Computer Vision
PKI: Prior Knowledge-Infused Neural Network for Few-Shot Class-Incremental Learning
Kexin Baoa, Fanzhao Lin, Zichen Wang et al. (+3 more)
Few-shot class-incremental learning (FSCIL) aims to continually adapt a model on a limited number of new-class examples, facing two well-known challenges: catastrophic forgetting and overfitting to new classes. Existing methods tend to freeze more parts of network components and finetune others with an extra
memory during incremental sessions. These methods emphasize preserving prior knowledge to ensure proficiency in recognizing old classes, thereby mitigating catastrophic forgetting. Meanwhile, constraining fewer parameters can help in overcoming overfitting with the assistance of prior knowledge. Following previous methods, we retain more prior knowledge and propose a prior knowledge-infused neural network (PKI) to facilitate FSCIL. PKI consists of a backbone, an ensemble of projectors, a classifier, and an extra
memory. In each incremental session, we build a new projector and add it to the ensemble. Subsequently, we finetune the new projector and the classifier jointly with other frozen network components, ensuring the rich prior knowledge is utilized effectively. By cascading projectors, PKI integrates prior knowledge accumulated from previous sessions and learns new knowledge flexibly, which helps to recognize old classes and efficiently learn new classes. Further, to reduce the resource consumption associated with keeping many projectors, we design two variants of the prior knowledge-infused neural network (PKIV-1 and PKIV-2) to trade off a balance between resource consumption and performance by reducing the number of projectors. Extensive experiments on three popular
benchmarks demonstrate that our approach outperforms state-of-the-art methods.
Read the full abstract ▾
All authors Kexin Baoa, Fanzhao Lin, Zichen Wang, Yong Li, Dan Zeng, Shiming Ge
Computer Vision
An IoT-Enabled Smart Aquarium System for Real-Time Water Quality Monitoring and Automated Feeding
MD Fatin Ishraque Ayon, Sabrin Nahar, Ataur Rahman et al. (+3 more)
Maintaining optimal water quality in aquariums is critical for aquatic health but remains challenging due to the need for continuous monitoring of multiple parameters. Traditional manual methods are inefficient, labor-intensive, and prone to human error, often leading to suboptimal aquatic conditions. This paper presents an IoT-based smart aquarium system that addresses these limitations by integrating an ESP32 microcontroller with multiple sensors (pH, TDS, temperature, turbidity) and actuators (servo feeder, water pump) for comprehensive real-time water quality monitoring and automated control. The system architecture incorporates edge processing capabilities, cloud connectivity via Blynk IoT platform, and an intelligent alert mechanism with configurable cooldown periods to prevent notification fatigue. Experimental evaluation in a 10-liter aquarium environment demonstrated the system's effectiveness, achieving 96% average sensor accuracy and 1.2-second response time for anomaly detection. The automated feeding and water circulation modules maintained 97% operational reliability throughout extended testing, significantly reducing manual intervention while ensuring stable aquatic conditions. This research demonstrates that cost-effective IoT solutions can revolutionize aquarium maintenance, making aquatic ecosystem management more accessible, reliable, and efficient for both residential and commercial applications.
Read the full abstract ▾
All authors MD Fatin Ishraque Ayon, Sabrin Nahar, Ataur Rahman, Md. Taslim Arif, Abdul Hasib, A. S. M. Ahsanul Sarkar Akib
Computer Vision · Cross-listed from cs.LG
DiffMM: Efficient Method for Accurate Noisy and Sparse Trajectory Map Matching via One Step Diffusion
Chenxu Han, Sean Bin Yang and Jilin Hu
Map matching for sparse trajectories is a fundamental problem for many trajectory-based applications, e.g., traffic scheduling and traffic flow analysis. Existing methods for map matching are generally based on Hidden Markov Model (HMM) or encoder-decoder framework. However, these methods continue to face significant challenges when handling noisy or sparsely sampled GPS trajectories. To address these limitations, we propose DiffMM, an encoder-
diffusion-based map matching framework that produces effective yet efficient matching results through a one-step
diffusion process. We first introduce a road segment-aware trajectory encoder that jointly embeds the input trajectory and its surrounding candidate road segments into a shared latent space through an
attention mechanism. Next, we propose a one step
diffusion method to realize map matching through a shortcut model by leveraging the joint
embedding of the trajectory and candidate road segments as conditioning context. We conduct extensive experiments on large-scale trajectory
datasets, demonstrating that our approach consistently outperforms state-of-the-art map matching methods in terms of both accuracy and efficiency, particularly for sparse trajectories and complex road network
topologies.
Read the full abstract ▾
Computer Vision
Cross-modal Proxy Evolving for OOD Detection with Vision-Language Models
Hao Tang, Yu Liu, Shuanglin Yan et al. (+3 more)
Reliable zero-shot detection of out-of-distribution (OOD) inputs is critical for deploying
vision-
language models in open-world settings. However, the lack of labeled negatives in zero-shot OOD detection necessitates proxy signals that remain effective under distribution shift. Existing negative-label methods rely on a fixed set of textual proxies, which (i) sparsely sample the semantic space beyond in-distribution (ID) classes and (ii) remain static while only visual features drift, leading to cross-modal misalignment and unstable predictions. In this paper, we propose CoEvo, a training- and annotation-free test-time framework that performs bidirectional, sample-conditioned adaptation of both textual and visual proxies. Specifically, CoEvo introduces a proxy-aligned co-
evolution mechanism to maintain two evolving proxy caches, which dynamically mines contextual textual negatives guided by test images and iteratively refines visual proxies, progressively realigning cross-modal similarities and enlarging local OOD margins. Finally, we dynamically re-weight the contributions of dual-modal proxies to obtain a calibrated OOD score that is robust to distribution shift. Extensive experiments on standard
benchmarks demonstrate that CoEvo achieves state-of-the-art performance, improving AUROC by 1.33% and reducing FPR95 by 45.98% on ImageNet-1K compared to strong negative-label baselines.
