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In this paper, we present a simple baseline for visual grounding forautonomous driving which outperforms the state of the art methods, whileretaining minimal design choices. Our framework minimizes the cross-entropyloss over the cosine distance between multiple image ROI features with a textembedding (representing the give sentence/phrase). We use pre-trained networksfor obtaining the initial embeddings and learn a transformation layer on top ofthe text embedding. We perform experiments on the Talk2Car dataset and achieve68.7% AP50 accuracy, improving upon the previous state of the art by 8.6%. Ourinvestigation suggests reconsideration towards more approaches employingsophisticated attention mechanisms or multi-stage reasoning or complex metriclearning loss functions by showing promise in simpler alternatives.

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