Images are Worth Variable Length of Representations
Most existing vision encoders map images into a fixed-length sequence of
tokens, overlooking the fact that different images contain varying amounts of
information. For example, a visually complex image (e.g., a cluttered room)
inherently carries more information and thus deserves more tokens than a simple
image (e.g., a blank wall). To address this inefficiency, we propose DOVE, a
dynamic vision encoder that produces a variable number of visual tokens (i.e.,
continuous representation vectors) to reconstruct each image. Our results show
that DOVE significantly reduces the average number of tokens while maintaining
high reconstruction quality. In several linear probing and downstream
multimodal tasks, it outperforms existing autoencoder-based tokenization
methods when using far fewer tokens, capturing more expressive semantic
features compared to fixed-length encoding. We further extend DOVE with
query-conditioned tokenization. By guiding the model to focus on query-relevant
regions, it achieves more efficient and targeted semantic extraction. Our code
and checkpoints are available at https://dove-encoder.github.io/dove-encoder.
6842d9cdc18e2d27141f82cd
Lingjun Mao
6842d9cdc18e2d27141f82ce
Rodolfo Corona
6842d9cdc18e2d27141f82cf
Xin Liang
6842d9cdc18e2d27141f82d0
Wenhao Yan
6842d9cdc18e2d27141f82d1
Zineng Tang
Images are Worth Variable Length of Representations
Most existing vision encoders map images into a fixed-length sequence of
tokens, overlooking the fact that different images contain varying amounts of
information. For example, a visually complex image (e.g., a cluttered room)
inherently carries more information and thus deserves more tokens than a simple
image (e.g., a blank wall). To address this inefficiency, we propose DOVE, a
dynamic vision encoder that produces a variable number of visual tokens (i.e.,
continuous representation vectors) to reconstruct each image. Our results show
that DOVE significantly reduces the average number of tokens while maintaining
high reconstruction quality. In several linear probing and downstream
multimodal tasks, it outperforms existing autoencoder-based tokenization
methods when using far fewer tokens, capturing more expressive semantic
features compared to fixed-length encoding. We further extend DOVE with
query-conditioned tokenization. By guiding the model to focus on query-relevant
regions, it achieves more efficient and targeted semantic extraction. Our code
and checkpoints are available at https://dove-encoder.github.io/dove-encoder.
A dynamic vision encoder, DOVE, generates a variable number of visual tokens for images, improving reconstruction quality and efficiency in multimodal tasks.
vision encoders tokens continuous representation vectors dynamic vision encoder reconstruction quality linear probing downstream multimodal tasks autoencoder-based tokenization query-conditioned tokenization semantic extraction