OLA-VLM: Elevating Visual Perception in Multimodal LLMs with Auxiliary Embedding Distillation (2412.09585v1)

Abstract: The standard practice for developing contemporary MLLMs is to feed features from vision encoder(s) into the LLM and train with natural language supervision. In this work, we posit an overlooked opportunity to optimize the intermediate LLM representations through a vision perspective (objective), i.e., solely natural language supervision is sub-optimal for the MLLM's visual understanding ability. To that end, we propose OLA-VLM, the first approach distilling knowledge into the LLM's hidden representations from a set of target visual representations. Firstly, we formulate the objective during the pretraining stage in MLLMs as a coupled optimization of predictive visual embedding and next text-token prediction. Secondly, we investigate MLLMs trained solely with natural language supervision and identify a positive correlation between the quality of visual representations within these models and their downstream performance. Moreover, upon probing our OLA-VLM, we observe improved representation quality owing to the embedding optimization. Thirdly, we demonstrate that our OLA-VLM outperforms the single and multi-encoder baselines, proving our approach's superiority over explicitly feeding the corresponding features to the LLM. Particularly, OLA-VLM boosts performance by an average margin of up to 2.5% on various benchmarks, with a notable improvement of 8.7% on the Depth task in CV-Bench. Our code is open-sourced at https://github.com/SHI-Labs/OLA-VLM .

Analyzing "OLA-VLM: Elevating Visual Perception in Multimodal LLMs with Auxiliary Embedding Distillation"

The paper presents OLA-VLM, a novel approach aimed at enhancing visual perception in multimodal LLMs (MLLMs) through the auxiliary embedding distillation technique. This work addresses the limitations of existing MLLMs, which typically rely solely on natural language supervision for training, an approach that may not optimize the visual understanding capabilities of these models.

Core Contributions and Methodology

The authors propose a unique methodology that involves coupling the visual embedding optimization with the next text-token prediction during the pretraining stage of MLLMs. This dual-focus training objective is designed to improve the intermediate layer representations of the LLM by incorporating vision-centric information from a set of target visual encoders.

Key innovations include:

• Auxiliary Embedding Distillation: This method optimizes the hidden representations within the LLM by distilling knowledge from target visual representations, which are derived from multiple visual tasks such as image segmentation and depth estimation.
• Predictive Embedding Optimization: The approach integrates a predictive embedding loss at selected LLM layers, supplementing the traditional next-token prediction loss. This optimization is achieved through a single-layer Perceiver Resampler acting as the embedding predictor.
• Single Encoder Usage in Inference: By only utilizing a single base vision encoder during inference, OLA-VLM ensures both efficiency and improved visual understanding performance without the computational overhead of multiple encoders.

Experimental Results

The experimental evaluations indicate that OLA-VLM outperforms both single and multi-encoder baseline models across various benchmarks. Notably, OLA-VLM achieves up to an 8.7% improvement on the Depth task in CV-Bench, underscoring the efficacy of the proposed approach. The results demonstrate the model's superior visual representation capabilities, as reflected in enhanced performance metrics.

Implications and Future Directions

OLA-VLM represents a significant advancement in the training of MLLMs by highlighting the benefits of integrating visual embedding optimization with conventional LLM objectives. The findings suggest that auxiliary visual information, when distilled effectively, can substantially improve the representation quality within LLMs.

Looking to the future, there are several avenues for further exploration and refinement:

1. Broader Integration of Visual Encoders: Incorporating a wider variety of visual encoders could potentially enhance the generalization abilities of MLLMs across a broader spectrum of visual reasoning tasks.
2. Application to Video Understanding: Extending the current framework to include temporal visual data could improve spatial and temporal reasoning, thereby expanding the applicability of MLLMs to domains requiring video comprehension.
3. Exploration of Low-Level Features: Investigation into the distillation of low-level visual features could open new possibilities for improving models’ perceptual abilities in tasks like motion detection and robotic control.

In conclusion, the OLA-VLM framework provides a compelling method for enriching MLLMs through strategic embedding distillation, paving the way for future developments in multimodal AI systems. The promising results highlight the potential of vision-centric training paradigms in augmenting the capabilities of modern LLMs, setting the stage for more powerful and efficient MLLMs.