Cosine meets Softmax: A tough-to-beat baseline for visual grounding

• URL: http://arxiv.org/abs/2009.06066v1
• Date: Sun, 13 Sep 2020 19:35:43 GMT
• Title: Cosine meets Softmax: A tough-to-beat baseline for visual grounding
• Authors: Nivedita Rufus, Unni Krishnan R Nair, K. Madhava Krishna and Vineet Gandhi
• Abstract summary: Our framework minimizes the cross-entropy loss over the cosine distance between multiple image ROI features with a 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%.
• Score: 17.316608734530124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 give 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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