  Exploring large-scale pretrained foundation models is of significant interest
in computer vision because these models can be quickly transferred to many
downstream tasks. This paper presents Contrastive Captioner (CoCa), a
minimalist design to pretrain an image-text encoder-decoder foundation model
jointly with contrastive loss and captioning loss, thereby subsuming model
capabilities from contrastive approaches like CLIP and generative methods like
SimVLM. In contrast to standard encoder-decoder transformers where all decoder
layers attend to encoder outputs, CoCa omits cross-attention in the first half
of decoder layers to encode unimodal text representations, and cascades the
remaining decoder layers which cross-attend to the image encoder for multimodal
image-text representations. We apply a contrastive loss between unimodal image
and text embeddings, in addition to a captioning loss on the multimodal decoder
outputs which predicts text tokens autoregressively. By sharing the same
computational graph, the two training objectives are computed efficiently with
minimal overhead. CoCa is pretrained end-to-end and from scratch on both
web-scale alt-text data and annotated images by treating all labels simply as
text, seamlessly unifying natural language supervision for representation
learning. Empirically, CoCa achieves state-of-the-art performance with
zero-shot transfer or minimal task-specific adaptation on a broad range of
downstream tasks, spanning visual recognition (ImageNet, Kinetics-400/600/700,
Moments-in-Time), crossmodal retrieval (MSCOCO, Flickr30K, MSR-VTT), multimodal
understanding (VQA, SNLI-VE, NLVR2), and image captioning (MSCOCO, NoCaps).
Notably on ImageNet classification, CoCa obtains 86.3% zero-shot top-1
accuracy, 90.6% with a frozen encoder and learned classification head, and new
state-of-the-art 91.0% top-1 accuracy on ImageNet with a finetuned encoder.
