Intuitive observations show that a baby may inherently possess the capability
of recognizing a new visual concept (e.g., chair, dog) by learning from only
very few positive instances taught by parent(s) or others, and this recognition
capability can be gradually further improved by exploring and/or interacting
with the real instances in the physical world. Inspired by these observations,
we propose a computational model for slightly-supervised object detection,
based on prior knowledge modelling, exemplar learning and learning with video
contexts. The prior knowledge is modeled with a pre-trained Convolutional
Neural Network (CNN). When very few instances of a new concept are given, an
initial concept detector is built by exemplar learning over the deep features
from the pre-trained CNN. Simulating the baby's interaction with physical
world, the well-designed tracking solution is then used to discover more
diverse instances from the massive online unlabeled videos. Once a positive
instance is detected/identified with high score in each video, more variable
instances possibly from different view-angles and/or different distances are
tracked and accumulated. Then the concept detector can be fine-tuned based on
these new instances. This process can be repeated again and again till we
obtain a very mature concept detector. Extensive experiments on Pascal
VOC-07/10/12 object detection datasets well demonstrate the effectiveness of
our framework. It can beat the state-of-the-art full-training based
performances by learning from very few samples for each object category, along
with about 20,000 unlabeled videos.

本文提出了一种基于先前知识建模、示例学习和视频上下文学习的轻度半监督目标检测的计算模型，通过与现实实例的交互，从未标记的视频中检测并识别出更多的实例，进一步提高识别能力。