Read the full abstract ▾
All authors Hao Tang, Yu Liu, Shuanglin Yan, Fei Shen, Shengfeng He, Jing Qin
Computer Vision
Towards Safer Mobile Agents: Scalable Generation and Evaluation of Diverse Scenarios for VLMs
Takara Taniguchi, Kuniaki Saito and Atsushi Hashimoto
Vision Language Models (VLMs) are increasingly deployed in autonomous vehicles and mobile systems, making it crucial to evaluate their ability to support safer decision-making in complex environments. However, existing
benchmarks inadequately cover diverse hazardous situations, especially anomalous scenarios with spatio-temporal dynamics. While image editing models are a promising means to synthesize such hazards, it remains challenging to generate well-formulated scenarios that include moving, intrusive, and distant objects frequently observed in the real world. To address this gap, we introduce HazardForge, a scalable pipeline that leverages image editing models to generate these scenarios with layout decision
algorithms, and validation modules. Using HazardForge, we construct MovSafeBench, a multiple-choice question (MCQ)
benchmark comprising 7,254 images and corresponding QA pairs across 13 object categories, covering both normal and anomalous objects. Experiments using MovSafeBench show that VLM performance degrades notably under conditions including anomalous objects, with the largest drop in scenarios requiring nuanced motion understanding.
Read the full abstract ▾
Computer Vision
Zero-Shot Distracted Driver Detection via Vision Language Models with Double Decoupling
Takamichi Miyata, Sumiko Miyata and Andrew Morris
Distracted driving is a major cause of traffic collisions, calling for robust and scalable detection methods.
Vision-
language models (VLMs) enable strong zero-shot image classification, but existing VLM-based distracted driver detectors often underperform in real-world conditions. We identify subject-specific appearance variations (e.g., clothing, age, and gender) as a key bottleneck: VLMs entangle these factors with behavior cues, leading to decisions driven by who the driver is rather than what the driver is doing. To address this, we propose a subject decoupling framework that extracts a driver appearance
embedding and removes its influence from the image
embedding prior to zero-shot classification, thereby emphasizing distraction-relevant evidence. We further orthogonalize text
embeddings via metric projection onto Stiefel
manifold to improve separability while staying close to the original semantics. Experiments demonstrate consistent gains over prior baselines, indicating the promise of our approach for practical road-safety applications. Code is available at https://github.com/mtakamichi/ZVL-DDD
Read the full abstract ▾
Computer Vision
CoMa: Contextual Massing Generation with Vision-Language Models
Evgenii Maslov, Alexandra Vabnits, Vladimir Vorona et al. (+6 more)
Context-aware building massing is an important early-stage design task: given a site for buildings, a generated massing should not only fit the target parcel, but also relate to the scale, density, and morphology of its surrounding urban fabric. This task is naturally
multimodal, since the target output should remain structured and editable, while the surrounding context, including other buildings or roads, can be represented as vector geometry, map imagery, or three-dimensional views. In this paper, we study contextual massing generation using
vision-
language models (VLMs) and analyze their performance on this task across different context modalities during training and inference. We assemble an experimental
dataset of 12,845 Melbourne massings with parcel contours, structured 3D geometry, neighboring buildings, top-down views, and multi-view 3D context images. We also introduce a learned contextual relevance metric for evaluating whether generated massings are morphologically compatible with their surrounding context. Using Qwen3-VL models, we compare no-context, unimodal-context, and
multimodal-context training regimes and evaluate inference performance under controlled combinations of modalities and amounts of context. The results show that model size strongly affects generation quality,
multimodal training improves the use of individual modalities, and
multimodal inference provides a stronger contextual signal than isolated context inputs.
Read the full abstract ▾
All authors Evgenii Maslov, Alexandra Vabnits, Vladimir Vorona, Anastasia Antsiferova, Valentin Khrulkov, Anastasia Volkova, Anton Gusarov, Andrey Kuznetsov, Ivan Oseledets
Computer Vision
Modality-Decoupled RGB-Thermal Object Detector via Query Fusion
Chao Tian, Zikun Zhou, Chao Yang et al. (+3 more)
The advantage of RGB-Thermal (RGB-T) detection lies in its ability to perform modality fusion and integrate cross-modality complementary information, enabling robust detection under diverse illumination and weather conditions. However, under extreme conditions where one modality exhibits poor quality and disturbs detection, modality separation is necessary to mitigate the impact of noise. To address this problem, we propose a Modality-Decoupled RGB-T detection framework with Query Fusion (MDQF) to balance modality complementation and separation. In this framework, DETR-like detectors are employed as separate branches for the RGB and TIR images, with query fusion interspersed between the two branches in each refinement stage. Herein, query fusion is performed by feeding the high-quality queries from one branch to the other one after query selection and adaptation. This design effectively excludes the degraded modality and corrects the predictions using high-quality queries. Moreover, the decoupled framework allows us to optimize each individual branch with unpaired RGB or TIR images, eliminating the need for paired RGB-T data. Extensive experiments demonstrate that our approach delivers superior performance to existing RGB-T detectors and achieves better modality independence.
