HyperLLaVA: Improving Multimodal Language Models with Language and Dynamic Visualization Experts

Large language models (LLMs) have demonstrated remarkable versatility in handling various language-centric applications. To extend their capabilities to multimodal inputs, multimodal large language models (MLLMs) have gained significant attention. These models are crucial for developing flexible general-purpose assistants that can understand information from various modalities, including text, images, videos, and audio.

Contemporary MLLMs, such as LLaVA, typically follow a two-stage training protocol: (1) Vision-language alignment, where a static projector is trained to synchronize visual features with the word embedding space of the language model, allowing the LLM to understand the visual content; and 2) Multimodal instruction settingwhere the LLM adjusts to multimodal instruction data to improve its ability to respond to various user requests involving visual content.

Despite the critical importance of these two stages, the projector structure and adjustment strategy of the LLM have been relatively little explored. Most existing research focuses on extending pre-training data, instruction-following data, visual encoders, or language models. However, the model learned with static parameters may limit the potential to handle various multimodal tasks.

To address this limitation, researchers have proposed hyperLLaVAa dynamic version of LLaVA that benefits from a carefully designed expert module derived from HyperNetworks, as illustrated in Figure 2. This expert module generates dynamic parameters based on the input information, allowing the model to adaptively adjust both the projector and LLM layers to improve reasoning skills in various multimodal tasks.

HyperLLaVA is trained in two steps:

1. In vision-language alignment, the projector is divided into static layers (the original MLP in LLaVA) and dynamic layers (visual expert). Parameters for static layers are fixed, while parameters for dynamic layers are dynamically generated based on visual input. The visual expert, leveraging HyperNetworks, helps the static projector learn a specific visual projector that adaptively models visual features according to the visual guide. This approach allows the projector to deliver adaptive visual tokens to the semantic space of the language.
2. In the multimodal instruction setting stage, the LLM is equipped with a language expert, which models dynamic parameters for the LLM blocks. The intermediate outcome of the LLM is considered a linguistic guide that guides the language expert to provide a better understanding specific to the instruction of the user's request. By generating unique parameters for each input, MLLM increases its flexibility, allowing you to use similarities between samples in data sets and avoid possible interference between samples within the same data set.

The proposed language expert serves as an efficient parameter tuning approach for MLLM, yielding comparable performance to the original LLaVA while improving the model's ability to handle various multimodal tasks.

In their experiments, the researchers evaluated HyperLLaVA on multiple data sets, including five VQA data sets (VQAv2, GQA, VizWiz, SQAI, and VQAT) and seven benchmark toolkits (POPE, MME, MMB, MMBCN, SEED, LLaVAW and MM-Vet). . The results shown in Table 1 demonstrate that HyperLLaVA outperforms existing state-of-the-art approaches, including larger MLLMs with billions of trainable parameters, in almost all multimodal scenarios on these benchmarks.. The carefully designed, lightweight visual and linguistic experts enable the static projector and LLM to facilitate different multimodal tasks, outperforming the original LLaVA in 11 out of 12 benchmarks.

In conclusion, HyperLLaVA's dynamic and innovative tuning strategy paves the way for advances in multimodal learning systems. By adaptively adjusting projector and LLM parameters and integrating dynamic language and vision experts, researchers have introduced a parameter-efficient methodology that outperforms existing performance benchmarks. This approach offers a new horizon for improving multimodal task performance through dynamic and personalized adjustments, potentially opening new avenues to understand and integrate multimodal information more seamlessly.

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