Papers Cross-Domain Few-Shot Object Detection
“Cross-Domain Few-Shot Object Detection” 태그가 달린 논문 23편 · 필터 해제
Prompt-Driven Simulation with Feature Perturbation for Cross-Domain Few-Shot Object Detection
Data augmentation, which simulates diverse visual variations to expand the source distribution and induce synthetic domain shifts, is a simple yet effective strategy for mitigating severe domain shifts and limited labele…
Cross-Domain Few-Shot Object DetectionData AugmentationVisual GroundingGiPL: Generative augmented iterative Pseudo-Labeling for Cross-Domain Few-Shot Object Detection
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 ut…
Cross-Domain Few-Shot Object DetectionZero-shot GeneralizationData AugmentationThe Second Challenge on Cross-Domain Few-Shot Object Detection at NTIRE 2026: Methods and Results
Cross-domain few-shot object detection (CD-FSOD) remains a challenging problem for existing object detectors and few-shot learning approaches, particularly when generalizing across distinct domains. As part of NTIRE 2026…
Cross-Domain Few-Shot Object DetectionFew-Shot LearningA Closer Look at Cross-Domain Few-Shot Object Detection: Fine-Tuning Matters and Parallel Decoder Helps
Few-shot object detection (FSOD) is challenging due to unstable optimization and limited generalization arising from the scarcity of training samples. To address these issues, we propose a hybrid ensemble decoder that en…
Cross-Domain Few-Shot Object DetectionEnsemble LearningRemedying Target-Domain Astigmatism for Cross-Domain Few-Shot Object Detection
Cross-domain few-shot object detection (CD-FSOD) aims to adapt pretrained detectors from a source domain to target domains with limited annotations, suffering from severe domain shifts and data scarcity problems. In this…
Cross-Domain Few-Shot Object DetectionLearning Multi-Modal Prototypes for Cross-Domain Few-Shot Object Detection
Cross-Domain Few-Shot Object Detection (CD-FSOD) aims to detect novel classes in unseen target domains given only a few labeled examples. While open-vocabulary detectors built on vision-language models (VLMs) transfer we…
Cross-Domain Few-Shot Object DetectionNo time to train! Training-Free Reference-Based Instance Segmentation
The performance of image segmentation models has historically been constrained by the high cost of collecting large-scale annotated data. The Segment Anything Model (SAM) alleviates this original problem through a prompt…
Cross-Domain Few-Shot Object DetectionFew-Shot Object DetectionImage SegmentationInstance Segmentation+2CDFormer: Cross-Domain Few-Shot Object Detection Transformer Against Feature Confusion
Cross-domain few-shot object detection (CD-FSOD) aims to detect novel objects across different domains with limited class instances. Feature confusion, including object-background confusion and object-object confusion, p…
Cross-Domain Few-ShotCross-Domain Few-Shot Object DetectionFew-Shot Object DetectionObject+2NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results
Cross-Domain Few-Shot Object Detection (CD-FSOD) poses significant challenges to existing object detection and few-shot detection models when applied across domains. In conjunction with NTIRE 2025, we organized the 1st C…
Cross-Domain Few-ShotCross-Domain Few-Shot Object DetectionFew-Shot Object DetectionObject+3Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object Detection
Foundation models pretrained on extensive datasets, such as GroundingDINO and LAE-DINO, have performed remarkably in the cross-domain few-shot object detection (CD-FSOD) task. Through rigorous few-shot training, we found…
Cross-Domain Few-ShotCross-Domain Few-Shot Object DetectionData AugmentationDomain Generalization+5Cross-domain Few-shot Object Detection with Multi-modal Textual Enrichment
Advancements in cross-modal feature extraction and integration have significantly enhanced performance in few-shot learning tasks. However, current multi-modal object detection (MM-OD) methods often experience notable pe…
Cross-Domain Few-ShotCross-Domain Few-Shot Object DetectionDomain AdaptationFew-Shot Learning+5Balanced ID-OOD tradeoff transfer makes query based detectors good few shot learners
Fine-tuning is a popular approach to solve the few-shot object detection problem. In this paper, we attempt to introduce a new perspective on it. We formulate the few-shot novel tasks as a type of distribution shifted fr…
Cross-Domain Few-Shot Object DetectionFew-Shot Object DetectionObjectobject-detection+1Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector
This paper studies the challenging cross-domain few-shot object detection (CD-FSOD), aiming to develop an accurate object detector for novel domains with minimal labeled examples. While transformer-based open-set detecto…
Cross-Domain Few-ShotCross-Domain Few-Shot Object DetectionFew-Shot Object DetectionObject+3Detect Everything with Few Examples
Few-shot object detection aims at detecting novel categories given only a few example images. It is a basic skill for a robot to perform tasks in open environments. Recent methods focus on finetuning strategies, with com…
Binary ClassificationCross-Domain Few-Shot Object DetectionFew-Shot Object DetectionObject+4CD-FSOD: A Benchmark for Cross-domain Few-shot Object Detection
In this paper, we propose a study of the cross-domain few-shot object detection (CD-FSOD) benchmark, consisting of image data from a diverse data domain. On the proposed benchmark, we evaluate state-of-art FSOD approache…
Cross-Domain Few-ShotCross-Domain Few-Shot Object DetectionFew-Shot Object DetectionMeta-Learning+2AcroFOD: An Adaptive Method for Cross-domain Few-shot Object Detection
Under the domain shift, cross-domain few-shot object detection aims to adapt object detectors in the target domain with a few annotated target data. There exists two significant challenges: (1) Highly insufficient target…
Cross-Domain Few-ShotCross-Domain Few-Shot Object DetectionData AugmentationDiversity+30/1 Deep Neural Networks via Block Coordinate Descent
The step function is one of the simplest and most natural activation functions for deep neural networks (DNNs). As it counts 1 for positive variables and 0 for others, its intrinsic characteristics (e.g., discontinuity a…
10-shot image generation16k2D Object Detection+92Exploring Plain Vision Transformer Backbones for Object Detection
We explore the plain, non-hierarchical Vision Transformer (ViT) as a backbone network for object detection. This design enables the original ViT architecture to be fine-tuned for object detection without needing to redes…
Cross-Domain Few-Shot Object DetectionInstance SegmentationObjectobject-detection+1Detecting Twenty-thousand Classes using Image-level Supervision
Current object detectors are limited in vocabulary size due to the small scale of detection datasets. Image classifiers, on the other hand, reason about much larger vocabularies, as their datasets are larger and easier t…
Cross-Domain Few-Shot Object Detectionimage-classificationImage ClassificationOpen Vocabulary Object DetectionDeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection
Few-shot object detection, which aims at detecting novel objects rapidly from extremely few annotated examples of previously unseen classes, has attracted significant research interest in the community. Most existing app…
ClassificationCross-Domain Few-Shot Object DetectionFew-Shot Object Detectionobject-detection+1