Read the full abstract ▾
All authors Chao Tian, Zikun Zhou, Chao Yang, Guoqing Zhu, Fu'an Zhong, Zhenyu He
Computer Vision
Developing Predictive and Robust Radiomics Models for Chemotherapy Response in High-Grade Serous Ovarian Carcinoma
Sepideh Hatamikia, Geevarghese George, Florian Schwarzhans et al. (+10 more)
Objectives: High-grade serous ovarian carcinoma (HGSOC) is typically diagnosed at an advanced stage with extensive peritoneal metastases, making treatment challenging. Neoadjuvant chemotherapy (NACT) is often used to reduce tumor burden before surgery, but about 40% of patients show limited response. Radiomics, combined with machine learning (ML), offers a promising non-invasive method for predicting NACT response by analyzing computed
tomography (CT) imaging data. This study aimed to improve response prediction in HGSOC patients undergoing NACT by integration different feature selection methods. Materials and methods: A framework for selecting robust radiomics features was introduced by employing an automated randomisation
algorithm to mimic inter-observer variability, ensuring a balance between feature robustness and prediction accuracy. Four response metrics were used: chemotherapy response score (CRS), RECIST, volume reduction (VolR), and diameter reduction (DiaR). Lesions in different anatomical sites were studied. Pre- and post-NACT CT scans were used for feature extraction and model training on one cohort, and an independent cohort was used for external testing. Results: The best prediction performance was achieved using all lesions combined for VolR prediction, with an AUC of 0.83. Omental lesions provided the best results for CRS prediction (AUC 0.77), while pelvic lesions performed best for DiaR (AUC 0.76). Conclusion: The integration of robustness into the feature selection processes ensures the development of reliable models and thus facilitates the implementation of the radiomics models in clinical applications for HGSOC patients. Future work should explore further applications of radiomics in ovarian cancer, particularly in real-time clinical settings.
Read the full abstract ▾
All authors Sepideh Hatamikia, Geevarghese George, Florian Schwarzhans, Amirreza Mahbod, Marika AV Reinius, Ali Abbasian Ardakani, Mercedes Jimenez-Linan, Satish Viswanath, Mireia Crispin-Ortuzar, Lorena Escudero Sanchez, Evis Sala, James D Brenton, Ramona Woitek
Computer Vision
Divide and Conquer: Static-Dynamic Collaboration for Few-Shot Class-Incremental Learning
Kexin Bao, Daichi Zhang, Yong Li et al. (+2 more)
Few-shot class-incremental learning (FSCIL) aims to continuously recognize novel classes under limited data, which suffers from the key stability-plasticity dilemma: balancing the retention of old knowledge with the acquisition of new knowledge. To address this issue, we divide the task into two different stages and propose a framework termed Static-Dynamic Collaboration (SDC) to achieve a better trade-off between stability and plasticity. Specifically, our method divides the normal pipeline of FSCIL into Static Retaining Stage (SRS) and Dynamic Learning Stage (DLS), which harnesses old static and incremental dynamic class information, respectively. During SRS, we train an initial model with sufficient data in the base session and preserve the key part as static
memory to retain fundamental old knowledge. During DLS, we introduce an extra dynamic projector jointly trained with the previous static
memory. By employing both stages, our method achieves improved retention of old knowledge while continuously adapting to new classes. Extensive experiments on three public
benchmarks and a real-world application
dataset demonstrate that our method achieves state-of-the-art performance against other competitors.
Read the full abstract ▾
All authors Kexin Bao, Daichi Zhang, Yong Li, Dan Zeng, Shiming Ge
Computer Vision
Noise-Adaptive Regularization for Robust Multi-Label Remote Sensing Image Classification
Tom Burgert, Julia Henkel and Begüm Demir
The development of reliable methods for multi-label classification (MLC) has become a prominent research direction in remote sensing (RS). As the scale of RS data continues to expand, annotation procedures increasingly rely on thematic products or crowdsourced procedures to reduce the cost of manual annotation. While cost-effective, these strategies often introduce multi-label noise in the form of partially incorrect annotations. In MLC, label noise arises as additive noise, subtractive noise, or a combination of both in the form of mixed noise. Previous work has largely overlooked this distinction and commonly treats noisy annotations as supervised signals, lacking mechanisms that explicitly adapt learning behavior to different noise types. To address this limitation, we propose NAR, a noise-adaptive regularization method that explicitly distinguishes between additive and subtractive noise within a semi-supervised learning framework. NAR employs a confidence-based label handling mechanism that dynamically retains label entries with high confidence, temporarily deactivates entries with moderate confidence, and corrects low confidence entries via flipping. This selective attenuation of supervision is integrated with early-learning regularization (ELR) to stabilize training and mitigate overfitting to corrupted labels. Experiments across additive, subtractive, and mixed noise scenarios demonstrate that NAR consistently improves robustness compared with existing methods. Performance improvements are most pronounced under subtractive and mixed noise, indicating that adaptive suppression and selective correction of noisy supervision provide an effective strategy for noise robust learning in RS MLC.
