overhead – rather than communication latency – is the primary bottleneck on modern high-bandwidth GPU interconnects.
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Computer Vision
ResWorld: Temporal Residual World Model for End-to-End Autonomous Driving
Jinqing Zhang, Zehua Fu, Zelin Xu et al. (+3 more)
The comprehensive understanding capabilities of world models for driving scenarios have significantly improved the planning accuracy of end-to-end autonomous driving frameworks. However, the redundant modeling of static regions and the lack of deep interaction with trajectories hinder world models from exerting their full effectiveness. In this paper, we propose Temporal Residual World Model (TR-World), which focuses on dynamic object modeling. By calculating the temporal residuals of scene representations, the information of dynamic objects can be extracted without relying on detection and tracking. TR-World takes only temporal residuals as input, thus predicting the future spatial distribution of dynamic objects more precisely. By combining the prediction with the static object information contained in the current BEV features, accurate future BEV features can be obtained. Furthermore, we propose Future-Guided Trajectory Refinement (FGTR) module, which conducts interaction between prior trajectories (predicted from the current scene representation) and the future BEV features. This module can not only utilize future road conditions to refine trajectories, but also provides sparse spatial-temporal supervision on future BEV features to prevent world model collapse. Comprehensive experiments conducted on the nuScenes and NAVSIM
datasets demonstrate that our method, namely ResWorld, achieves state-of-the-art planning performance. The code is available at https://github.com/mengtan00/ResWorld.git.
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All authors Jinqing Zhang, Zehua Fu, Zelin Xu, Wenying Dai, Qingjie Liu, Yunhong Wang
Computer Vision
Chart Specification: Structural Representations for Incentivizing VLM Reasoning in Chart-to-Code Generation
Minggui He, Mingchen Dai, Jian Zhang et al. (+5 more)
Vision-
Language Models (VLMs) have shown promise in generating plotting code from chart images, yet achieving structural fidelity remains challenging. Existing approaches largely rely on supervised fine-tuning, encouraging surface-level
token imitation rather than faithful modeling of underlying chart structure, which often leads to hallucinated or semantically inconsistent outputs. We propose Chart Specification, a structured intermediate representation that shifts training from text imitation to semantically grounded supervision. Chart Specification filters syntactic noise to construct a structurally balanced training set and supports a Spec-Align Reward that provides fine-grained, verifiable feedback on structural correctness, enabling reinforcement learning to enforce consistent plotting logic. Experiments on three public
benchmarks show that our method consistently outperforms prior approaches. With only 3K training samples, we achieve strong data efficiency, surpassing leading baselines by up to 61.7% on complex
benchmarks, and scaling to 4K samples establishes new state-of-the-art results across all evaluated metrics. Overall, our results demonstrate that precise structural supervision offers an efficient pathway to high-fidelity chart-to-code generation. Code and
dataset are available at: https://github.com/Mighten/chart-specification-paper
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All authors Minggui He, Mingchen Dai, Jian Zhang, Yilun Liu, Shimin Tao, Pufan Zeng, Osamu Yoshie, Yuya Ieiri
Computer Vision
Stride-Net: Fairness-Aware Disentangled Representation Learning for Chest X-Ray Diagnosis
Darakshan Rashid, Raza Imam, Dwarikanath Mahapatra et al. (+1 more)
Deep neural networks for chest X-ray classification achieve strong average performance, yet often underperform for specific demographic subgroups, raising critical concerns about clinical safety and equity. Existing debiasing methods frequently yield inconsistent improvements across
datasets or attain
fairness by degrading overall diagnostic utility, treating
fairness as a post hoc constraint rather than a property of the learned representation. In this work, we propose Stride-Net (Sensitive Attribute Resilient Learning via Disentanglement and Learnable Masking with
Embedding Alignment), a
fairness-aware framework that learns disease-discriminative yet demographically invariant representations for chest X-ray analysis. Stride-Net operates at the patch level, using a learnable stride-based mask to select label-aligned image regions while suppressing sensitive attribute information through
adversarial confusion loss. To anchor representations in clinical semantics and discourage shortcut learning, we further enforce semantic
alignment between image features and BioBERT-based disease label
embeddings via Group Optimal Transport. We evaluate Stride-Net on the MIMIC-CXR and CheXpert
benchmarks across race and intersectional race-gender subgroups. Across architectures including ResNet and
Vision Transformers, Stride-Net consistently improves
fairness metrics while matching or exceeding baseline accuracy, achieving a more favorable accuracy-
fairness trade-off than prior debiasing approaches. Our code is available at https://github.com/Daraksh/
Fairness_StrideNet.
