aiqbench analysis
This paper addresses the computational bottleneck of Vision Transformers by introducing ReConV, a reconstruction-guided framework that compresses visual token sequences without sacrificing critical information. By retaining only tokens with high reconstruction residuals while summarizing shared data in a compact bank, the method achieves significant efficiency gains, such as reducing GMACs by 48% on ImageNet. This approach offers a practical pathway for deploying large-scale vision models in resource-constrained environments through efficient multi-turn inference.
Bottlenecked semantic compression by representation reconstruction
arXiv:2610.17412v1 Announce Type: cross Abstract: Vision Transformers incur high computational costs when processing long visual token sequences. Existing reduction methods typically rank tokens independently and discard those deemed unimportant or redundant. We propose a reconstruction-guided framework for visual token condensation (ReConV), which summarizes the complete visual sequence with a compact Summary Bank (SMB) and retains tokens whose information is not adequately captured by the SMB, as indicated by their reconstruction residuals. At each compression layer, a lightweight position-conditioned decoder reconstructs the current tokens, leaving shared information compressed in the SMB while explicitly preserving poorly reconstructed details. Experiments on image classification and multimodal understanding demonstrate favorable performance-efficiency trade-offs: ReConV surpasses the uncompressed baseline with 48\% fewer GMACs on ImageNet and enables reusable image-level representations for efficient multi-turn inference. Code is available at TokenDrop.
Abstract:Vision Transformers incur high computational costs when processing long visual token sequences. Existing reduction methods typically rank tokens independently and discard those deemed unimportant or redundant. We propose a reconstruction-guided framework for visual token condensation (ReConV), which summarizes the complete visual sequence with a compact Summary Bank (SMB) and retains tokens whose information is not adequately captured by the SMB, as indicated by their reconstruction residuals. At each compression layer, a lightweight position-conditioned decoder reconstructs the current tokens, leaving shared information compressed in the SMB while explicitly preserving poorly reconstructed details. Experiments on image classification and multimodal understanding demonstrate favorable performance-efficiency trade-offs: ReConV surpasses the uncompressed baseline with 48\% fewer GMACs on ImageNet and enables reusable image-level representations for efficient multi-turn inference. Code is available at TokenDrop.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2610.17412 [cs.CV]
(or arXiv:2610.17412v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2610.17412 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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