LEASE achieves state-of-the-art unified performance on ImageNet-1K by combining masked token reconstruction and codebook contrast losses in a one-time precomputed discrete token space.
MergeVQ: A Uni- fied Framework for Visual Generation and Representation with Disentangled Token Merging and Quantization, 2025
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Learning from Semantic Dictionaries: Discriminative Codebook Contrastive Learning for Unified Visual Representation and Generation
LEASE achieves state-of-the-art unified performance on ImageNet-1K by combining masked token reconstruction and codebook contrast losses in a one-time precomputed discrete token space.