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All authors Darakshan Rashid, Raza Imam, Dwarikanath Mahapatra, Brejesh Lall
Computer Vision · Cross-listed from cs.HC
Viewpoint Recommendation for Point Cloud Labeling through Interaction Cost Modeling
Yu Zhang, Xinyi Zhao, Chongke Bi et al. (+1 more)
Semantic
segmentation of 3D point clouds is important for many applications, such as autonomous driving. To train semantic
segmentation models, labeled point cloud
segmentation datasets are essential. Meanwhile, point cloud labeling is time-consuming for annotators, which typically involves tuning the camera viewpoint and selecting points by lasso. To reduce the time cost of point cloud labeling, we propose a viewpoint recommendation approach to reduce annotators' labeling time costs. We adapt Fitts' law to model the time cost of lasso selection in point clouds. Using the modeled time cost, the viewpoint that minimizes the lasso selection time cost is recommended to the annotator. We build a data labeling system for semantic
segmentation of 3D point clouds that integrates our viewpoint recommendation approach. The system enables users to navigate to recommended viewpoints for efficient annotation. Through an ablation study, we observed that our approach effectively reduced the data labeling time cost. We also qualitatively compare our approach with previous viewpoint selection approaches on different
datasets.
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All authors Yu Zhang, Xinyi Zhao, Chongke Bi, Siming Chen
Computer Vision
Hyperspectral Smoke Segmentation via Mixture of Prototypes
Lujian Yao, Haitao Zhao, Xianghai Kong et al. (+1 more)
Smoke
segmentation is critical for wildfire management and industrial safety applications. Traditional visible-light-based methods face limitations due to insufficient spectral information, particularly struggling with cloud interference and semi-transparent smoke regions. To address these challenges, we introduce hyperspectral imaging for smoke
segmentation and present the first hyperspectral smoke
segmentation dataset (HSSDataset) with carefully annotated samples collected from over 18,000 frames across 20 real-world scenarios using a Many-to-One annotations protocol. However, different spectral bands exhibit varying discriminative capabilities across spatial regions, necessitating adaptive band weighting strategies. We decompose this into three technical challenges: spectral interaction contamination, limited spectral pattern modeling, and complex weighting router problems. We propose a mixture of prototypes (MoP) network with: (1) band split (BS) for spectral isolation, (2) prototype-based spectral representation (PSR) for diverse patterns, and (3) dual-stage router (DSR) for adaptive spatial-aware band weighting. We further construct a multispectral
dataset (MSSDataset) with RGB-infrared images. Extensive experiments validate superior performance across both hyperspectral and multispectral modalities, establishing a new paradigm for spectral-based smoke
segmentation.
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All authors Lujian Yao, Haitao Zhao, Xianghai Kong, Yuhan Xu
Computer Vision
Flow caching for autoregressive video generation
Yuexiao Ma, Xuzhe Zheng, Jing Xu et al. (+9 more)
Autoregressive models, often built on
Transformer architectures, represent a powerful paradigm for generating ultra-long
videos by synthesizing content in sequential chunks. However, this sequential generation process is notoriously slow. While caching strategies have proven effective for accelerating traditional
video diffusion models, existing methods assume uniform denoising across all frames-an assumption that breaks down in autoregressive models where different
video chunks exhibit varying similarity patterns at identical timesteps. In this paper, we present FlowCache, the first caching framework specifically designed for autoregressive
video generation. Our key insight is that each
video chunk should maintain independent caching policies, allowing fine-grained control over which chunks require recomputation at each timestep. We introduce a chunkwise caching strategy that dynamically adapts to the unique denoising characteristics of each chunk, complemented by a joint importance-redundancy optimized KV cache compression mechanism that maintains fixed
memory bounds while preserving generation quality. Our method achieves remarkable speedups of 2.38 times on MAGI-1 and 6.7 times on SkyReels-V2, with negligible quality degradation (VBench: 0.87 increase and 0.79 decrease respectively). These results demonstrate that FlowCache successfully unlocks the potential of autoregressive models for real-time, ultra-long
video generation-establishing a new
benchmark for efficient
video synthesis at scale. The code is available at https://github.com/mikeallen39/FlowCache.
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All authors Yuexiao Ma, Xuzhe Zheng, Jing Xu, Xiwei Xu, Feng Ling, Xiawu Zheng, Huafeng Kuang, Huixia Li, Xing Wang, Xuefeng Xiao, Fei Chao, Rongrong Ji
Computer Vision
Resource-Efficient RGB-Only Action Recognition for Edge Deployment
Dongsik Yoon, Jongeun Kim and Dayeon Lee
Resource-constrained assistive monitoring requires compact local
video perception and an explicit understanding of how recognition reliability changes under common visual degradation. We present a compact RGB-only action-recognition network that combines an X3D-style hierarchy with temporal shift, a 3D Universal Inverted Bottleneck, Ghost pointwise convolutions, factorized depthwise operators, selective temporal adaptation, and parameter-free
attention. The model attains 95.10/98.31% on NTU RGB+D 60 (X-Sub/X-View) and 90.88/92.66% on NTU RGB+D 120 (X-Sub/X-Set) with only 0.96-0.99M parameters. To connect compact deployment with assistive use, we evaluate frozen checkpoints without retraining on the
datasets' official Medical Conditions groups while retaining the original 60-/120-way decision spaces. Medical macro recall reaches 93.19/97.93% on NTU60 and 91.47/91.48% on NTU120. Under controlled Level-2 visual stress on NTU60 X-Sub, medical recall drops by 1.77 points under low light, 2.23 under motion blur, 4.87 under crop perturbation, and 19.05 under occlusion; among the four selected Level-2 perturbations, occlusion causes the largest observed degradation. On Jetson Orin Nano, the TensorRT FP16 engine occupies 5.30 MiB, demonstrating strong static compactness but not throughput superiority. The results position the model as a compact perception component for lower-rate assistive monitoring, rather than as a complete clinical or robotic system.
