Sequential Context Encoding for Duplicate Removal
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, i.e., 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.
- Abstract Although Duplicate Removal is an important step in object detection, since only the score of each candidate region is considered, the information of the input data was not sufficiently used by the conventional method. This paper proposes a new Duplicate Removal method based on RNN. Based on each candidate area, informative features are extracted by using appearance feature, position and ranking information in addition to the score. Then, they are treated as series data and are input into the RNN-based model to improve the final accuracy by capturing global information. The number of candidate regions is enormous to the number of objects that are to be left. Therefore, this paper proposes to reduce the box gradually by dividing it into two stages. In the two stages, the RNN model of the same structure was used. In stage I, to remove simple boxes the model is trained by using NMS results as a teaching signal. In stage II, to remove difficult boxes, the model is trained by using the grand-truth boxes. Experiments showed that mAP is increased in the SOTA object detection methods (FPN, Mask R - CNN, PANet with DCN) with the proposed method.  - Pros: -- This paper is easy to understand.  -- This paper pointed out the existing Duplicate Removal and proposed a new method to solve it. -- The proposed method is well designed.  - Cons: -- The proposed method is complicated. -- Improvement of detection accuracy seems to be marginal against the complexity of the method.  -- This paper does not include theoretical insights. -- Calculation of NMS is necessary for learning in stage I.  -- There is no description about the calculation cost. Since object detection is a trade-off between detection accuracy and calculation speed mainly in inference, a description on calculation cost is indispensable.  -- It would be better to try on other datasets. In addition, not only the overall mAP, but also more detailed analysis of the results is required. -- - In Table 7, the paper compares only with NMS. This paper should also compare with Box voting and Soft NMS. -- The publication year of [21] might be CVPR 2018.  Although methods are well designed, it is unclear about the calculation cost, and theoretical insights and new findings are not enough at this time.