Tootfinder

Few Shot Semi-Supervised Learning for Abnormal Stop Detection from Sparse GPS Trajectories
Muhammad Ayub Sabir, Junbiao Pang, Jiaqi Wu, Fatima Ashraf
https://arxiv.org/abs/2510.12686

Few Shot Semi-Supervised Learning for Abnormal Stop Detection from Sparse GPS Trajectories
Abnormal stop detection (ASD) in intercity coach transportation is critical for ensuring passenger safety, operational reliability, and regulatory compliance. However, two key challenges hinder ASD effectiveness: sparse GPS trajectories, which obscure short or unauthorized stops, and limited labeled data, which restricts supervised learning. Existing methods often assume dense sampling or regular movement patterns, limiting their applicability. To address data sparsity, we propose a Sparsity-Aw…

Few-Shot Adaptation Benchmark for Remote Sensing Vision-Language Models
Karim El Khoury, Maxime Zanella, Christophe De Vleeschouwer, Benoit Macq
https://arxiv.org/abs/2510.07135

Few-Shot Adaptation Benchmark for Remote Sensing Vision-Language Models
Remote Sensing Vision-Language Models (RSVLMs) have shown remarkable potential thanks to large-scale pretraining, achieving strong zero-shot performance on various tasks. However, their ability to generalize in low-data regimes, such as few-shot learning, remains insufficiently explored. In this work, we present the first structured benchmark for evaluating few-shot adaptation methods on RSVLMs. We conduct comprehensive experiments across ten remote sensing scene classification datasets, applyi…

Few-shot Molecular Property Prediction: A Survey
Zeyu Wang, Tianyi Jiang, Huanchang Ma, Yao Lu, Xiaoze Bao, Shanqing Yu, Qi Xuan, Shirui Pan, Xin Zheng
https://arxiv.org/abs/2510.08900

Few-shot Molecular Property Prediction: A Survey
AI-assisted molecular property prediction has become a promising technique in early-stage drug discovery and materials design in recent years. However, due to high-cost and complex wet-lab experiments, real-world molecules usually experience the issue of scarce annotations, leading to limited labeled data for effective supervised AI model learning. In light of this, few-shot molecular property prediction (FSMPP) has emerged as an expressive paradigm that enables learning from only a few labeled…

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Aueaphum Aueawatthanaphisut
https://arxiv.org/abs/2510.00976 https://

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Rare-disease diagnosis remains one of the most pressing challenges in digital health, hindered by extreme data scarcity, privacy concerns, and the limited resources of edge devices. This paper proposes the Adaptive Federated Few-Shot Rare-Disease Diagnosis (AFFR) framework, which integrates three pillars: (i) few-shot federated optimization with meta-learning to generalize from limited patient samples, (ii) energy-aware client scheduling to mitigate device dropouts and ensure balanced participa…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[7/14]:
- Prompt Optimization Meets Subspace Representation Learning for Few-shot Out-of-Distribution Detec...
Faizul Rakib Sayem, Shahana Ibrahim

Advancing Few-Shot Pediatric Arrhythmia Classification with a Novel Contrastive Loss and Multimodal Learning
Yiqiao Chen, Zijian Huang, Zhenghui Feng
https://arxiv.org/abs/2509.19315

Advancing Few-Shot Pediatric Arrhythmia Classification with a Novel Contrastive Loss and Multimodal Learning
Pediatric arrhythmias are a major risk factor for disability and sudden cardiac death, yet their automated classification remains challenging due to class imbalance, few-shot categories, and complex signal characteristics, which severely limit the efficiency and reliability of early screening and clinical intervention. To address this problem, we propose a multimodal end-to-end deep learning framework that combines dual-branch convolutional encoders for ECG and IEGM, semantic attention for cros…

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Aueaphum Aueawatthanaphisut
https://arxiv.org/abs/2510.00976 https://

Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation
Rare-disease diagnosis remains one of the most pressing challenges in digital health, hindered by extreme data scarcity, privacy concerns, and the limited resources of edge devices. This paper proposes the Adaptive Federated Few-Shot Rare-Disease Diagnosis (AFFR) framework, which integrates three pillars: (i) few-shot federated optimization with meta-learning to generalize from limited patient samples, (ii) energy-aware client scheduling to mitigate device dropouts and ensure balanced participa…

MoTiC: Momentum Tightness and Contrast for Few-Shot Class-Incremental Learning
Zeyu He, Shuai Huang, Yuwu Lu, Ming Zhao
https://arxiv.org/abs/2509.19664 https://

MoTiC: Momentum Tightness and Contrast for Few-Shot Class-Incremental Learning
Few-Shot Class-Incremental Learning (FSCIL) must contend with the dual challenge of learning new classes from scarce samples while preserving old class knowledge. Existing methods use the frozen feature extractor and class-averaged prototypes to mitigate against catastrophic forgetting and overfitting. However, new-class prototypes suffer significant estimation bias due to extreme data scarcity, whereas base-class prototypes benefit from sufficient data. In this work, we theoretically demonstra…

Self-evolved Imitation Learning in Simulated World
Yifan Ye, Jun Cen, Jing Chen, Zhihe Lu
https://arxiv.org/abs/2509.19460 https://arxiv.org/pdf/2509.19460…

Self-evolved Imitation Learning in Simulated World
Imitation learning has been a trend recently, yet training a generalist agent across multiple tasks still requires large-scale expert demonstrations, which are costly and labor-intensive to collect. To address the challenge of limited supervision, we propose Self-Evolved Imitation Learning (SEIL), a framework that progressively improves a few-shot model through simulator interactions. The model first attempts tasksin the simulator, from which successful trajectories are collected as new demonst…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[10/14]:
- MGPATH: Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot WSI Classification
Nguyen, Nguyen, Diep, Nguyen, Ho, Metsch, Maurer, Sonntag, Bohnenberger, Hauschild

