Meta-Personalizing Vision-Language Models to Find Named Instances in Video

• URL: http://arxiv.org/abs/2306.10169v1
• Date: Fri, 16 Jun 2023 20:12:11 GMT
• Title: Meta-Personalizing Vision-Language Models to Find Named Instances in Video
• Authors: Chun-Hsiao Yeh, Bryan Russell, Josef Sivic, Fabian Caba Heilbron, Simon Jenni
• Abstract summary: Large-scale vision-language models (VLM) have shown impressive results for language-guided search applications. They currently struggle with personalized searches for moments in a video where a specific object instance such as My dog Biscuit'' appears. We present a method to meta-personalize a pre-trained VLM, i.e., learning how to learn to personalize a VLM at test time to search in video.
• Score: 30.63415402318075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale vision-language models (VLM) have shown impressive results for language-guided search applications. While these models allow category-level queries, they currently struggle with personalized searches for moments in a video where a specific object instance such as ``My dog Biscuit'' appears. We present the following three contributions to address this problem. First, we describe a method to meta-personalize a pre-trained VLM, i.e., learning how to learn to personalize a VLM at test time to search in video. Our method extends the VLM's token vocabulary by learning novel word embeddings specific to each instance. To capture only instance-specific features, we represent each instance embedding as a combination of shared and learned global category features. Second, we propose to learn such personalization without explicit human supervision. Our approach automatically identifies moments of named visual instances in video using transcripts and vision-language similarity in the VLM's embedding space. Finally, we introduce This-Is-My, a personal video instance retrieval benchmark. We evaluate our approach on This-Is-My and DeepFashion2 and show that we obtain a 15% relative improvement over the state of the art on the latter dataset.

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