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Computer Vision
Why Does RL Generalize Better Than SFT? A Data-Centric Perspective on VLM Post-Training
Aojun Lu, Tao Feng, Hangjie Yuan et al. (+2 more)
The adaptation of large-scale
Vision-
Language Models (VLMs) through post-training reveals a pronounced generalization gap: models fine-tuned with Reinforcement Learning (RL) consistently achieve superior out-of-distribution (OOD) performance compared to those trained with Supervised Fine-Tuning (SFT). This paper posits a data-centric explanation for this phenomenon, contending that RL's generalization advantage arises from an implicit data filtering mechanism that inherently prioritizes medium-difficulty training samples. To test this hypothesis, we systematically evaluate the OOD generalization of SFT models across training
datasets of varying difficulty levels. Our results confirm that data difficulty is a critical factor, revealing that training on hard samples significantly degrades OOD performance. Motivated by this finding, we introduce Difficulty-Curated SFT (DC-SFT), a straightforward method that explicitly filters the training set based on sample difficulty. Experiments show that DC-SFT not only substantially enhances OOD generalization over standard SFT, but also surpasses the performance of RL-based training, all while providing greater stability and computational efficiency. This work offers a data-centric account of the OOD generalization gap in VLMs and establishes a more efficient pathway to achieving robust generalization. Code is available at https://github.com/byyx666/DC-SFT.
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All authors Aojun Lu, Tao Feng, Hangjie Yuan, Wei Li, Yanan Sun
Computer Vision
DeepImageSearch: Benchmarking Multimodal Agents for Context-Aware Image Retrieval in Visual Histories
Chenlong Deng, Mengjie Deng, Junjie Wu et al. (+10 more)
Existing
multimodal retrieval systems excel at semantic matching but implicitly assume that query-image relevance can be measured in isolation. This paradigm overlooks the rich dependencies inherent in realistic visual streams, where information is distributed across temporal sequences rather than confined to single snapshots. To bridge this gap, we introduce DeepImageSearch, a novel agentic paradigm that reformulates image
retrieval as an autonomous exploration task. Models must plan and perform multi-step
reasoning over raw visual histories to locate targets based on implicit contextual cues. We construct DISBench, a challenging
benchmark built on interconnected visual data. To address the scalability challenge of creating context-dependent queries, we propose a human-model collaborative pipeline that employs
vision-
language models to mine latent spatiotemporal associations, effectively offloading intensive context discovery before human verification. Furthermore, we build a robust baseline using a modular
agent framework equipped with fine-grained tools and a dual-
memory system for long-horizon
navigation. Extensive experiments demonstrate that DISBench poses significant challenges to state-of-the-art models, highlighting the necessity of incorporating agentic
reasoning into next-generation
retrieval systems.
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All authors Chenlong Deng, Mengjie Deng, Junjie Wu, Dun Zeng, Teng Wang, Qingsong Xie, Jiadeng Huang, Shengjie Ma, Changwang Zhang, Zhaoxiang Wang, Jun Wang, Yutao Zhu, Zhicheng Dou
Computer Vision
DMP-3DAD: Cross-Category 3D Anomaly Detection via Realistic Depth Map Projection with Few Normal Samples
Zi Wang, Katsuya Hotta, Koichiro Kamide et al. (+4 more)
Cross-category anomaly detection for 3D point clouds aims to determine whether an unseen object belongs to a target category using only a few normal examples. Most existing methods rely on category-specific training, which limits their flexibility in few-shot scenarios. In this paper, we propose DMP-3DAD, a training-free framework for cross-category 3D anomaly detection based on multi-view realistic depth map projection. Specifically, by converting point clouds into a fixed set of realistic depth images, our method leverages a frozen CLIP visual encoder to extract multi-view representations and performs anomaly detection via weighted feature similarity, which does not require any fine-tuning or category-dependent adaptation. Extensive experiments on the ShapeNetPart
dataset demonstrate that DMP-3DAD achieves state-of-the-art performance under few-shot setting. The results show that the proposed approach provides a simple yet effective solution for practical cross-category 3D anomaly detection.
