TraceNet: Segment one thing efficiently

• URL: http://arxiv.org/abs/2406.14874v1
• Date: Fri, 21 Jun 2024 05:46:46 GMT
• Title: TraceNet: Segment one thing efficiently
• Authors: Mingyuan Wu, Zichuan Liu, Haozhen Zheng, Hongpeng Guo, Bo Chen, Xin Lu, Klara Nahrstedt,
• Abstract summary: We propose a one tap driven single instance segmentation task that segments a single instance selected by a user via a positive tap. We present TraceNet, which explicitly locates the selected instance by way of receptive field tracing. We evaluate the performance of TraceNet on instance IoU average over taps and the proportion of the region that a user tap can fall into for a high-quality single-instance mask.
• Score: 12.621208412232733
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficient single instance segmentation is essential for unlocking features in the mobile imaging applications, such as capture or editing. Existing on-the-fly mobile imaging applications scope the segmentation task to portraits or the salient subject due to the computational constraints. Instance segmentation, despite its recent developments towards efficient networks, is still heavy due to the cost of computation on the entire image to identify all instances. To address this, we propose and formulate a one tap driven single instance segmentation task that segments a single instance selected by a user via a positive tap. This task, in contrast to the broader task of segmenting anything as suggested in the Segment Anything Model \cite{sam}, focuses on efficient segmentation of a single instance specified by the user. To solve this problem, we present TraceNet, which explicitly locates the selected instance by way of receptive field tracing. TraceNet identifies image regions that are related to the user tap and heavy computations are only performed on selected regions of the image. Therefore overall computation cost and memory consumption are reduced during inference. We evaluate the performance of TraceNet on instance IoU average over taps and the proportion of the region that a user tap can fall into for a high-quality single-instance mask. Experimental results on MS-COCO and LVIS demonstrate the effectiveness and efficiency of the proposed approach. TraceNet can jointly achieve the efficiency and interactivity, filling in the gap between needs for efficient mobile inference and recent research trend towards multimodal and interactive segmentation models.

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