Sequential Context Encoding for Duplicate Removal
arXiv:1810.08770
Abstract
Duplicate removal is a critical step to accomplish a reasonable amount of predictions in prevalent proposal-based object detection frameworks. Albeit simple and effective, most previous algorithms utilize a greedy process without making sufficient use of properties of input data. In this work, we design a new two-stage framework to effectively select the appropriate proposal candidate for each object. The first stage suppresses most of easy negative object proposals, while the second stage selects true positives in the reduced proposal set. These two stages share the same network structure, \ie, an encoder and a decoder formed as recurrent neural networks (RNN) with global attention and context gate. The encoder scans proposal candidates in a sequential manner to capture the global context information, which is then fed to the decoder to extract optimal proposals. In our extensive experiments, the proposed method outperforms other alternatives by a large margin.
Accepted in NIPS 2018
References in corpus (10)
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Neural Machine Translation by Jointly Learning to Align and Translate
- Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- R-FCN: Object Detection via Region-based Fully Convolutional Networks
- Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
- Recurrent Models of Visual Attention
- Effective Approaches to Attention-based Neural Machine Translation
- End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results
- Polyphonic Music Generation by Modeling Temporal Dependencies Using a RNN-DBN
- Context Gates for Neural Machine Translation