Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners
This work addresses the challenge of few-shot learning for video-language tasks, enabling generalization to unseen tasks without extensive data, which is incremental as it builds on existing image-language and language models.
The paper tackles the problem of building flexible video-language models that can generalize to various video-to-text tasks from few examples, such as captioning and question answering, by proposing VidIL, which uses image-language models to translate video content into descriptors and instructs a language model with prompts, achieving strong performance without pretraining or finetuning on video datasets and significantly outperforming state-of-the-art supervised models on video future event prediction.
The goal of this work is to build flexible video-language models that can generalize to various video-to-text tasks from few examples, such as domain-specific captioning, question answering, and future event prediction. Existing few-shot video-language learners focus exclusively on the encoder, resulting in the absence of a video-to-text decoder to handle generative tasks. Video captioners have been pretrained on large-scale video-language datasets, but they rely heavily on finetuning and lack the ability to generate text for unseen tasks in a few-shot setting. We propose VidIL, a few-shot Video-language Learner via Image and Language models, which demonstrates strong performance on few-shot video-to-text tasks without the necessity of pretraining or finetuning on any video datasets. We use the image-language models to translate the video content into frame captions, object, attribute, and event phrases, and compose them into a temporal structure template. We then instruct a language model, with a prompt containing a few in-context examples, to generate a target output from the composed content. The flexibility of prompting allows the model to capture any form of text input, such as automatic speech recognition (ASR) transcripts. Our experiments demonstrate the power of language models in understanding videos on a wide variety of video-language tasks, including video captioning, video question answering, video caption retrieval, and video future event prediction. Especially, on video future event prediction, our few-shot model significantly outperforms state-of-the-art supervised models trained on large-scale video datasets. Code and resources are publicly available for research purposes at https://github.com/MikeWangWZHL/VidIL .