Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids

In this paper, we propose gated recurrent feature pyramid for the problem of learning object detection from scratch. Our approach is motivated by the recent work of deeply supervised object detector (DSOD) [24], but explores new network architecture that dynamically adjusts the supervision intensities of intermediate layers for various scales in object detection. The benefits of the proposed method are two-fold: First, we propose a recurrent featurepyramid structure to squeeze rich spatial and semantic features into a single prediction layer that further reduces the number of parameters to learn (DSOD need learn 1/2, but our method need only 1/3). Thus our new model is more fit for learning from scratch, and can converge faster than DSOD (using only 50% of iterations). Second, we introduce a novel gate-controlled prediction strategy to adaptively enhance or attenuate supervision at different scales based on the input object size. As a result, our model is more suitable for detecting small objects. To the best of our knowledge, our study is the best performed model of learning object detection from scratch. Our method in the PASCAL VOC 2012 comp3 leaderboard (which compares object detectors that are trained only with PASCAL VOC data) demonstrates a significant performance jump, from previous 64% to our 77% (VOC 07++12) and 72.5% (VOC 12). We also evaluate the performance of our method on PASCAL VOC 2007, 2012 and MS COCO datasets, and find that the accuracy of our learning from scratch method can even beat a lot of the state-of-the-art detection methods which use pre-trained models from ImageNet. Code is available at: https://github.com/szq0214/GRP-DSOD.