Read the full abstract ▾
Computer Vision
Incentivizing Cardiologist-Like Reasoning in MLLMs for Interpretable Echocardiographic Diagnosis
Yi Qin, Lehan Wang, Chenxu Zhao et al. (+2 more)
Echocardiographic diagnosis is vital for cardiac screening yet remains challenging. Existing echocardiography foundation models do not effectively capture the relationships between quantitative measurements and clinical manifestations, whereas medical
reasoning multimodal large
language models (MLLMs) require costly construction of detailed
reasoning paths and remain ineffective at directly incorporating such echocardiographic priors into their
reasoning. To address these limitations, we propose a novel approach comprising Cardiac
Reasoning Template (CRT) and CardiacMind to enhance MLLM's echocardiographic
reasoning by introducing cardiologist-like mindset. Specifically, CRT provides stepwise canonical diagnostic procedures for complex cardiac diseases to streamline
reasoning path construction without the need for costly case-by-case verification. To incentivize
reasoning MLLM under CRT, we develop CardiacMind, a new reinforcement learning scheme with three novel rewards: Procedural Quantity Reward (PQtR), Procedural Quality Reward (PQlR), and Echocardiographic Semantic Reward (ESR). PQtR promotes detailed
reasoning; PQlR promotes integration of evidence across views and modalities, while ESR grounds stepwise descriptions in visual content. Our methods show a 48% improvement in multiview echocardiographic diagnosis for 15 complex cardiac diseases and a 5% improvement on CardiacNet-PAH over prior methods. The user study on our method's
reasoning outputs shows 93.33% clinician agreement with cardiologist-like
reasoning logic. Our code will be available.
Read the full abstract ▾
All authors Yi Qin, Lehan Wang, Chenxu Zhao, Alex P.W. Lee, Xiaomeng Li
Computer Vision
Deep Learning Based Facial Retargeting Using Local Patches
Yeonsoo Choi, Inyup Lee, Sihun Cha et al. (+3 more)
In the era of digital animation, the quest to produce lifelike facial animations for virtual characters has led to the development of various retargeting methods. While the retargeting facial motion between models of similar shapes has been very successful, challenges arise when the retargeting is performed on stylized or exaggerated 3D characters that deviate significantly from human facial structures. In this scenario, it is important to consider the target character's facial structure and possible range of motion to preserve the semantics assumed by the original facial motions after the retargeting. To achieve this, we propose a local patch-based retargeting method that transfers facial animations captured in a source performance
video to a target stylized 3D character. Our method consists of three modules. The Automatic Patch Extraction Module extracts local patches from the source
video frame. These patches are processed through the Reenactment Module to generate correspondingly re-enacted target local patches. The Weight Estimation Module calculates the animation parameters for the target character at every frame for the creation of a complete facial animation sequence. Extensive experiments demonstrate that our method can successfully transfer the semantic meaning of source facial expressions to stylized characters with considerable variations in facial feature proportion.
Read the full abstract ▾
All authors Yeonsoo Choi, Inyup Lee, Sihun Cha, Seonghyeon Kim, Sunjin Jung, Junyong Noh
Computer Vision
MMLGNet: Cross-Modal Alignment of Remote Sensing Data using CLIP
Aditya Chaudhary, Sneha Barman, Mainak Singha et al. (+3 more)
In this paper, we propose a novel
multimodal framework,
Multimodal Language-Guided Network (MMLGNet), to align heterogeneous remote sensing modalities like Hyperspectral Imaging (HSI) and
LiDAR with natural
language semantics using
vision-
language models such as CLIP. With the increasing availability of
multimodal Earth observation data, there is a growing need for methods that effectively fuse spectral, spatial, and
geometric information while enabling semantic-level understanding. MMLGNet employs modality-specific encoders and aligns visual features with handcrafted textual
embeddings in a shared latent space via bi-directional contrastive learning. Inspired by CLIP's training paradigm, our approach bridges the gap between high-dimensional remote sensing data and
language-guided interpretation. Notably, MMLGNet achieves strong performance with simple CNN-based encoders, outperforming several established
multimodal visual-only methods on two
benchmark datasets, demonstrating the significant benefit of
language supervision. Codes are available at https://github.com/AdityaChaudhary2913/CLIP_HSI.
Read the full abstract ▾
All authors Aditya Chaudhary, Sneha Barman, Mainak Singha, Ankit Jha, Girish Mishra, Biplab Banerjee
Computer Vision
SPARK: Scalable Real-Time Point Cloud Aggregation with Multi-View Self-Calibration
Chentian Sun
Real-time multi-camera 3D reconstruction is crucial for 3D perception, immersive interaction, and robotics. Existing methods struggle with multi-view fusion, camera extrinsic uncertainty, and scalability for large camera setups. We propose SPARK, a self-calibrating real-time multi-camera point cloud reconstruction framework that jointly handles point cloud fusion and extrinsic uncertainty. SPARK consists of: (1) a geometry-aware online extrinsic estimation module leveraging multi-view priors and enforcing cross-view and temporal consistency for stable self-calibration, and (2) a confidence-driven point cloud fusion strategy modeling depth reliability and visibility at pixel and point levels to suppress noise and view-dependent inconsistencies. By performing frame-wise fusion without accumulation, SPARK produces stable point clouds in dynamic scenes while scaling linearly with the number of cameras. Extensive experiments on real-world multi-camera systems show that SPARK outperforms existing approaches in extrinsic accuracy,
geometric consistency, temporal stability, and real-time performance, demonstrating its effectiveness and scalability for large-scale multi-camera 3D reconstruction.
