Exploit Bounding Box Annotations for Multi-label Object Recognition

TL;DR

This paper introduces a multi-view multi-instance learning framework that leverages both bounding box annotations and CNN features to improve multi-label object recognition, outperforming existing methods on benchmark datasets.

Contribution

It proposes a novel multi-view multi-instance learning approach that effectively combines weak and strong labels, enhancing recognition accuracy and generalization to unseen categories.

Findings

Achieves state-of-the-art results on benchmark datasets.

Utilizes both bounding box annotations and CNN features effectively.

Demonstrates improved generalization to unseen categories.

Abstract

Convolutional neural networks (CNNs) have shown great performance as general feature representations for object recognition applications. However, for multi-label images that contain multiple objects from different categories, scales and locations, global CNN features are not optimal. In this paper, we incorporate local information to enhance the feature discriminative power. In particular, we first extract object proposals from each image. With each image treated as a bag and object proposals extracted from it treated as instances, we transform the multi-label recognition problem into a multi-class multi-instance learning problem. Then, in addition to extracting the typical CNN feature representation from each proposal, we propose to make use of ground-truth bounding box annotations (strong labels) to add another level of local information by using nearest-neighbor relationships of local regions to form a multi-view pipeline. The proposed multi-view multi-instance framework utilizes both weak and strong labels effectively, and more importantly it has the generalization ability to even boost the performance of unseen categories by partial strong labels from other categories. Our framework is extensively compared with state-of-the-art hand-crafted feature based methods and CNN based methods on two multi-label benchmark datasets. The experimental results validate the discriminative power and the generalization ability of the proposed framework. With strong labels, our framework is able to achieve state-of-the-art results in both datasets.