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All authors Zi Wang, Katsuya Hotta, Koichiro Kamide, Yawen Zou, Jianjian Qin, Chao Zhang, Jun Yu
Computer Vision
RSHallu: Dual-Mode Hallucination Evaluation for Remote-Sensing Multimodal Large Language Models with Domain-Tailored Mitigation
Zihui Zhou, Yong Feng, Yanying Chen et al. (+4 more)
Multimodal large
language models (MLLMs) are increasingly adopted in remote sensing (RS) and have shown strong performance on tasks such as RS visual grounding (RSVG), RS visual question answering (RSVQA), and
multimodal dialogue. However,
hallucinations, which are responses inconsistent with the input RS images, severely hinder their deployment in high-stakes scenarios (e.g., emergency management and agricultural monitoring) and remain under-explored in RS. In this work, we present RSHallu, a systematic study with three deliverables: (1) we formalize RS
hallucinations with an RS-oriented taxonomy and introduce image-level
hallucination to capture RS-specific inconsistencies beyond object-centric errors (e.g., modality, resolution, and scene-level semantics); (2) we build a
hallucination benchmark RSHalluEval (2,023 QA pairs) and enable dual-mode checking, supporting high-precision cloud
auditing and low-cost reproducible local checking via a compact checker fine-tuned on RSHalluCheck
dataset (15,396 QA pairs); and (3) we introduce a domain-tailored
dataset RSHalluShield (30k QA pairs) for training-friendly mitigation and further propose training-free plug-and-play strategies, including decoding-time logit correction and RS-aware prompting. Across representative RS-MLLMs, our mitigation improves the
hallucination-free rate by up to 21.63 percentage points under a unified protocol, while maintaining competitive performance on downstream RS tasks (RSVQA/RSVG). Code and
datasets will be released.
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All authors Zihui Zhou, Yong Feng, Yanying Chen, Guofan Duan, Zhenxi Song, Mingliang Zhou, Weijia Jia
Computer Vision · Cross-listed from cs.LG
Kill it with FIRE: On Leveraging Latent Space Directions for Runtime Backdoor Mitigation in Deep Neural Networks
Enrico Ahlers, Daniel Passon, Yannic Noller et al. (+1 more)
Machine learning models are increasingly present in our everyday lives; as a result, they become targets of
adversarial attackers seeking to manipulate the systems we interact with. A well-known vulnerability is a
backdoor introduced into a neural network by poisoned training data or a malicious training process.
Backdoors can be used to induce unwanted behavior by including a certain trigger in the input. Existing mitigations filter training data, modify the model, or perform expensive input modifications on samples. If a vulnerable model has already been deployed, however, those strategies are either ineffective or inefficient. To address this gap, we propose our inference-time
backdoor mitigation approach called FIRE (Feature-space Inference-time REpair). We hypothesize that a trigger induces structured and repeatable changes in the model's internal representation. We view the trigger as directions in the latent spaces between layers that can be applied in reverse to correct the inference mechanism. Therefore, we turn the backdoored model against itself by manipulating its latent representations and moving a poisoned sample's features along the
backdoor directions to neutralize the trigger. Our evaluation shows that FIRE has low computational overhead and outperforms current runtime mitigations on image
benchmarks across various attacks,
datasets, and network architectures.
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All authors Enrico Ahlers, Daniel Passon, Yannic Noller, Lars Grunske
Computer Vision
From Steering to Pedalling: Do Autonomous Driving VLMs Generalize to Cyclist-Assistive Spatial Perception and Planning?
Krishna Kanth Nakka and Vedasri Nakka
Cyclists often encounter safety-critical situations in urban traffic, highlighting the need for assistive systems that support safe and informed decision-making. Recently,
vision-
language models (VLMs) have demonstrated strong performance on autonomous driving
benchmarks, suggesting their potential for general traffic understanding and
navigation-related
reasoning. However, existing evaluations are predominantly vehicle-centric and fail to assess perception and
reasoning from a cyclist-centric viewpoint. To address this gap, we introduce CyclingVQA, a diagnostic
benchmark designed to probe perception, spatio-temporal understanding, and traffic-rule-to-lane
reasoning from a cyclist's perspective. Evaluating 31+ recent VLMs spanning general-purpose, spatially enhanced, and autonomous-driving-specialized models, we find that current models demonstrate encouraging capabilities, while also revealing clear areas for improvement in cyclist-centric perception and
reasoning, particularly in interpreting cyclist-specific traffic cues and associating signs with the correct navigational lanes. Notably, several driving-specialized models underperform strong generalist VLMs, indicating limited transfer from vehicle-centric training to cyclist-assistive scenarios. Finally, through systematic error analysis, we identify recurring failure modes to guide the development of more effective cyclist-assistive intelligent systems.
