Object Detection

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Object Detection

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Open-Vocabulary Object Detection

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Yolo26

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Yolov8-Nano Model

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GiPL: Generative augmented iterative Pseudo-Labeling for Cross-Domain Few-Shot Object Detection
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May 27, 2026Yupeng Zhang, Ruize Han, Wei Feng +2Open-Vocabulary Object Detection
YOLO26-RipeLoc Lite: A lightweight architecture for tomato ripeness detection and picking point localization in greenhouse robotic harvesting
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May 26, 2026Melanie Neubauer, Christian Rauch, Gerald Koinig +3Video Dataset Object Detection
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May 24, 2026Chidera G. Oguine, Kanyifeechukwu J. Oguine, Obiozor M. Oguine +1Yolov8-Nano Model Yolo26
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Beyond Normal References: Discriminative Few-Shot Anomaly Detection
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May 22, 2026Chenxu Wang, Yuxuan Li, Yunheng Li +3Fine-Grained Perception Object Detection
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May 21, 2026Zacharie Chenail-Larcher, Brahim Mahmoudi, Naouel Moha +2Code Quality LLM Inference Efficiency
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May 13, 2026Chiara Musso, Joy Battocchio, Andrea Montibeller +1Explainable Deepfake Detection Adversarial Examples
Multi-Modal Guided Multi-Source Domain Adaptation for Object Detection
May 13, 2026Sangin Lee, Seokjun Kwon, Jeongmin Shin +2Object Detection Domain Adaptation
ERPPO: Entropy Regularization-based Proximal Policy Optimization
May 13, 2026Changha Lee, Gyusang ChoProximal Policy Optimization Multi-Agent Reinforcement Learning
FRAME: Forensic Routing and Adaptive Multi-path Evidence Fusion for Image Manipulation Detection
May 12, 2026Kaixiang Zhao, Tianrun Yu, Aoxu Zhang +3Multimedia Forensics Digital Forensics
A Data Efficiency Study of Synthetic Fog for Object Detection Using the Clear2Fog Pipeline
May 12, 2026Mohamed Ahmed Mohamed, Xiaowei HuangFog Synthetic-To-Real Domain Gap
Trajectory-Aware Adaptive Inference in Object Detection Models
May 12, 2026Grigorios Papanikolaou, Ioannis Kontopoulos, Giannis Spiliopoulos +2Object Detection Open-Vocabulary Object Detection
Anomaly-Aware Vision-Language Adapters for Zero-Shot Anomaly Detection
May 12, 2026Muhammad Aqeel, Maham Nazir, Uzair Khan +2Zero-Shot Anomaly Detection Vision-Language Model Adaptation

May 28, 2026 · cs.CVJ/K move · Enter open · S save

GiPL: Generative augmented iterative Pseudo-Labeling for Cross-Domain Few-Shot Object Detection

Jiacong Liu, Shu Luo, Yikai Qin, Yaze Zhao+2

Huazhong University of Science and Technology

Vision-language foundation models have shown promising zero-shot generalization for Cross-Domain Few-Shot Object Detection (CD-FSOD). However, they face two critical challenges in fine-tuning: insufficient support set utilization due to sparse single-instance annotations, and severe overfitting under extremely limited target-domain samples. To address these issues, this paper proposes GiPL, an efficient two-branch training framework. In the first branch, we design an iterative pseudo-label self-training paradigm, which performs zero-shot inference on the support set to generate reliable pseudo-annotations, fuses them with ground-truth labels, and iteratively optimizes the model to fully exploit support set data. In the second branch, we introduce generative data augmentation pipeline using large vision-language models, which synthesizes domain-aligned, multi-object annotated images to enrich training samples and suppress overfitting. Extensive experiments on three challenging CD-FSOD datasets (RUOD, CARPK, CarDD) under 1/5/10-shot settings demonstrate that GiPL consistently outperforms state-of-the-art methods with significant performance gains. Code is available at \href{https://github.com/z-yaz/CDiscover}{CDiscover}.

Few-Shot Segmentation Pseudo-Labeling Object Detection Large Vision Language Models Generative

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