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There are great demands for automatically regulating inappropriate appearanceof shocking firearm images in social media or identifying firearm types inforensics. Image retrieval techniques have great potential to solve theseproblems. To facilitate research in this area, we introduce Firearm 14k, alarge dataset consisting of over 14,000 images in 167 categories. It can beused for both fine-grained recognition and retrieval of firearm images. Recentadvances in image retrieval are mainly driven by fine-tuning state-of-the-artconvolutional neural networks for retrieval task. The conventional singlemargin contrastive loss, known for its simplicity and good performance, hasbeen widely used. We find that it performs poorly on the Firearm 14k datasetdue to: (1) Loss contributed by positive and negative image pairs is unbalancedduring training process. (2) A huge domain gap exists between this dataset andImageNet. We propose to deal with the unbalanced loss by employing a doublemargin contrastive loss. We tackle the domain gap issue with a two-stagetraining strategy, where we first fine-tune the network for classification, andthen fine-tune it for retrieval. Experimental results show that our approachoutperforms the conventional single margin approach by a large margin (up to88.5% relative improvement) and even surpasses the strong triplet-loss-basedapproach.

DeepFirearm: Learning Discriminative Feature Representation for Fine-grained Firearm Retrieval