From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models
(2310.08825)Abstract
Multi-modal LLMs (MLLMs) have made significant strides in expanding the capabilities of LLMs through the incorporation of visual perception interfaces. Despite the emergence of exciting applications and the availability of diverse instruction tuning data, existing approaches often rely on CLIP or its variants as the visual branch, and merely extract features from the deep layers. However, these methods lack a comprehensive analysis of the visual encoders in MLLMs. In this paper, we conduct an extensive investigation into the effectiveness of different vision encoders within MLLMs. Our findings reveal that the shallow layer features of CLIP offer particular advantages for fine-grained tasks such as grounding and region understanding. Surprisingly, the vision-only model DINO, which is not pretrained with text-image alignment, demonstrates promising performance as a visual branch within MLLMs. By simply equipping it with an MLP layer for alignment, DINO surpasses CLIP in fine-grained related perception tasks. Building upon these observations, we propose a simple yet effective feature merging strategy, named COMM, that integrates CLIP and DINO with Multi-level features Merging, to enhance the visual capabilities of MLLMs. We evaluate COMM through comprehensive experiments on a wide range of benchmarks, including image captioning, visual question answering, visual grounding, and object hallucination. Experimental results demonstrate the superior performance of COMM compared to existing methods, showcasing its enhanced visual capabilities within MLLMs.
Overview
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The paper evaluates the effectiveness of visual encoders such as CLIP and DINO in Multi-modal LLMs (MLLMs) and introduces a new feature merging strategy called COMM.
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Through extensive experiments, the researchers demonstrate that shallow-layer features of visual encoders provide advantages for fine-grained perception tasks and that COMM significantly enhances visual capabilities by merging features from CLIP and DINO.
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COMM's performance is validated across various benchmarks, showing improvements in tasks like image captioning, visual question answering, and object hallucination, highlighting both the theoretical and practical implications for future research and applications.
An Analysis of "From CLIP to DINO: Visual Encoders Shout in Multi-modal LLMs"
The paper "From CLIP to DINO: Visual Encoders Shout in Multi-modal LLMs" explores the effectiveness of different visual encoders in Multi-modal LLMs (MLLMs). Authored by Dongsheng Jiang et al., this research conducts an in-depth examination of visual encoders such as CLIP and DINO within the context of MLLMs and introduces a novel feature merging strategy, COMM, to enhance the visual perception capabilities of these models.
Key Findings and Contributions
1. Evaluation of Visual Encoders
The study begins by scrutinizing the most commonly used visual encoders in MLLMs, primarily focusing on CLIP and its deep-layer features. The authors argue that existing methods leveraging only the deep features of CLIP may overlook the granularity captured by shallow layers. Through comprehensive experiments, it is demonstrated that shallow-layer features indeed hold significant advantages for fine-grained perception tasks such as grounding and positioning. Surprisingly, DINO, a vision-only model devoid of text-image alignment pretraining, exhibits promising performance on fine-grained tasks when equipped with a Multi-Layer Perceptron (MLP) layer for feature alignment.
2. Proposed COMM Strategy
Building upon the observations from the visual encoders evaluation, the authors propose a novel feature merging strategy called COMM (Combination of Multi-level features Merging). COMM merges the features of CLIP and DINO, capitalizing on the fine-grained localization information from DINO and the global semantic understanding from CLIP. This strategy is designed to enhance the overall visual capabilities of MLLMs, resulting in noticeable improvements across diverse vision-language benchmarks such as image captioning, visual question answering, visual grounding, and object hallucination.
3. Extensive Experimental Validation
COMM's effectiveness and superiority are validated through rigorous experimental evaluations on various benchmarks:
- Referring Expression Comprehension (REC): COMM achieves significant performance gains, outperforming state-of-the-art generalist VL models and even some specialist models that are fine-tuned for localization tasks.
- Referring Expression Generation (REG): COMM demonstrates enhanced regional understanding, achieving higher CIDEr scores compared to previous methods.
- Object Hallucination Benchmark: The proposed model effectively mitigates the object hallucination problem, showing a higher accuracy compared to other MLLMs.
- Visual Question Answering and Image Captioning: COMM exhibits state-of-the-art performance on VQAv2, OK-VQA, COCO, and Flickr30k benchmarks, underscoring its improved fine-grained visual capabilities.
Theoretical and Practical Implications
The theoretical implications of this research highlight the importance of considering both low-level and high-level features in visual encoders for MLLMs. By demonstrating the effectiveness of DINO's fine-grained features and the merits of multi-level feature merging, the paper provides a fresh perspective on enhancing visual encodings in MLLMs. The insights garnered could lead to the development of more robust and accurate multi-modal models, influencing future research in extending these principles to other visual encoders.
On a practical level, the improved performance of COMM in numerous vision-language tasks hints at potential applications in areas requiring precise visual understanding and interpretation, such as autonomous driving, robotic vision, and advanced human-computer interaction systems.
Future Directions
The research opens several avenues for future exploration. Continuing the investigation into more powerful visual models could unveil additional methods for enhancing the visual branches of MLLMs. Moreover, extending the current evaluation setup to include a broader range of tasks and datasets could provide further insights into the generalizability and robustness of the proposed methods. Future work might also explore optimizing the training processes for even more efficient alignment between visual and linguistic features.
Conclusion
The paper "From CLIP to DINO: Visual Encoders Shout in Multi-modal LLMs" makes significant strides in evaluating and improving the visual branches of MLLMs. The proposal of COMM and its demonstrated effectiveness across multiple benchmarks underscores the potential of integrating diverse visual features for enhanced performance in multi-modal tasks. This work lays the groundwork for more advanced and capable multi-modal models, offering a comprehensive framework for future research and practical implementations in the field.
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