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Computer Vision
Dual-End Consistency Model
Linwei Dong, Ruoyu Guo, Ge Bai et al. (+3 more)
The slow iterative sampling nature remains a major bottleneck for the practical deployment of
diffusion and flow-based generative models. While consistency models (CMs) represent a state-of-the-art
distillation-based approach for efficient generation, their large-scale application is still limited by two key issues: training instability and inflexible sampling. Existing methods seek to mitigate these problems through architectural adjustments or regularized objectives, yet overlook the critical reliance on trajectory selection. In this work, we first conduct an analysis on these two limitations: training instability originates from loss divergence induced by unstable self-supervised term, whereas sampling inflexibility arises from error accumulation. Based on these insights and analysis, we propose the Dual-End Consistency Model (DE-CM) that selects vital sub-trajectory clusters to achieve stable and effective training. DE-CM decomposes the PF-ODE trajectory and selects three critical sub-trajectories as optimization targets. Specifically, our approach leverages continuous-time CMs objectives to achieve few-step
distillation and utilizes flow matching as a boundary regularizer to stabilize the training process. Furthermore, we propose a novel noise-to-noisy (N2N) mapping that can map noise to any point, thereby alleviating the error accumulation in the first step. Extensive experimental results show the effectiveness of our method: it achieves a state-of-the-art FID score of 1.70 in one-step generation on the ImageNet 256x256
dataset, outperforming existing CM-based one-step approaches.
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All authors Linwei Dong, Ruoyu Guo, Ge Bai, Zehuan Yuan, Yawei Luo, Changqing Zou
Computer Vision
Text-to-Vector Conversion for Residential Plan Design
Egor Bazhenov, Stepan Kasai, Viacheslav Shalamov et al. (+1 more)
Computer graphics, comprising both raster and vector components, is a fundamental part of modern science, industry, and digital communication. While raster graphics offer ease of use, its pixel-based structure limits scalability. Vector graphics, defined by mathematical primitives, provides scalability without quality loss, however, it is more complex to produce. For design and architecture, the versatility of vector graphics is paramount, despite its computational demands. This paper introduces a novel method for generating vector residential plans from textual descriptions. Our approach surpasses existing solutions by approximately 5% in CLIPScore-based visual quality, benefiting from its inherent handling of right angles and flexible settings. Additionally, we present a new
algorithm for vectorizing raster plans into structured vector images. Such images have a better CLIPscore compared to others by about 4%.
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All authors Egor Bazhenov, Stepan Kasai, Viacheslav Shalamov, Valeria Efimova
Computer Vision · Cross-listed from cs.CR
SecureScan: An AI-Driven Multi-Layer Framework for Malware and Phishing Detection Using Logistic Regression and Threat Intelligence Integration
Rumman Firdos and Aman Dangi
The growing sophistication of modern malware and phishing campaigns has diminished the effectiveness of traditional signature-based intrusion detection systems. This work presents SecureScan, an AI-driven, triple-layer detection framework that integrates logistic regression-based classification, heuristic analysis, and external threat intelligence via the VirusTotal API for comprehensive triage of URLs, file hashes, and binaries. The proposed architecture prioritizes efficiency by filtering known threats through heuristics, classifying uncertain samples using machine learning, and validating borderline cases with third-party intelligence. On
benchmark datasets, SecureScan achieves 93.1 percent accuracy with balanced precision (0.87) and recall (0.92), demonstrating strong generalization and reduced overfitting through threshold-based decision calibration. A calibrated threshold and gray-zone logic (0.45-0.55) were introduced to minimize false positives and enhance real-world stability. Experimental results indicate that a lightweight statistical model, when augmented with calibrated verification and external intelligence, can achieve reliability and performance comparable to more complex deep learning systems.
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Computer Vision
Spectral-Spatial Contrastive Learning Framework for Regression on Hyperspectral Data
Mohamad Dhaini, Paul Honeine, Maxime Berar et al. (+1 more)
Contrastive learning has demonstrated great success in representation learning, especially for image classification tasks. However, there is still a shortage in studies targeting regression tasks, and more specifically applications on hyperspectral data. In this paper, we propose a spectral-spatial contrastive learning framework for regression tasks for hyperspectral data, in a model-agnostic design allowing to enhance backbones such as 3D convolutional and
transformer-based networks. Moreover, we provide a collection of transformations relevant for augmenting hyperspectral data. Experiments on synthetic and real
datasets show that the proposed framework and transformations significantly improve the performance of all studied backbone models.