Read the full abstract ▾
Computer Vision
Edge-Optimized Multimodal Learning for UAV Video Understanding via BLIP-2
Yizhan Feng, Hichem Snoussi, Jing Teng et al. (+4 more)
The demand for real-time visual understanding and interaction in complex scenarios is increasingly critical for unmanned aerial vehicles. However, a significant challenge arises from the contradiction between the high computational cost of large
Vision language models and the limited computing resources available on
UAV edge devices. To address this challenge, this paper proposes a lightweight
multimodal task platform based on BLIP-2, integrated with YOLO-World and YOLOv8-Seg models. This integration extends the multi-task capabilities of BLIP-2 for
UAV applications with minimal adaptation and without requiring task-specific fine-tuning on drone data. Firstly, the deep integration of BLIP-2 with YOLO models enables it to leverage the precise perceptual results of YOLO for fundamental tasks like object detection and instance
segmentation, thereby facilitating deeper visual-
attention understanding and
reasoning. Secondly, a content-aware key frame sampling mechanism based on K-Means clustering is designed, which incorporates intelligent frame selection and temporal feature concatenation. This equips the lightweight BLIP-2 architecture with the capability to handle
video-level interactive tasks effectively. Thirdly, a unified
prompt optimization scheme for multi-task adaptation is implemented. This scheme strategically injects structured event logs from the YOLO models as contextual information into BLIP-2's input. Combined with output constraints designed to filter out technical details, this approach effectively guides the model to generate accurate and contextually relevant outputs for various tasks.
Read the full abstract ▾
All authors Yizhan Feng, Hichem Snoussi, Jing Teng, Jian Liu, Yuyang Wang, Abel Cherouat, Tian Wang
Computer Vision
An Explainable Two Stage Deep Learning Framework for Pericoronitis Assessment in Panoramic Radiographs Using YOLOv8 and ResNet-50
Ajo Babu George, Pranav S and Kunal Agarwal
Objectives: To overcome challenges in diagnosing pericoronitis on panoramic radiographs, an AI-assisted assessment system integrating anatomical localization, pathological classification, and interpretability. Methods: A two-stage deep learning pipeline was implemented. The first stage used YOLOv8 to detect third molars and classify their anatomical positions and angulations based on Winter's classification. Detected regions were then fed into a second-stage classifier, a modified ResNet-50 architecture, for detecting radiographic features suggestive of pericoronitis. To enhance clinical trust, Grad-CAM was used to highlight key diagnostic regions on the radiographs. Results: The YOLOv8 component achieved 92% precision and 92.5% mean average precision. The ResNet-50 classifier yielded F1-scores of 88% for normal cases and 86% for pericoronitis. Radiologists reported 84%
alignment between Grad-CAM and their diagnostic impressions, supporting the radiographic relevance of the interpretability output. Conclusion: The system shows strong potential for AI-assisted panoramic assessment, with explainable AI features that support clinical confidence.
Read the full abstract ▾
Computer Vision
Design and Development of a Low-Cost Scalable GSM-IoT Smart Pet Feeder with a Remote Mobile Application
Md. Rakibul Hasan Nishat, S. M. Khalid Bin Zahid, Abdul Hasib et al. (+3 more)
Pet ownership is increasingly common in modern households, yet maintaining a consistent feeding schedule remains challenging for the owners particularly those who live in cities and have busy lifestyles. This paper presents the design, development, and validation of a low-cost, scalable GSM-IoT smart pet feeder that enables remote monitoring and control through cellular communication. The device combines with an Arduino microcontroller, a SIM800L GSM module for communication, an ultrasonic sensor for real-time food-level assessment, and a servo mechanism for accurate portion dispensing. A dedicated mobile application was developed using MIT App Inventor which allows owners to send feeding commands and receive real-time status updates. Experimental results demonstrate a 98% SMS command success rate, consistent portion dispensing with $\pm 2.67$% variance, and reliable autonomous operation. Its modular, energy-efficient design makes it easy to use in a wide range of households, including those with limited resources. This work pushes forward the field of accessible pet care technology by providing a practical, scalable, and completely internet-independent solution for personalized pet feeding. In doing so, it sets a new
benchmark for low-cost, GSM-powered automation in smart pet products.
Read the full abstract ▾
All authors Md. Rakibul Hasan Nishat, S. M. Khalid Bin Zahid, Abdul Hasib, T. M. Mehrab Hasan, Mohammad Arman, A. S. M. Ahsanul Sarkar Akib
Computer Vision · Cross-listed from cs.LG
MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance
Matina Mahdizadeh Sani, Nima Jamali, Mohammad Jalali et al. (+1 more)
Pre-trained
diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their outputs often deviate from the characteristics of user-specific target data. Such mismatches are especially problematic in domain adaptation tasks, where only a few reference examples are available and retraining the
diffusion model is infeasible. Existing inference-time guidance methods can adjust sampling trajectories, but they typically optimize surrogate objectives such as classifier likelihoods rather than directly aligning with the target distribution. We propose MMD Guidance, a training-free mechanism that augments the reverse
diffusion process with gradients of the Maximum Mean Discrepancy (MMD) between generated samples and a reference
dataset. MMD provides reliable distributional estimates from limited data, exhibits low variance in practice, and is efficiently differentiable, which makes it particularly well-suited for the guidance task. Our framework naturally extends to
prompt-aware adaptation in conditional generation models via product kernels. Also, it can be applied with computational efficiency in latent
diffusion models (LDMs), since guidance is applied in the latent space of the LDM. Experiments on synthetic and real-world
benchmarks demonstrate that MMD Guidance can achieve distributional
alignment while preserving sample fidelity. The project code is available at github.com/matinamehdizadeh/MMD-Guidance.
