Abstract
We introduce Perception Encoder (PE), a state-of-the-art encoder for image and video understanding trained via simple vision-language learning. Traditionally, vision encoders have relied on a variety of pretraining objectives, each tailored to specific downstream tasks such as classification, captioning, or localization. Surprisingly, after scaling our carefully tuned image pretraining recipe and refining with our robust video data engine, we find that contrastive vision-language training alone can produce strong, general embeddings for all of these downstream tasks. There is only one caveat: these embeddings are hidden within the intermediate layers of the network. To draw them out, we introduce two alignment methods, language alignment for multimodal language modeling, and spatial alignment for dense prediction. Together with the core contrastive checkpoint, our PE family of models achieves state-of-the-art performance on a wide variety of tasks, including zero-shot image and video classification and retrieval; document, image, and video Q&A; and spatial tasks such as detection, depth estimation, and tracking. To foster further research, we are releasing our models, code, and a novel dataset of synthetically and human-annotated videos.
Abstract (translated)
我们介绍了一种先进的感知编码器（PE），这是一种通过简单视觉-语言学习训练出来的图像和视频理解的编码器。传统上，视觉编码器依赖于一系列用于特定下游任务如分类、描述或定位的预训练目标。令人惊讶的是，在扩展了我们精心调整的图像预训练方案并用我们的稳健视频数据引擎进行微调后，我们发现仅通过对比式视觉-语言训练就能产生适用于所有这些下游任务的强大且通用的嵌入表示。唯一的不足是：这些嵌入隐藏在网络中间层中。 为了提取它们，我们引入了两种对齐方法：多模态语言模型的语言对齐和密集预测的空间对齐。结合核心对比检查点，我们的PE家族模型在广泛的任务上取得了最先进的性能，包括零样本图像和视频分类及检索；文档、图像和视频问答；以及空间任务如检测、深度估计和跟踪。 为了促进进一步的研究，我们将发布我们的模型、代码以及一套新颖的合成和人工标注视频数据集。
URL
https://arxiv.org/abs/2504.13181
