DiViCo: Disentangled Visual Token Compression for Efficient Large Vision-Language Model

Large Vision-Language Models have drawn much attention and become increasingly applicable in complicated multimodal tasks such as visual question answering, video grounding, etc. However, it still suffers from inefficiency problem during the inference stage due to the computational overhead brought by the large number of visual tokens. Existing works either utilize an attention score (or visual-text relevance) to filter out the less significant visual tokens, or insert learnable projection layers to directly compress the tokens, which neglects the informative details in visual signals and introduces information loss, resulting in poor generalizability to test data. To solve these problems, in this paper we propose a novel Disentangled Visual Token Compression module, i.e., DiViCo, that effectively compresses the visual tokens and maintains good performance simultaneously. In concrete, we first select the top τ% visual tokens according to their average attention scores, then predict the gap between these selected tokens and the original information by employing the chosen tokens in a disentangled and variational manner. Specifically, we model the mean and variance, sampling the predicted gap from the Gaussian prior. We further keep the informativeness of the compressed visual tokens via KL divergence, which ensures the generalizability of the model. Extensive experiments demonstrate the advantage of our proposed DiViCo module against several state-of-the-art baselines over various real-world datasets. Most notably, LLaVA-v1.5-7b equipped with DiViCo is able to reduce 67.7% FLOPs and save 51.7% time while maintaining 95.6% of the accuracy for LLaVA-v1.5-7b without any compression.