The Background Also Matters: Background-Aware Motion-Guided Objects Discovery

• URL: http://arxiv.org/abs/2311.02633v1
• Date: Sun, 5 Nov 2023 12:35:47 GMT
• Title: The Background Also Matters: Background-Aware Motion-Guided Objects Discovery
• Authors: Sandra Kara, Hejer Ammar, Florian Chabot, Quoc-Cuong Pham
• Abstract summary: We propose a Background-aware Motion-guided Objects Discovery method. We leverage masks of moving objects extracted from optical flow and design a learning mechanism to extend them to the true foreground. This enables a joint learning of the objects discovery task and the object/non-object separation.
• Score: 2.6442319761949875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent works have shown that objects discovery can largely benefit from the inherent motion information in video data. However, these methods lack a proper background processing, resulting in an over-segmentation of the non-object regions into random segments. This is a critical limitation given the unsupervised setting, where object segments and noise are not distinguishable. To address this limitation we propose BMOD, a Background-aware Motion-guided Objects Discovery method. Concretely, we leverage masks of moving objects extracted from optical flow and design a learning mechanism to extend them to the true foreground composed of both moving and static objects. The background, a complementary concept of the learned foreground class, is then isolated in the object discovery process. This enables a joint learning of the objects discovery task and the object/non-object separation. The conducted experiments on synthetic and real-world datasets show that integrating our background handling with various cutting-edge methods brings each time a considerable improvement. Specifically, we improve the objects discovery performance with a large margin, while establishing a strong baseline for object/non-object separation.

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