Read the full abstract ▾
All authors Matina Mahdizadeh Sani, Nima Jamali, Mohammad Jalali, Farzan Farnia
Computer Vision
Source-Free Domain Adaptation for Geospatial Point Cloud Semantic Segmentation
Yuan Gao, Di Cao, Xiaohuan Xi et al. (+3 more)
Semantic
segmentation of 3D geospatial point clouds is fundamental to remote sensing applications, yet domain shifts caused by regional and acquisition-related variations often degrade model performance. Although domain adaptation can mitigate such shifts, existing methods typically require access to source-domain data, which is often infeasible due to
privacy concerns and regulatory policies. To address this, we propose LoGo (Local-Global Dual-Consensus), a novel source-free unsupervised domain adaptation (SFUDA) framework requiring only a pretrained model and unlabeled target data. At the local level, we introduce a class-balanced prototype estimation module that ensures that robust feature prototypes can be generated even for sample-scarce tail classes, effectively mitigating the feature collapse caused by long-tailed distributions. At the global level, we introduce an optimal transport-based global distribution
alignment module that formulates pseudo-label assignment as a global optimization problem, effectively correcting the over-dominance of head classes inherent in local greedy assignments, and thereby preventing model predictions from being severely biased towards majority classes. Finally, we propose a dual-consistency pseudo-label filtering mechanism that retains only high-confidence pseudo-labels where local multi-augmented ensemble predictions align with global optimal transport assignments for self-training. Extensive experiments on two challenging
benchmarks, encompassing cross-scene and cross-sensor settings, demonstrate that LoGo consistently outperforms existing state-of-the-art methods. The source code is available at https://github.com/GYproject/LoGo-SFUDA.
Read the full abstract ▾
All authors Yuan Gao, Di Cao, Xiaohuan Xi, Sheng Nie, Shaobo Xia, Cheng Wang
Computer Vision
Geo-NVS-w: Geometry-Aware Novel View Synthesis In-the-Wild with an SDF Renderer
Anastasios Tsalakopoulos, Angelos Kanlis, Evangelos Chatzis et al. (+2 more)
We introduce Geo-NVS-w, a geometry-aware framework for high-fidelity novel view synthesis from unstructured, in-the-wild image collections. While existing in-the-wild methods already excel at novel view synthesis, they often lack
geometric grounding on complex surfaces, sometimes producing results that contain inconsistencies. Geo-NVS-w addresses this limitation by leveraging an underlying
geometric representation based on a Signed Distance Function (SDF) to guide the rendering process. This is complemented by a novel Geometry-Preservation Loss which ensures that fine structural details are preserved. Our framework achieves competitive rendering performance, while demonstrating a 4-5x reduction reduction in energy consumption compared to similar methods. We demonstrate that Geo-NVS-w is a robust method for in-the-wild NVS, yielding photorealistic results with sharp, geometrically coherent details.
Read the full abstract ▾
All authors Anastasios Tsalakopoulos, Angelos Kanlis, Evangelos Chatzis, Antonis Karakottas, Dimitrios Zarpalas
Computer Vision
Semantic Misalignment in Vision-Language Models under Perceptual Degradation
Guo Cheng and Huang Li
Vision-
Language Models (VLMs) are increasingly deployed in autonomous driving and
embodied AI systems, where reliable perception is critical for safe semantic
reasoning and decision-making. While recent VLMs demonstrate strong performance on
multimodal benchmarks, their robustness to realistic perception degradation remains poorly understood. In this work, we systematically study semantic misalignment in VLMs under controlled degradation of upstream visual perception, using semantic
segmentation on the Cityscapes
dataset as a representative perception module. We introduce perception-realistic corruptions that induce only moderate drops in conventional
segmentation metrics, yet observe severe failures in downstream VLM behavior, including hallucinated object mentions, omission of safety-critical entities, and inconsistent safety judgments. To quantify these effects, we propose a set of
language-level misalignment metrics that capture
hallucination, critical omission, and safety misinterpretation, and analyze their relationship with
segmentation quality across multiple contrastive and generative VLMs. Our results reveal a clear disconnect between pixel-level robustness and
multimodal semantic reliability, highlighting a critical limitation of current VLM-based systems and motivating the need for evaluation frameworks that explicitly account for perception uncertainty in safety-critical applications.
Read the full abstract ▾
Computer Vision
From Local Windows to Adaptive Candidates via Individualized Exploratory: Rethinking Attention for Image Super-Resolution
Chunyu Meng, Wei Long and Shuhang Gu
Single Image Super-Resolution (SISR) is a fundamental computer
vision task that aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) input.
Transformer-based methods have achieved remarkable performance by modeling long-range dependencies in degraded images. However, their feature-intensive
attention computation incurs high computational cost. To improve efficiency, most existing approaches partition images into fixed groups and restrict
attention within each group. Such group-wise
attention overlooks the inherent asymmetry in
token similarities, thereby failing to enable flexible and
token-adaptive
attention computation. To address this limitation, we propose the Individualized Exploratory
Transformer (IET), which introduces a novel Individualized Exploratory
Attention (IEA) mechanism that allows each
token to adaptively select its own content-aware and independent
attention candidates. This
token-adaptive and asymmetric design enables more precise information aggregation while maintaining computational efficiency. Extensive experiments on standard SR
benchmarks demonstrate that IET achieves state-of-the-art performance under comparable computational complexity.
