  Recent development of object detection mainly depends on deep learning with
large-scale benchmarks. However, collecting such fully-annotated data is often
difficult or expensive for real-world applications, which restricts the power
of deep neural networks in practice. Alternatively, humans can detect new
objects with little annotation burden, since humans often use the prior
knowledge to identify new objects with few elaborately-annotated examples, and
subsequently generalize this capacity by exploiting objects from wild images.
Inspired by this procedure of learning to detect, we propose a novel
Progressive Object Transfer Detection (POTD) framework. Specifically, we make
three main contributions in this paper. First, POTD can leverage various object
supervision of different domains effectively into a progressive detection
procedure. Via such human-like learning, one can boost a target detection task
with few annotations. Second, POTD consists of two delicate transfer stages,
i.e., Low-Shot Transfer Detection (LSTD), and Weakly-Supervised Transfer
Detection (WSTD). In LSTD, we distill the implicit object knowledge of source
detector to enhance target detector with few annotations. It can effectively
warm up WSTD later on. In WSTD, we design a recurrent object labelling
mechanism for learning to annotate weakly-labeled images. More importantly, we
exploit the reliable object supervision from LSTD, which can further enhance
the robustness of target detector in the WSTD stage. Finally, we perform
extensive experiments on a number of challenging detection benchmarks with
different settings. The results demonstrate that, our POTD outperforms the
recent state-of-the-art approaches.
