This paper is on Few-Shot Object Detection (FSOD), where given a few templates (examples) depicting a novel class (not seen during training), the goal is to detect all of its occurrences within a set of images. From a practical perspective, an FSOD system must fulfil the following desiderata: (a) it must be used as is, without requiring any fine-tuning at test time, (b) it must be able to process an arbitrary number of novel objects concurrently while supporting an arbitrary number of examples from each class and (c) it must achieve accuracy comparable to a closed system. Towards satisfying (a)-(c), in this work, we make the following contributions: We introduce, for the first time, a simple, yet powerful, few-shot detection transformer (FS-DETR) based on visual prompting that can address both desiderata (a) and (b). Our system builds upon the DETR framework, extending it based on two key ideas: (1) feed the provided visual templates of the novel classes as visual prompts during test time, and (2) ``stamp'' these prompts with pseudo-class embeddings (akin to soft prompting), which are then predicted at the output of the decoder. Importantly, we show that our system is not only more flexible than existing methods, but also, it makes a step towards satisfying desideratum (c). Specifically, it is significantly more accurate than all methods that do not require fine-tuning and even matches and outperforms the current state-of-the-art fine-tuning based methods on the most well-established benchmarks (PASCAL VOC & MSCOCO).
Beyond Templates: Revisiting Zero-Shot Remote Sensing through Meta-Prompting
Remote Sensing Lab, National Technical University of Athens（遥感实验室，国家技术大学雅典分校）；Remote Sensing Lab, Department of Engineering and Sciences, National Technical University of Athens（遥感实验室，工程与科学系，国家技术大学雅典分校）；Universitas Mercatorum（默卡托大学）
Meta-Prompting for Automating Zero-shot Visual Recognition with LLMs
ICG, TU Graz（格拉茨工业大学ICG研究所）；CDL-EML（CDL-EML中心）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；JKU（约翰内斯开普勒大学）；IBM Research（IBM研究院）；Weizmann Institute of Science（魏茨曼科学研究所）；University of Bonn（波恩大学）
SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting
Mediterranean Agronomic Institute of Montpellier - CIHEAM-IAMM（地中海农业研究院-CIHEAM-IAMM）；Inria（法国国家信息与自动化技术研究院）；INRAE（法国国家农业研究咨询中心）；Cirad（国际热带农业研究中心）；UMR TETIS（TETIS联合研究单位）；Univ. of Montpellier（蒙彼利埃大学）
Prompting Decision Transformer for Few-Shot Policy Generalization
Carnegie Mellon University（卡内基梅隆大学）；University of Montreal（蒙特利尔大学）；Mila（米拉）；MIT-IBM Watson AI Lab（MIT-IBM沃森人工智能实验室）；Massachusetts Institute of Technology（麻省理工学院）；UMass Amherst（马萨诸塞大学阿默斯特分校）
PSMP-CLIP: Patch-Prompt SAM and Multi-Semantic Prompting for CLIP-Based Zero-Shot Anomaly Detection
Harbin Engineering University（哈尔滨工程大学）；Key Laboratory of Advanced Marine Communication and Information Technology（先进海洋通信与信息技术重点实验室）；University of Toronto（多伦多大学）；Kanagawa University（神奈川大学）
Interpretable Few-Shot Retinal Disease Diagnosis with Concept-Guided Prompting of Vision-Language Models
Monash University（莫纳什大学）；Centre for Eye Research Australia（澳大利亚眼科研究中心）；Royal Victorian Eye and Ear Hospital（皇家维多利亚眼耳医院）；The University of Melbourne（墨尔本大学）；The Hong Kong Polytechnic University（香港理工大学）；Centre for Eye and Vision Research（眼与视觉研究中心）；Indian Institute of Technology Bombay（印度理工学院孟买分校）；Airdoc-Monash Research Lab（Airdoc-莫纳什研究实验室）
Zero-Shot Temporal Action Detection via Vision-Language Prompting
University of Surrey（萨里大学）；iFlyTek-Surrey Joint Research Centre on Artificial Intelligence（讯飞-萨里人工智能联合研究中心）；Surrey Institute for People-Centred Artificial Intelligence, University of Surrey（萨里大学以人为本人工智能研究所）
Vita-CLIP: Video and text adaptive CLIP via Multimodal Prompting
Mohamed bin Zayed University of AI（穆罕默德·本·扎耶德人工智能大学）；Australian National University（澳大利亚国立大学）；Linköping University（林雪平大学）；University of Central Florida（中佛罗里达大学）