Measuring dissimilarity between convex cones by means of max-min angles
Welington de Oliveira, Valentina Sessa, David Sossa
https://arxiv.org/abs/2511.10483 https://arxiv.org/pdf/2511.10483 https://arxiv.org/html/2511.10483
arXiv:2511.10483v1 Announce Type: new
Abstract: This work introduces a novel dissimilarity measure between two convex cones, based on the max-min angle between them. We demonstrate that this measure is closely related to the Pompeiu-Hausdorff distance, a well-established metric for comparing compact sets. Furthermore, we examine cone configurations where the measure admits simplified or analytic forms. For the specific case of polyhedral cones, a nonconvex cutting-plane method is deployed to compute, at least approximately, the measure between them. Our approach builds on a tailored version of Kelley's cutting-plane algorithm, which involves solving a challenging master program per iteration. When this master program is solved locally, our method yields an angle that satisfies certain necessary optimality conditions of the underlying nonconvex optimization problem yielding the dissimilarity measure between the cones. As an application of the proposed mathematical and algorithmic framework, we address the image-set classification task under limited data conditions, a task that falls within the scope of the \emph{Few-Shot Learning} paradigm. In this context, image sets belonging to the same class are modeled as polyhedral cones, and our dissimilarity measure proves useful for understanding whether two image sets belong to the same class.
toXiv_bot_toot

FusionAdapter for Few-Shot Relation Learning in Multimodal Knowledge Graphs
Ran Liu, Yuan Fang, Xiaoli Li
https://arxiv.org/abs/2510.00894 https://arxiv.or…

FusionAdapter for Few-Shot Relation Learning in Multimodal Knowledge Graphs
Multimodal Knowledge Graphs (MMKGs) incorporate various modalities, including text and images, to enhance entity and relation representations. Notably, different modalities for the same entity often present complementary and diverse information. However, existing MMKG methods primarily align modalities into a shared space, which tends to overlook the distinct contributions of specific modalities, limiting their performance particularly in low-resource settings. To address this challenge, we pro…

In-Context Learning can Perform Continual Learning Like Humans
Liuwang Kang, Fan Wang, Shaoshan Liu, Hung-Chyun Chou, Chuan Lin, Ning Ding
https://arxiv.org/abs/2509.22764 https…

In-Context Learning can Perform Continual Learning Like Humans
Large language models (LLMs) can adapt to new tasks via in-context learning (ICL) without parameter updates, making them powerful learning engines for fast adaptation. While extensive research has examined ICL as a few-shot learner, whether it can achieve long-term retention and cross-task knowledge accumulation when multitasks arrive sequentially remains underexplored. Motivated by human memory studies, we investigate the retention characteristics of ICL in multitask settings and extend it to …

FusionAdapter for Few-Shot Relation Learning in Multimodal Knowledge Graphs
Ran Liu, Yuan Fang, Xiaoli Li
https://arxiv.org/abs/2510.00894 https://arxiv.or…

FusionAdapter for Few-Shot Relation Learning in Multimodal Knowledge Graphs
Multimodal Knowledge Graphs (MMKGs) incorporate various modalities, including text and images, to enhance entity and relation representations. Notably, different modalities for the same entity often present complementary and diverse information. However, existing MMKG methods primarily align modalities into a shared space, which tends to overlook the distinct contributions of specific modalities, limiting their performance particularly in low-resource settings. To address this challenge, we pro…

RoboSSM: Scalable In-context Imitation Learning via State-Space Models
Youngju Yoo, Jiaheng Hu, Yifeng Zhu, Bo Liu, Qiang Liu, Roberto Mart\'in-Mart\'in, Peter Stone
https://arxiv.org/abs/2509.19658

RoboSSM: Scalable In-context Imitation Learning via State-Space Models
In-context imitation learning (ICIL) enables robots to learn tasks from prompts consisting of just a handful of demonstrations. By eliminating the need for parameter updates at deployment time, this paradigm supports few-shot adaptation to novel tasks. However, recent ICIL methods rely on Transformers, which have computational limitations and tend to underperform when handling longer prompts than those seen during training. In this work, we introduce RoboSSM, a scalable recipe for in-context im…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/5]:
- Few-shot multi-token DreamBooth with LoRa for style-consistent character generation
Ruben Pascual, Mikel Sesma-Sara, Aranzazu Jurio, Daniel Paternain, Mikel Galar

RePro: Leveraging Large Language Models for Semi-Automated Reproduction of Networking Research Results
Yining Jiang, Wenyun Xu, Qingyu Song, Yuling Lin, Xuanhao Liu, Xiaoqiang Zheng, Qiang Su, Lizhao You, Lu Tang, Wangjian Feng, Linghe Kong, Qiao Xiang, Jiwu Shu
https://arxiv.org/abs/2509.21074

RePro: Leveraging Large Language Models for Semi-Automated Reproduction of Networking Research Results
Reproducing networking research is a critical but challenging task due to the scarcity of open-source code. While Large Language Models (LLMs) can automate code generation, current approaches lack the generalizability required for the diverse networking field. To address this, we propose RePro, a semi-automated reproduction framework that leverages advanced prompt engineering to reproduce network systems from their research papers. RePro combines few-shot in-context learning with Structured and…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/11]:
- Beyond Synthetic Replays: Turning Diffusion Features into Few-Shot Class-Incremental Learning Kno...
Junsu Kim, Yunhoe Ku, Dongyoon Han, Seungryul Baek