Conventional vision based object detection or recognition models mostly depend on the view information of target examples. However

such attached view information is usually limited in several datasets. When the view information is scare

some generic object detection models try to learn the target by evaluating the view information with unsupervised learning methods. In this paper

a selective transfer learning method

TransferBoost

is improved and introduced to relieve the lack of object view information in training. The proposed TransferBoost

based on the GentleBoost framework

prompts the performance of learning the target by reusing prior knowledge from other object classes. Given a well labeled training set as source task

TransferBoost can transfer the knowledge on both instance level and task level by adjusting the weights of examples and task simultaneously. Such a combination of two levels transfers extracts useful information more effectively from mixed relevant source tasks and irrelevant source tasks. Our experimental results show

that compared to traditional machine learning methods

transfer learning needs much less training examples thus reduces the training cost of object detection or recognition models and extends the applicability of existing models.