Object-based (yet Class-agnostic) Video Domain Adaptation
Existing video-based action recognition systems typically require dense annotation and struggle in environments when there is significant distribution shift relative to the training data. Current methods for video domain adaptation typically fine-tune the model using fully annotated data on a subset of target domain data or align the representation of the two domains using bootstrapping or adversarial learning. Inspired by the pivotal role of objects in recent supervised object-centric action recognition models, we present Object-based (yet Class-agnostic) Video Domain Adaptation (ODAPT), a simple yet effective framework for adapting the existing action recognition systems to new domains by utilizing a sparse set of frames with class-agnostic object annotations in a target domain. Our model achieves a +6.5 increase when adapting across kitchens in Epic-Kitchens and a +3.1 increase adapting between Epic-Kitchens and the EGTEA dataset. ODAPT is a general framework that can also be combined with previous unsupervised methods, offering a +5.0 boost when combined with the self-supervised multi-modal method MMSADA and a +1.7 boost when added to the adversarial-based method TA$^3$N on Epic-Kitchens.
Code (0)
등록된 구현이 없습니다.
Tasks
Action RecognitionDomain AdaptationObjectMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Multi-Modal Guided Multi-Source Domain Adaptation for Object Detection
General object detection (OD) struggles to detect objects in the target domain that differ from the training distribution. To address this, recent studies demonstrate that training from multiple source domains and explic…
Unsupervised Domain AdaptationObject DetectionActions and Objects Pathways for Domain Adaptation in Video Question Answering
In this paper, we introduce the Actions and Objects Pathways (AOPath) for out-of-domain generalization in video question answering tasks. AOPath leverages features from a large pretrained model to enhance generalizabilit…
Domain AdaptationDomain GeneralizationQuestion AnsweringVideo Question AnsweringLAGUNA: LAnguage Guided UNsupervised Adaptation with structured spaces
Unsupervised domain adaptation remains a critical challenge in enabling the knowledge transfer of models across unseen domains. Existing methods struggle to balance the need for domain-invariant representations with pres…
Domain AdaptationTransfer LearningUnsupervised Domain AdaptationAdversarial Bipartite Graph Learning for Video Domain Adaptation
Domain adaptation techniques, which focus on adapting models between distributionally different domains, are rarely explored in the video recognition area due to the significant spatial and temporal shifts across the sou…
Domain AdaptationGraph LearningVideo RecognitionSemi-supervised Domain Adaptation with Instance Constraints
Most successful object classification and detection methods rely on classifiers trained on large labeled datasets. However, for domains where labels are limited, simply borrowing labeled data from existing datasets can h…
Domain AdaptationGeneral Classificationimage-classificationImage Classification+4