Unseen No More: Unlocking the Potential of CLIP for Generative Zero-shot HOI Detection

TL;DR

This paper introduces HOIGen, a generation-based model leveraging CLIP for zero-shot human-object interaction detection, significantly improving unseen class recognition by generating features and using prototype banks.

Contribution

It presents the first generation-based approach with CLIP for zero-shot HOI detection, addressing seen-unseen confusion and enhancing generalization.

Findings

HOIGen outperforms existing methods on HICO-DET benchmark.

Generates realistic features for seen and unseen classes.

Utilizes prototype banks to improve HOI scoring.

Abstract

Zero-shot human-object interaction (HOI) detector is capable of generalizing to HOI categories even not encountered during training. Inspired by the impressive zero-shot capabilities offered by CLIP, latest methods strive to leverage CLIP embeddings for improving zero-shot HOI detection. However, these embedding-based methods train the classifier on seen classes only, inevitably resulting in seen-unseen confusion for the model during inference. Besides, we find that using prompt-tuning and adapters further increases the gap between seen and unseen accuracy. To tackle this challenge, we present the first generation-based model using CLIP for zero-shot HOI detection, coined HOIGen. It allows to unlock the potential of CLIP for feature generation instead of feature extraction only. To achieve it, we develop a CLIP-injected feature generator in accordance with the generation of human, object and union features. Then, we extract realistic features of seen samples and mix them with synthetic features together, allowing the model to train seen and unseen classes jointly. To enrich the HOI scores, we construct a generative prototype bank in a pairwise HOI recognition branch, and a multi-knowledge prototype bank in an image-wise HOI recognition branch, respectively. Extensive experiments on HICO-DET benchmark demonstrate our HOIGen achieves superior performance for both seen and unseen classes under various zero-shot settings, compared with other top-performing methods. Code is available at: https://github.com/soberguo/HOIGen