We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| jshtok/RepMet | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| bb_overlap | Ran | jshtok/RepMet/fpn/few_shot_benchmark.py code served (permissive licence) · get_code("1f8d2cb74d49770a") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only a few examples. In this work, we propose a new method for DML that simultaneously learns the backbone network parameters, the embedding space, and the multi-modal distribution of each of the training categories in that space, in a single end-to-end training process. Our approach outperforms state-of-the-art methods for DML-based object classification on a variety of standard fine-grained datasets. Furthermore, we demonstrate the effectiveness of our approach on the problem of few-shot object detection, by incorporating the proposed DML architecture as a classification head into a standard object detection model. We achieve the best results on the ImageNet-LOC dataset compared to strong baselines, when only a few training examples are available. We also offer the community a new episodic benchmark based on the ImageNet dataset for the few-shot object detection task.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1806.04728")
get_code_for_paper("1806.04728")
have("1806.04728")
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