Learning Embeddings with Centroid Triplet Loss for Object Identification in Robotic Grasping

• URL: http://arxiv.org/abs/2404.06277v2
• Date: Mon, 8 Jul 2024 15:29:27 GMT
• Title: Learning Embeddings with Centroid Triplet Loss for Object Identification in Robotic Grasping
• Authors: Anas Gouda, Max Schwarz, Christopher Reining, Sven Behnke, Alice Kirchheim,
• Abstract summary: Foundation models are a strong trend in deep learning and computer vision. Here, we focus on training such an object identification model. Key solution to train such a model is the centroid triplet loss (CTL), which aggregates image features to their centroids.
• Score: 14.958823096408175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Foundation models are a strong trend in deep learning and computer vision. These models serve as a base for applications as they require minor or no further fine-tuning by developers to integrate into their applications. Foundation models for zero-shot object segmentation such as Segment Anything (SAM) output segmentation masks from images without any further object information. When they are followed in a pipeline by an object identification model, they can perform object detection without training. Here, we focus on training such an object identification model. A crucial practical aspect for an object identification model is to be flexible in input size. As object identification is an image retrieval problem, a suitable method should handle multi-query multi-gallery situations without constraining the number of input images (e.g. by having fixed-size aggregation layers). The key solution to train such a model is the centroid triplet loss (CTL), which aggregates image features to their centroids. CTL yields high accuracy, avoids misleading training signals and keeps the model input size flexible. In our experiments, we establish a new state of the art on the ArmBench object identification task, which shows general applicability of our model. We furthermore demonstrate an integrated unseen object detection pipeline on the challenging HOPE dataset, which requires fine-grained detection. There, our pipeline matches and surpasses related methods which have been trained on dataset-specific data.

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