FlashVLM: Text-Guided Visual Token Selection for Large Multimodal Models
This addresses efficiency bottlenecks for users of large multimodal models, offering a novel method for token reduction that improves performance trade-offs.
The paper tackles the problem of high computational cost and redundancy in large vision-language models by proposing FlashVLM, a text-guided visual token selection framework that dynamically adapts visual inputs to queries, achieving up to 77.8% token pruning on LLaVA 1.5 while slightly surpassing the unpruned baseline and maintaining 92.8% accuracy under 94.4% compression.
Large vision-language models (VLMs) typically process hundreds or thousands of visual tokens per image or video frame, incurring quadratic attention cost and substantial redundancy. Existing token reduction methods often ignore the textual query or rely on deep attention maps, whose instability under aggressive pruning leads to degraded semantic alignment. We propose FlashVLM, a text guided visual token selection framework that dynamically adapts visual inputs to the query. Instead of relying on noisy attention weights, FlashVLM computes an explicit cross modal similarity between projected image tokens and normalized text embeddings in the language model space. This extrinsic relevance is fused with intrinsic visual saliency using log domain weighting and temperature controlled sharpening. In addition, a diversity preserving partition retains a minimal yet representative set of background tokens to maintain global context. Under identical token budgets and evaluation protocols, FlashVLM achieves beyond lossless compression, slightly surpassing the unpruned baseline while pruning up to 77.8 percent of visual tokens on LLaVA 1.5, and maintaining 92.8 percent accuracy even under 94.4 percent compression. Extensive experiments on 14 image and video benchmarks demonstrate that FlashVLM delivers state of the art efficiency performance trade offs while maintaining strong robustness and generalization across mainstream VLMs.