Read the full abstract ▾
Computer Vision
Tissue Classification and Whole-Slide Images Analysis via Modeling of the Tumor Microenvironment and Biological Pathways
Junzhuo Liu, Xuemei Du, Daniel Reisenbuchler et al. (+5 more)
Automatic integration of whole slide images (WSIs) and gene expression profiles has demonstrated substantial potential in precision clinical diagnosis and cancer progression studies. However, most existing studies focus on individual gene sequences and slide level classification tasks, with limited
attention to spatial transcriptomics and patch level applications. To address this limitation, we propose a
multimodal network, BioMorphNet, which automatically integrates tissue morphological features and spatial gene expression to support tissue classification and differential gene analysis. For considering morphological features, BioMorphNet constructs a graph to model the relationships between target patches and their neighbors, and adjusts the response strength based on morphological and molecular level similarity, to better characterize the tumor microenvironment. In terms of
multimodal interactions, BioMorphNet derives clinical pathway features from spatial transcriptomic data based on a predefined pathway database, serving as a bridge between tissue morphology and gene expression. In addition, a novel learnable pathway module is designed to automatically simulate the biological pathway formation process, providing a complementary representation to existing clinical pathways. Compared with the latest morphology gene
multimodal methods, BioMorphNet's average classification metrics improve by 2.67%, 5.48%, and 6.29% for prostate cancer, colorectal cancer, and breast cancer
datasets, respectively. BioMorphNet not only classifies tissue categories within WSIs accurately to support tumor localization, but also analyzes differential gene expression between tissue categories based on prediction confidence, contributing to the discovery of potential tumor biomarkers.
Read the full abstract ▾
All authors Junzhuo Liu, Xuemei Du, Daniel Reisenbuchler, Ye Chen, Markus Eckstein, Christian Matek, Friedrich Feuerhake, Dorit Merhof
Computer Vision
IGAN: A New Inception-based Model for Stable and High-Fidelity Image Synthesis Using Generative Adversarial Networks
Ahmed A. Hashim, Ali Al-Shuwaili, Asraa Saeed et al. (+1 more)
Generative
Adversarial Networks (GANs) face a significant challenge of striking an optimal balance between high-quality image generation and training stability. Recent techniques, such as DCGAN, BigGAN, and StyleGAN, improve visual fidelity; however, such techniques usually struggle with mode collapse and unstable gradients at high network depth. This paper proposes a novel GAN structural model that incorporates deeper inception-inspired convolution and dilated convolution. This novel model is termed the Inception Generative
Adversarial Network (IGAN). The IGAN model generates high-quality synthetic images while maintaining training stability, by reducing mode collapse as well as preventing vanishing and exploding gradients. Our proposed IGAN model achieves the Frechet Inception Distance (FID) of 13.12 and 15.08 on the CUB-200 and ImageNet
datasets, respectively, representing a 28-33% improvement in FID over the state-of-the-art GANs. Additionally, the IGAN model attains an Inception Score (IS) of 9.27 and 68.25, reflecting improved image diversity and generation quality. Finally, the two techniques of dropout and spectral normalization are utilized in both the generator and discriminator structures to further mitigate gradient explosion and overfitting. These findings confirm that the IGAN model potentially balances training stability with image generation quality, constituting a scalable and computationally efficient framework for high-fidelity image synthesis.
Read the full abstract ▾
All authors Ahmed A. Hashim, Ali Al-Shuwaili, Asraa Saeed, Ali Al-Bayaty
Computer Vision
UM-Text: A Unified Multimodal Model for Image Understanding and Visual Text Editing
Lichen Ma, Xiaolong Fu, Gaojing Zhou et al. (+6 more)
With the rapid advancement of image generation, visual text editing using natural
language instructions has received increasing
attention. The main challenge of this task is to fully understand the instruction and reference image, and thus generate visual text that is style-consistent with the image. Previous methods often involve complex steps of specifying the text content and attributes, such as font size, color, and layout, without considering the stylistic consistency with the reference image. To address this, we propose UM-Text, a unified
multimodal model for context understanding and visual text editing by natural
language instructions. Specifically, we introduce a Visual
Language Model (VLM) to process the instruction and reference image, so that the text content and layout can be elaborately designed according to the context information. To generate an accurate and harmonious visual text image, we further propose the UM-Encoder to combine the
embeddings of various condition information, where the combination is automatically configured by VLM according to the input instruction. During training, we propose a regional consistency loss to offer more effective supervision for glyph generation on both latent and RGB space, and design a tailored three-stage training strategy to further enhance model performance. In addition, we contribute the UM-DATA-200K, a large-scale visual text image
dataset on diverse scenes for model training. Extensive qualitative and quantitative results on multiple public
benchmarks demonstrate that our method achieves state-of-the-art performance.
Read the full abstract ▾
All authors Lichen Ma, Xiaolong Fu, Gaojing Zhou, Zipeng Guo, Ting Zhu, Yichun Liu, Yu Shi, Jason Li, Junshi Huang
Computer Vision
YOLOBirDrone: Dataset for Bird vs Drone Detection and Classification and a YOLO based enhanced learning architecture
Dapinder Kaur, Neeraj Battish, Arnav Bhavsar et al. (+1 more)
The use of aerial drones for commercial and defense applications has benefited in many ways and is therefore utilized in several different application domains. However, they are also increasingly used for targeted attacks, posing a significant safety challenge and necessitating the development of drone detection systems.
