Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners (2205.10747v4)

Abstract: The goal of this work is to build flexible video-LLMs 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 LLMs, 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-LLMs 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 LLM, 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 LLMs 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 .

Overview of LLMs with Image Descriptors as Few-Shot Video-Language Learners

The paper "LLMs with Image Descriptors are Strong Few-Shot Video-Language Learners" introduces a novel approach to video-to-text tasks by leveraging pre-trained image-language and LLMs in a few-shot setting. The researchers present a model that excels in various video-language tasks such as video captioning, video question answering, video caption retrieval, and video future event prediction, without the need for extensive pretraining or finetuning on video datasets.

Key Contributions and Methodology

The primary contribution of this work is the integration of image descriptors with LLMs to enhance video-to-text task performance. Unlike previous approaches that primarily focus on encoder-only frameworks, the proposed method utilizes a novel decomposition strategy to convert video content into text-based forms. This involves:

1. Image-Language Translation: Employing image-LLMs to extract frame captions, objects, attributes, and events from video frames. This information is composed into a temporal template that captures the sequential nature of video content.
2. Temporal-Aware Prompting: Introducing temporal-aware prompts fed into a LLM, such as InstructGPT, enabling it to generate coherent video-based summaries or answers. This method leverages the prompt flexibility to include various text inputs, such as ASR transcripts, without requiring model retraining.
3. Strong Few-Shot Performance: Demonstrating that their framework substantially outperforms state-of-the-art supervised models on video future event prediction using only ten labeled examples. This suggests the potential for significant generalization capabilities inherent in the proposed model.

Experimental Findings

The experimental evaluation on datasets like MSR-VTT, MSVD, VaTeX, YouCook2, and VLEP shows strong numerical results:

• On tasks such as video future event prediction, the approach exceeds existing state-of-the-art models, emphasizing the effectiveness of integrating temporal context into the model's architecture.
• The framework's ability to adapt to video-to-text generation tasks quickly highlights its competitive advantage against heavily finetuned models.

These results underline the potent capabilities of the model in addressing complex cross-modal tasks with limited example data.

Implications and Future Directions

From a practical standpoint, this approach reduces the dependency on large-scale annotations and pretraining video datasets, offering a cost-effective and scalable solution for video-language understanding tasks. Theoretically, it advances the intersection of image processing and LLMs, paving the way for more dynamic and flexible AI systems capable of multitasking within multimodal contexts.

The integration of effective prompting strategies with hierarchical representations of video content highlights a critical trajectory for future research, where improving the human-like temporal reasoning in AI systems could be pivotal. Enhanced capabilities in temporal understanding will improve applications ranging from interactive AI systems to content moderation in dynamic media.

Future developments may include exploring alternative strategies for integrating other modalities, such as audio cues beyond ASR transcripts, expanding the model's applicability in comprehensive video analysis and scene understanding. Further exploration into more fine-grained temporal modeling techniques and their alignment with human narrative understanding in AI could continue to enrich advancements in few-shot video-language learning.