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All authors Mohamad Dhaini, Paul Honeine, Maxime Berar, Antonin Van Exem
Computer Vision
Self-Supervised Image Super-Resolution Quality Assessment based on Content-Free Multi-Model Oriented Representation Learning
Kian Majlessi, Amir Masoud Soltani, Mohammad Ebrahim Mahdavi et al. (+2 more)
Super-resolution (SR) applied to real-world low-resolution (LR) images often results in complex, irregular degradations that stem from the inherent complexity of natural scene acquisition. In contrast to SR artifacts arising from synthetic LR images created under well-defined scenarios, those distortions are highly unpredictable and vary significantly across different real-life contexts. Consequently, assessing the quality of SR images (SR-IQA) obtained from realistic LR, remains a challenging and underexplored problem. In this work, we introduce a no-reference SR-IQA approach tailored for such highly ill-posed realistic settings. The proposed method enables domain-adaptive IQA for real-world SR applications, particularly in data-scarce domains. We hypothesize that degradations in super-resolved images are strongly dependent on the underlying SR
algorithms, rather than being solely determined by image content. To this end, we introduce a self-supervised learning (SSL) strategy that first pretrains multiple SR model oriented representations in a pretext stage. Our contrastive learning framework forms positive pairs from images produced by the same SR model and negative pairs from those generated by different methods, independent of image content. The proposed approach S3 RIQA, further incorporates targeted preprocessing to extract complementary quality information and an auxiliary task to better handle the various degradation profiles associated with different SR scaling factors. To this end, we constructed a new
dataset, SRMORSS, to support unsupervised pretext training; it includes a wide range of SR
algorithms applied to numerous real LR images, which addresses a gap in existing
datasets. Experiments on real SR-IQA
benchmarks demonstrate that S3 RIQA consistently outperforms most state-of-the-art relevant metrics.
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All authors Kian Majlessi, Amir Masoud Soltani, Mohammad Ebrahim Mahdavi, Aurelien Gourrier, Peyman Adibi
Computer Vision
OccFace: Unified Occlusion-Aware Facial Landmark Detection with Per-Point Visibility
Xinhao Xiang, Zhengxin Li, Saurav Dhakad et al. (+3 more)
Accurate facial landmark detection under occlusion remains challenging, especially for human-like faces with large appearance variation and rotation-driven self-occlusion. Existing detectors typically localize landmarks while handling occlusion implicitly, without predicting per-point visibility that downstream applications can benefits. We present OccFace, an occlusion-aware framework for universal human-like faces, including humans, stylized characters, and other non-human designs. OccFace adopts a unified dense 100-point layout and a heatmap-based backbone, and adds an occlusion module that jointly predicts landmark coordinates and per-point visibility by combining local evidence with cross-landmark context. Visibility supervision mixes manual labels with landmark-aware masking that derives pseudo visibility from mask-heatmap overlap. We also create an occlusion-aware evaluation suite reporting NME on visible vs. occluded landmarks and benchmarking visibility with Occ AP, F1@0.5, and ROC-AUC, together with a
dataset annotated with 100-point landmarks and per-point visibility. Experiments show improved robustness under external occlusion and large head rotations, especially on occluded regions, while preserving accuracy on visible landmarks.
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All authors Xinhao Xiang, Zhengxin Li, Saurav Dhakad, Theo Bancroft, Jiawei Zhang, Weiyang Li
Computer Vision
A Diffusion-Based Generative Prior Approach to Sparse-view Computed Tomography
Davide Evangelista, Pasquale Cascarano and Elena Loli Piccolomini
The reconstruction of X-rays CT images from sparse or limited-angle geometries is a highly challenging task. The lack of data typically results in artifacts in the reconstructed image and may even lead to object distortions. For this reason, the use of deep generative models in this context has great interest and potential success. In the Deep Generative Prior (DGP) framework, the use of
diffusion-based generative models is combined with an iterative optimization
algorithm for the reconstruction of CT images from sinograms acquired under sparse geometries, to maintain the explainability of a model-based approach while introducing the generative power of a neural network. There are therefore several aspects that can be further investigated within these frameworks to improve reconstruction quality, such as image generation, the model, and the iterative
algorithm used to solve the minimization problem, for which we propose modifications with respect to existing approaches. The results obtained even under highly sparse geometries are very promising, although further research is clearly needed in this direction.
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Computer Vision
Ecological mapping with geospatial foundation models
Craig Mahlasi, Gciniwe S. Baloyi, Zaheed Gaffoor et al. (+6 more)
The value of Earth observation foundation models for high-impact ecological applications remains insufficiently characterized. This study is one of the first to systematically evaluate the performance, limitations and practical considerations across three common ecological use cases: forest functional trait estimation, land use and land cover mapping and peatland detection. We fine-tune two pretrained models (Prithvi-EO-2.0 and TerraMind) and
benchmark them against a ResNet-101 baseline using
datasets collected from open sources. Across all tasks, Prithvi-EO-2.0 and TerraMind consistently outperform the ResNet baseline, demonstrating improved generalization and transfer across ecological domains. TerraMind marginally exceeds Prithvi-EO-2.0 in unimodal settings and shows substantial gains when additional modalities are incorporated. However, performance is sensitive to divergence between downstream inputs and pretraining modalities, underscoring the need for careful
dataset alignment. Results also indicate that higher-resolution inputs and more accurate pixel-level labels remain critical for capturing fine-scale ecological dynamics.
