Balancing Specialization, Generalization, and Compression for Detection and Tracking

Kaufman, Dotan; Bibas, Koby; Borenstein, Eran; Chertok, Michael; Hassner, Tal

doi:10.48550/arxiv.1909.11348

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…To measure classification performance apply the widely-used Mean Average Precision (mAP) metric [24], arguably one of the most adopted evaluation metrics for multi-label classification (e.g., [27,48,62,63]). mAP is computed as:…”

Section: Evaluation Metricmentioning

confidence: 99%

Collaborative Image Understanding

Bibas

Shalom²,

Jannach

2022

Proceedings of the 31st ACM International Conference on Information &Amp; Knowledge Management

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Automatically understanding the contents of an image is a highly relevant problem in practice. In e-commerce and social media settings, for example, a common problem is to automatically categorize user-provided pictures. Nowadays, a standard approach is to fine-tune pre-trained image models with application-specific data. Besides images, organizations however often also collect collaborative signals in the context of their application, in particular how users interacted with the provided online content, e.g., in forms of viewing, rating, or tagging. Such signals are commonly used for item recommendation, typically by deriving latent user and item representations from the data. In this work, we show that such collaborative information can be leveraged to improve the classification process of new images. Specifically, we propose a multitask learning framework, where the auxiliary task is to reconstruct collaborative latent item representations. A series of experiments on datasets from e-commerce and social media demonstrates that considering collaborative signals helps to significantly improve the performance of the main task of image classification by up to 9.1%.

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Section: Evaluation Metricmentioning

confidence: 99%