An analysis of HOI: using a training-free method with multimodal visual foundation models when only the test set is available, without the training set

• URL: http://arxiv.org/abs/2408.05772v1
• Date: Sun, 11 Aug 2024 13:40:02 GMT
• Title: An analysis of HOI: using a training-free method with multimodal visual foundation models when only the test set is available, without the training set
• Authors: Chaoyi Ai,
• Abstract summary: Under default settings, Human-Object Interaction (HOI) performance is nearly saturated. This study uses two experimental settings: grounding truth and random arbitrary combinations. We find that the open vocabulary capabilities of the multimodal visual foundation model are not yet fully realized.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human-Object Interaction (HOI) aims to identify the pairs of humans and objects in images and to recognize their relationships, ultimately forming $\langle human, object, verb \rangle$ triplets. Under default settings, HOI performance is nearly saturated, with many studies focusing on long-tail distribution and zero-shot/few-shot scenarios. Let us consider an intriguing problem:``What if there is only test dataset without training dataset, using multimodal visual foundation model in a training-free manner? '' This study uses two experimental settings: grounding truth and random arbitrary combinations. We get some interesting conclusion and find that the open vocabulary capabilities of the multimodal visual foundation model are not yet fully realized. Additionally, replacing the feature extraction with grounding DINO further confirms these findings.

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