Vision-based drone detection systems currently have an accuracy limitation and struggle to distinguish between drones and birds, particularly when the birds are small in size. This research work proposes a novel YOLOBirDrone architecture that improves the detection and classification accuracy of birds and drones. YOLOBirDrone has different components, including an adaptive and extended layer aggregation (AELAN), a multi-scale progressive dual
attention module (MPDA), and a reverse MPDA (RMPDA) to preserve shape information and enrich features with local and global spatial and channel information. A large-scale
dataset, BirDrone, is also introduced in this article, which includes small and challenging objects for robust aerial object identification. Experimental results demonstrate an improvement in performance metrics through the proposed YOLOBirDrone architecture compared to other state-of-the-art
algorithms, with detection accuracy reaching approximately 85% across various scenarios.
Read the full abstract ▾
All authors Dapinder Kaur, Neeraj Battish, Arnav Bhavsar, Shashi Poddar
Computer Vision · Cross-listed from cs.LG
Deep Exploration of Epoch-wise Double Descent in Noisy Data: Signal Separation, Large Activation, and Benign Overfitting
Tomoki Kubo, Ryuken Uda and Yusuke Iida
Deep double descent is one of the key phenomena underlying the generalization capability of deep learning models. In this study, epoch-wise double descent, which is delayed generalization following overfitting, was empirically investigated by focusing on the
evolution of internal structures. Fully connected neural networks of three different sizes were trained on the CIFAR-10
dataset with 30% label noise. By decomposing the loss curves into signal contributions from clean and noisy training data, the epoch-wise evolutions of internal signals were analyzed separately. Three main findings were obtained from this analysis. First, the model achieved strong re-generalization on test data even after perfectly fitting noisy training data during the double descent phase, corresponding to a "benign overfitting" state. Second, noisy data were learned after clean data, and as learning progressed, their corresponding internal activations became increasingly separated in outer layers; this enabled the model to overfit only noisy data. Third, a single, very large activation emerged in the shallow layer across all models; this phenomenon is referred as "outliers," "massive activa-tions," and "super activations" in recent large
language models and evolves with re-generalization. The magnitude of large activation correlated with input patterns but not with output patterns. These empirical findings directly link the recent key phenomena of "deep double descent," "benign overfitting," and "large activation", and support the proposal of a novel scenario for understanding deep double descent.
Read the full abstract ▾
Computer Vision
Enhancing Image Quality Assessment Ability of LMMs via Retrieval-Augmented Generation
Kang Fu, Huiyu Duan, Zicheng Zhang et al. (+5 more)
Large
Multimodal Models (LMMs) have recently shown remarkable promise in low-level visual perception tasks, particularly in Image Quality Assessment (IQA), demonstrating strong zero-shot capability. However, achieving state-of-the-art performance often requires computationally expensive fine-tuning methods, which aim to align the distribution of quality-related
token in output with image quality levels. Inspired by recent training-free works for LMM, we introduce IQARAG, a novel, training-free framework that enhances LMMs' IQA ability. IQARAG leverages
Retrieval-Augmented Generation (RAG) to retrieve some semantically similar but quality-variant reference images with corresponding Mean Opinion Scores (MOSs) for input image. These retrieved images and input image are integrated into a specific
prompt. Retrieved images provide the LMM with a visual perception anchor for IQA task. IQARAG contains three key phases:
Retrieval Feature Extraction, Image
Retrieval, and Integration & Quality Score Generation. Extensive experiments across multiple diverse IQA
datasets, including KADID, KonIQ, LIVE Challenge, and SPAQ, demonstrate that the proposed IQARAG effectively boosts the IQA performance of LMMs, offering a resource-efficient alternative to fine-tuning for quality assessment.
Read the full abstract ▾
All authors Kang Fu, Huiyu Duan, Zicheng Zhang, Yucheng Zhu, Jun Zhao, Xiongkuo Min, Jia Wang, Guangtao Zhai
Computer Vision
SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices
Dongting Hu, Aarush Gupta, Magzhan Gabidolla et al. (+12 more)
Recent advances in
diffusion transformers (DiTs) have set new standards in image generation, yet remain impractical for on-device deployment due to their high computational and
memory costs. In this work, we present an efficient DiT framework tailored for mobile and edge devices that achieves
transformer-level generation quality under strict resource constraints. Our design combines three key components. First, we propose a compact DiT architecture with an adaptive global-local sparse
attention mechanism that balances global context modeling and local detail preservation. Second, we propose an elastic training framework that jointly optimizes sub-DiTs of varying capacities within a unified supernetwork, allowing a single model to dynamically adjust for efficient inference across different hardware. Finally, we develop Knowledge-Guided Distribution Matching
Distillation, a step-
distillation pipeline that integrates the DMD objective with knowledge transfer from few-step teacher models, producing high-fidelity and low-latency generation (e.g., 4-step) suitable for real-time on-device use. Together, these contributions enable scalable, efficient, and high-quality
diffusion models for deployment on diverse hardware.
Read the full abstract ▾
All authors Dongting Hu, Aarush Gupta, Magzhan Gabidolla, Arpit Sahni, Huseyin Coskun, Yanyu Li, Yerlan Idelbayev, Ahsan Mahmood, Aleksei Lebedev, Dishani Lahiri, Anujraaj Goyal, Ju Hu, Mingming Gong, Sergey Tulyakov, Anil Kag