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All authors Craig Mahlasi, Gciniwe S. Baloyi, Zaheed Gaffoor, Levente Klein, Anne Jones, Etienne Vos, Michal Muszynski, Geoffrey Dawson, Campbell Watson
Computer Vision · Cross-listed from cs.RO
From Representational Complementarity to Dual Systems: Synergizing VLM and Vision-Only Backbones for End-to-End Driving
Sining Ang, Yuguang Yang, Chenxu Dang et al. (+8 more)
Vision-
Language-Action (VLA) driving augments end-to-end (E2E) planning with
language-enabled visual backbones, but how VLM representations differ from
vision-only encoders after policy learning, and whether such differences matter for planning, remains unclear. Under a unified VLM-hidden +
diffusion-policy paradigm, we compare multiple VLM families/scales (InternVL3 and Qwen3VL) with standard
vision-only encoders (ResNet, ViT, and EVA-CLIP) while keeping the downstream planner fixed. We study representation, behavior, and system design. CKA/CCA and Shared–Unique SAE show that policy learning enlarges a common decision subspace, but both branches retain non-transferable residual factors. We further replicate this shared-plus-unique representation pattern on nuPlan using the AsyncDrive planning stack. Latent-intervention policies and scenario-level analysis further show that these residuals are behaviorally meaningful:
vision-only encoders are stronger in simple geometry-dominant scenes, whereas VLMs are more effective in semantically complex and interaction-heavy long-tail cases. The two branches also exhibit distinct progress–braking and path-choice tendencies, and an oracle best-of-two VLM+ViT selector reaches 93.58 PDMS on NAVSIM. We convert this complementarity into two lightweight systems: HybridDriveVLA, which selects from a compact cross-model candidate set using a learned trajectory scorer and improves PDMS from 90.80 to 92.10, and DualDriveVLA, a fast–slow variant that invokes the VLM in only 15% of scenarios, achieving 91.00 PDMS with about $1.9\times$ lower latency than the VLM baseline. Code will be available at https://github.com/WilliamXuanYu/HybridDriveVLA.
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All authors Sining Ang, Yuguang Yang, Chenxu Dang, Canyu Chen, Cheng Chi, Haiyan Liu, Xuanyao Mao, Jason Bao, Xuliang, Bingchuan Sun, Yan Wang
Computer Vision
FGAA-FPN: Foreground-Guided Angle-Aware Feature Pyramid Network for Oriented Object Detection
Jialin Ma
With the increasing availability of high-resolution remote sensing and aerial imagery, oriented object detection has become a key capability for geographic information updating, maritime surveillance, and disaster response. However, it remains challenging due to cluttered backgrounds, severe scale variation, and large orientation changes. Existing approaches largely improve performance through multi-scale feature fusion with feature pyramid networks or contextual modeling with
attention, but they often lack explicit foreground modeling and do not leverage
geometric orientation priors, which limits feature discriminability. To overcome these limitations, we propose FGAA-FPN, a Foreground-Guided Angle-Aware Feature Pyramid Network for oriented object detection. FGAA-FPN is built on a hierarchical functional decomposition that accounts for the distinct spatial resolution and semantic abstraction across pyramid levels, thereby strengthening multi-scale representations. Concretely, a Foreground-Guided Feature Modulation module learns foreground saliency under weak supervision to enhance object regions and suppress background interference in low-level features. In parallel, an Angle-Aware Multi-Head
Attention module encodes relative orientation relationships to guide global interactions among high-level semantic features. Extensive experiments on DOTA v1.0 and DOTA v1.5 demonstrate that FGAA-FPN achieves state-of-the-art results, reaching 75.5% and 68.3% mAP, respectively.
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Computer Vision
(MGS)$^2$-Net: Unifying Micro-Geometric Scale and Macro-Geometric Structure for Cross-View Geo-Localization
Minglei Li, Mengfan He, Chunyu Li et al. (+3 more)
Cross-view geo-localization (CVGL) is pivotal for GNSS-denied
UAV navigation but remains brittle under the drastic
geometric misalignment between oblique aerial views and orthographic satellite references. Existing methods predominantly operate within a 2D
manifold, neglecting the underlying 3D geometry where view-dependent vertical facades (macro-structure) and scale variations (micro-scale) severely corrupt feature
alignment. To bridge this gap, we propose (MGS)$^2$, a geometry-grounded framework. The core of our innovation is the Macro-
Geometric Structure Filtering (MGSF) module. Unlike pixel-wise matching sensitive to noise, MGSF leverages dilated
geometric gradients to physically filter out high-frequency facade artifacts while enhancing the view-invariant horizontal plane, directly addressing the domain shift. To guarantee robust input for this structural filtering, we explicitly incorporate a Micro-
Geometric Scale Adaptation (MGSA) module. MGSA utilizes depth priors to dynamically rectify scale discrepancies via multi-branch feature fusion. Furthermore, a
Geometric-Appearance Contrastive
Distillation (GACD) loss is designed to strictly discriminate against oblique occlusions. Extensive experiments demonstrate that (MGS)$^2$ achieves state-of-the-art performance, recording a Recall@1 of 97.5% on University-1652 and 97.02% on SUES-200. Furthermore, the framework exhibits superior cross-
dataset generalization against
geometric ambiguity. The code is available at: \href{https://github.com/GabrielLi1473/MGS-Net}{https://github.com/GabrielLi1473/MGS-Net}.
