Improving Object Detection and Attribute Recognition by Feature Entanglement Reduction

• URL: http://arxiv.org/abs/2108.11501v1
• Date: Wed, 25 Aug 2021 22:27:06 GMT
• Title: Improving Object Detection and Attribute Recognition by Feature Entanglement Reduction
• Authors: Zhaoheng Zheng, Arka Sadhu and Ram Nevatia
• Abstract summary: We show that object detection should be attribute-independent and attributes be largely object-independent. We disentangle them by the use of a two-stream model where the category and attribute features are computed independently but the classification heads share Regions of Interest (RoIs) Compared with a traditional single-stream model, our model shows significant improvements over VG-20, a subset of Visual Genome, on both supervised and attribute transfer tasks.
• Score: 26.20319853343761
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We explore object detection with two attributes: color and material. The task aims to simultaneously detect objects and infer their color and material. A straight-forward approach is to add attribute heads at the very end of a usual object detection pipeline. However, we observe that the two goals are in conflict: Object detection should be attribute-independent and attributes be largely object-independent. Features computed by a standard detection network entangle the category and attribute features; we disentangle them by the use of a two-stream model where the category and attribute features are computed independently but the classification heads share Regions of Interest (RoIs). Compared with a traditional single-stream model, our model shows significant improvements over VG-20, a subset of Visual Genome, on both supervised and attribute transfer tasks.

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