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Poster
CyCLIP: Cyclic Contrastive Language-Image Pretraining
Shashank Goel · Hritik Bansal · Sumit Bhatia · Ryan Rossi · Vishwa Vinay · Aditya Grover

Tue Nov 29 02:00 PM -- 04:00 PM (PST) @ Hall J #609
in Poster Session 2 »

Recent advances in contrastive representation learning over paired image-text data have led to models such as CLIP that achieve state-of-the-art performance for zero-shot classification and distributional robustness. Such models typically require joint reasoning in the image and text representation spaces for downstream inference tasks. Contrary to prior beliefs, we demonstrate that the image and text representations learned via a standard contrastive objective are not interchangeable and can lead to inconsistent downstream predictions. To mitigate this issue, we formalize consistency and propose CyCLIP, a framework for contrastive representation learning that explicitly optimizes for the learned representations to be geometrically consistent in the image and text space. In particular, we show that consistent representations can be learned by explicitly symmetrizing (a) the similarity between the two mismatched image-text pairs (cross-modal consistency); and (b) the similarity between the image-image pair and the text-text pair (in-modal consistency). Empirically, we show that the improved consistency in CyCLIP translates to significant gains over CLIP, with gains ranging from 10%-24% for zero-shot classification on standard benchmarks (CIFAR-10, CIFAR-100, ImageNet1K) and 10%-27% for robustness to various natural distribution shifts.

Keywords: Representation Learning · zero-shot transfer · robustness · Multimodal Learning · CLIP · Language-Image Pretraining · Cyclic Consistency · Contrastive

Author Information

Shashank Goel (University of California, Los Angeles)
Hritik Bansal (University of California, Los Angeles (UCLA))
Sumit Bhatia (MDSR Lab, Adobe Systems)
Ryan Rossi (Purdue University)
Vishwa Vinay (Adobe Research)
Aditya Grover (University of California, Los Angeles)

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