Self-Supervision & Meta-Learning for One-Shot Unsupervised Cross-Domain Detection
This addresses the challenge of deploying detection models in dynamic, real-world scenarios like social media feeds where target domains are unpredictable and data is scarce.
The paper tackles the problem of adapting object detection models to unseen domains using only a single target sample at test time, achieving state-of-the-art performance on a new social media dataset with significant improvements over existing methods.
Deep detection approaches are powerful in controlled conditions, but appear brittle and fail when source models are used off-the-shelf on unseen domains. Most of the existing works on domain adaptation simplify the setting and access jointly both a large source dataset and a sizable amount of target samples. However this scenario is unrealistic in many practical cases as when monitoring image feeds from social media: only a pretrained source model is available and every target image uploaded by the users belongs to a different domain not foreseen during training. We address this challenging setting by presenting an object detection algorithm able to exploit a pre-trained source model and perform unsupervised adaptation by using only one target sample seen at test time. Our multi-task architecture includes a self-supervised branch that we exploit to meta-train the whole model with single-sample cross-domain episodes, and prepare to the test condition. At deployment time the self-supervised task is iteratively solved on any incoming sample to one-shot adapt on it. We introduce a new dataset of social media image feeds and present a thorough benchmark with the most recent cross-domain detection methods showing the advantages of our approach.