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Found 6 papers, 1 papers with code
Learning Causal Features for Incremental Object Detection
\keywords{Object detection, incremental learning, causal feature.
Multi-adversarial Faster-RCNN with Paradigm Teacher for Unrestricted Object Detection
Our proposed MAF has two distinct contributions: (1) The Hierarchical Domain Feature Alignment (HDFA) module is introduced to minimize the image-level domain disparity, where Scale Reduction Module (SRM) reduces the feature map size without information loss and increases the training efficiency.
Tasks Integrated Networks: Joint Detection and Retrieval for Image Search
The traditional object retrieval task aims to learn a discriminative feature representation with intra-similarity and inter-dissimilarity, which supposes that the objects in an image are manually or automatically pre-cropped exactly.
Domain Adaptive Object Detection via Asymmetric Tri-way Faster-RCNN
Unsupervised domain adaptive object detection is proposed recently to reduce the disparity between domains, where the source domain is label-rich while the target domain is label-agnostic.
Multi-adversarial Faster-RCNN for Unrestricted Object Detection
Conventional object detection methods essentially suppose that the training and testing data are collected from a restricted target domain with expensive labeling cost.
End-to-End Detection and Re-identification Integrated Net for Person Search
This paper proposes a pedestrian detection and re-identification (re-id) integration net (I-Net) in an end-to-end learning framework.
