EXPLORATION INTO TRANSLATION-EQUIVARIANT IMAGE QUANTIZATION
Woncheol Shin (Korea Advanced Institute of Science and Technology, KAIST); Gyubok Lee (KAIST); Jiyoung Lee (KAIST); Eunyi Lyou (Seoul national university); Joonseok Lee (Google Research & Seoul National University); Edward Choi (KAIST)
-
SPS
IEEE Members: $11.00
Non-members: $15.00
This is an exploratory study that discovers the current image quantization (vector quantization) do not satisfy translation equivariance in the quantized space due to aliasing. Instead of focusing on anti-aliasing, we propose a simple yet effective way to achieve translation-equivariant image quantization by enforcing orthogonality among the codebook embeddings. To explore the advantages of translation-equivariant image quantization, we conduct three proof-of-concept experiments with a carefully controlled dataset: (1) text-to-image generation, where the quantized image indices are the target to predict, (2) image-to-text generation, where the quantized image indices are given as a condition, (3) using a smaller training set to analyze sample efficiency. From the strictly controlled experiments, we empirically verify that the translation-equivariant image quantizer improves not only sample efficiency but also the accuracy over VQGAN up to +11.9% in text-to-image generation and +3.9% in image-to-text generation.