Multiple Anchor Learning for Visual Object Detection

Classification and localization are two pillars of visual object detectors. However, in CNN-based detectors, these two modules are usually optimized under a fixed set of candidate (or anchor) bounding boxes. This configuration significantly limits the possibility to jointly optimize classification and localization. In this paper, we propose a Multiple Instance Learning (MIL) approach that selects anchors and jointly optimizes the two modules of a CNN-based object detector. Our approach, referred to as Multiple Anchor Learning (MAL), constructs anchor bags and selects the most representative anchors from each bag. Such an iterative selection process is potentially NP-hard to optimize. To address this issue, we solve MAL by repetitively depressing the confidence of selected anchors by perturbing their corresponding features. In an adversarial selection-depression manner, MAL not only pursues optimal solutions but also fully leverages multiple anchors/features to learn a detection model. Experiments show that MAL improves the baseline RetinaNet with significant margins on the commonly used MS-COCO object detection benchmark and achieves new state-of-the-art detection performance compared with recent methods.

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Datasets


Results from the Paper


Ranked #92 on Object Detection on COCO test-dev (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Object Detection COCO test-dev MAL (ResNeXt101, single-scale) box AP 45.9 # 105
Hardware Burden None # 1
Operations per network pass None # 1
Object Detection COCO test-dev MAL (ResNeXt101, multi-scale) box AP 47.0 # 92
Hardware Burden None # 1
Operations per network pass None # 1
Object Detection COCO test-dev MAL (ResNet50, single-scale) box AP 39.2 # 174
Hardware Burden None # 1
Operations per network pass None # 1

Methods