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On Generalizing Detection Models for Unconstrained Environments

2019-09-28Code Available0· sign in to hype

Prajjwal Bhargava

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Abstract

Object detection has seen tremendous progress in recent years. However, current algorithms don't generalize well when tested on diverse data distributions. We address the problem of incremental learning in object detection on the India Driving Dataset (IDD). Our approach involves using multiple domain-specific classifiers and effective transfer learning techniques focussed on avoiding catastrophic forgetting. We evaluate our approach on the IDD and BDD100K dataset. Results show the effectiveness of our domain adaptive approach in the case of domain shifts in environments.

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
BDD100K valhybrid incremental netmAP@0.545.7Unverified
India Driving Datasethybrid incremental netmAP@0.531.57Unverified

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