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All authors Minglei Li, Mengfan He, Chunyu Li, Chao Chen, Xingyu Shao, Ziyang Meng
Computer Vision
AugVLA-3D: Depth-Driven Feature Augmentation for Vision-Language-Action Models
Zhifeng Rao, Wenlong Chen, Lei Xie et al. (+4 more)
Vision-
Language-Action (VLA) models have recently achieved remarkable progress in robotic perception and control, yet most existing approaches primarily rely on VLM trained using 2D images, which limits their spatial understanding and action grounding in complex 3D environments. To address this limitation, we propose a novel framework that integrates depth estimation into VLA models to enrich 3D feature representations. Specifically, we employ a depth estimation baseline called VGGT to extract geometry-aware 3D cues from standard RGB inputs, enabling efficient utilization of existing large-scale 2D
datasets while implicitly recovering 3D structural information. To further enhance the reliability of these depth-derived features, we introduce a new module called action assistant, which constrains the learned 3D representations with action priors and ensures their consistency with downstream control tasks. By fusing the enhanced 3D features with conventional 2D visual
tokens, our approach significantly improves the generalization ability and robustness of VLA models. Experimental results demonstrate that the proposed method not only strengthens perception in geometrically ambiguous scenarios but also leads to superior action prediction accuracy. This work highlights the potential of depth-driven data augmentation and auxiliary expert supervision for bridging the gap between 2D observations and 3D-aware decision-making in robotic systems.
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All authors Zhifeng Rao, Wenlong Chen, Lei Xie, Xia Hua, Dongfu Yin, Zhen Tian, F. Richard Yu
Computer Vision
OmniVL-Guard: Towards Unified Vision-Language Forgery Detection and Grounding via Balanced RL
Jinjie Shen, Jing Wu, Yaxiong Wang et al. (+5 more)
Existing forgery detection methods are often limited to uni-modal or bi-modal settings, failing to handle the interleaved text, images, and
videos prevalent in real-world misinformation. To bridge this gap, this paper targets to develop a unified framework for omnibus
vision-
language forgery detection and grounding. In this unified setting, the {interplay} between diverse modalities and the dual requirements of simultaneous detection and localization pose a critical “difficulty bias“ problem: the simpler veracity classification task tends to dominate the gradients, leading to suboptimal performance in fine-grained grounding during multi-task optimization. To address this challenge, we propose OmniVL-Guard, a balanced reinforcement learning framework for omnibus
vision-
language forgery detection and grounding. Particularly, OmniVL-Guard comprises two core designs: Self-Evolving CoT Generatio and Adaptive Reward Scaling Policy Optimization (ARSPO). {Self-Evolving CoT Generation} synthesizes high-quality
reasoning paths, effectively overcoming the cold-start challenge. Building upon this, {Adaptive Reward Scaling Policy Optimization (ARSPO)} dynamically modulates reward scales and task weights, ensuring a balanced joint optimization. Extensive experiments demonstrate that OmniVL-Guard significantly outperforms state-of-the-art methods and exhibits zero-shot robust generalization across out-of-domain scenarios. The
dataset and code are publicly available at https://github.com/shen8424/OmniVL-Guard.
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All authors Jinjie Shen, Jing Wu, Yaxiong Wang, Lechao Cheng, Shengeng Tang, Tianrui Hui, Nan Pu, Zhun Zhong
Computer Vision
TwiFF (Think With Future Frames): A Large-Scale Dataset for Dynamic Visual Reasoning
Junhua Liu, Zhangcheng Wang, Zhike Han et al. (+3 more)
Visual Chain-of-Thought (VCoT) has emerged as a promising paradigm for enhancing
multimodal reasoning by integrating visual perception into intermediate
reasoning steps. However, existing VCoT approaches are largely confined to static scenarios and struggle to capture the temporal dynamics essential for tasks such as instruction, prediction, and camera motion. To bridge this gap, we propose TwiFF-2.7M, the first large-scale, temporally grounded VCoT
dataset derived from $2.7$ million
video clips, explicitly designed for dynamic visual question and answer. Accompanying this, we introduce TwiFF-Bench, a high-quality evaluation
benchmark of $1,078$ samples that assesses both the plausibility of
reasoning trajectories and the correctness of final answers in open-ended dynamic settings. Building on these foundations, we propose the TwiFF model, a unified modal that synergistically leverages pre-trained
video generation and image comprehension capabilities to produce temporally coherent visual
reasoning cues-iteratively generating future action frames and textual
reasoning. Extensive experiments demonstrate that TwiFF significantly outperforms existing VCoT methods and Textual Chain-of-Thought baselines on dynamic
reasoning tasks, which fully validates the effectiveness for visual question answering in dynamic scenarios. Our code and data is available at https://github.com/LiuJunhua02/TwiFF.
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All authors Junhua Liu, Zhangcheng Wang, Zhike Han, Ningli Wang, Guotao Liang, Kun Kuang