Img2Vec: A Teacher of High Token-Diversity Helps Masked AutoEncoders
This work addresses the challenge of improving self-supervised learning in computer vision by identifying token-diversity as a key factor, offering incremental but practical gains for researchers and practitioners in the field.
The paper tackles the problem of selecting effective deep features for masked image modeling (MIM) by introducing Img2Vec, a pipeline that uses a teacher model with high token-diversity to generate features, resulting in top-1 accuracy of 85.1% on ImageNet fine-tuning and state-of-the-art results on downstream tasks like COCO and ADE20K.
We present a pipeline of Image to Vector (Img2Vec) for masked image modeling (MIM) with deep features. To study which type of deep features is appropriate for MIM as a learning target, we propose a simple MIM framework with serials of well-trained self-supervised models to convert an Image to a feature Vector as the learning target of MIM, where the feature extractor is also known as a teacher model. Surprisingly, we empirically find that an MIM model benefits more from image features generated by some lighter models (e.g., ResNet-50, 26M) than from those by a cumbersome teacher like Transformer-based models (e.g., ViT-Large, 307M). To analyze this remarkable phenomenon, we devise a novel attribute, token diversity, to evaluate the characteristics of generated features from different models. Token diversity measures the feature dissimilarity among different tokens. Through extensive experiments and visualizations, we hypothesize that beyond the acknowledgment that a large model can improve MIM, a high token-diversity of a teacher model is also crucial. Based on the above discussion, Img2Vec adopts a teacher model with high token-diversity to generate image features. Img2Vec pre-trained on ImageNet unlabeled data with ViT-B yields 85.1\% top-1 accuracy on fine-tuning. Moreover, we scale up Img2Vec on larger models, ViT-L and ViT-H, and get $86.7\%$ and $87.5\%$ accuracy respectively. It also achieves state-of-the-art results on other downstream tasks, e.g., 51.8\% mAP on COCO and 50.7\% mIoU on ADE20K. Img2Vec is a simple yet effective framework tailored to deep feature MIM learning, accomplishing superb comprehensive performance on representative vision tasks.