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Erschienen in:
Commands 4 Autonomous Vehicles (C4AV) Workshop Summary Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Cosine Meets Softmax: A Tough-to-beat Baseline for Visual Grounding

verfasst von : Nivedita Rufus, Unni Krishnan R Nair, K. Madhava Krishna, Vineet Gandhi

Erschienen in: Computer Vision – ECCV 2020 Workshops

Verlag: Springer International Publishing

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Abstract

In this paper, we present a simple baseline for visual grounding for autonomous driving which outperforms the state of the art methods, while retaining minimal design choices. Our framework minimizes the cross-entropy loss over the cosine distance between multiple image ROI features with a text embedding (representing the given sentence/phrase). We use pre-trained networks for obtaining the initial embeddings and learn a transformation layer on top of the text embedding. We perform experiments on the Talk2Car dataset and achieve 68.7% AP50 accuracy, improving upon the previous state of the art by 8.6%. Our investigation suggests reconsideration towards more approaches employing sophisticated attention mechanisms or multi-stage reasoning or complex metric learning loss functions by showing promise in simpler alternatives.

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Vorheriges Kapitel C4AV: Learning Cross-Modal Representations from Transformers
Nächstes Kapitel Attention Enhanced Single Stage Multimodal Reasoner
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Metadaten
Titel
Cosine Meets Softmax: A Tough-to-beat Baseline for Visual Grounding
verfasst von
Nivedita Rufus
Unni Krishnan R Nair
K. Madhava Krishna
Vineet Gandhi
Copyright-Jahr
2020
Buch
Computer Vision – ECCV 2020 Workshops

Print ISBN: 978-3-030-66095-6

Electronic ISBN: 978-3-030-66096-3

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-66096-3_4

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