CLIPCAM: A Simple Baseline For Zero-Shot Text-Guided Object And Action Localization

Abstract

The key for the contemporary deep learning-based object and action localization algorithms to work is the large-scale annotated data. However, in real-world scenarios, since there are infinite amounts of unlabeled data beyond the categories of publicly available datasets, it is not only time- and manpower-consuming to annotate all the data but also requires a lot of computational resources to train the detectors. To address these issues, we show a simple and reliable baseline that can be easily obtained and work directly for the zero-shot text-guided object and action localization tasks without introducing additional training costs by using Grad-CAM, the widely used class visual saliency map generator, with the help of the recently released Contrastive Language-Image Pre-Training (CLIP) model by OpenAI, which is trained contrastively using the dataset of 400 million image-sentence pairs with rich cross-modal information between text semantics and image appearances. With extensive experiments on the Open Images and HICO-DET datasets, the results demonstrate the effectiveness of the proposed approach for the text-guided unseen object and action localization tasks for images.