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Sequential Context Encoding for Duplicate Removal
Lu Qi · Shu Liu · Jianping Shi · Jiaya Jia

Tue Dec 04 02:00 PM -- 04:00 PM (PST) @ Room 210 #57

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 utilized 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, 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.

Author Information

Lu Qi (The Chinese University of Hong Kong)
Shu Liu (Chinese University of Hong Kong)
Jianping Shi (Sensetime Group Limited)
Jiaya Jia (CUHK)

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