Vision Language Models (VLMs) are crucial for understanding images via natural language instructions. Current VLMs struggle with fine-grained object comprehension, impacting their performance. CoLLaVO, developed by KAIST, integrates language and vision capabilities to enhance object-level image understanding and achieve superior zero-shot performance on vision language tasks, marking a significant breakthrough.
The Importance of Object-Level Image Understanding in Vision Language Models
The evolution of Vision Language Models (VLMs) towards general-purpose models relies on their ability to understand images and perform tasks via natural language instructions. However, it must be clarified if current VLMs truly grasp detailed object information in images. The analysis shows that their image understanding correlates strongly with zero-shot performance on vision language tasks. It suggests that prioritizing basic image understanding is key for VLMs to excel. Despite recent advancements, leading VLMs still struggle with fine-grained object comprehension, impacting their performance on related tasks. Improving VLMs’ object-level understanding is essential for enhancing their overall task performance.
CoLLaVO: Advancing Object-Level Image Understanding in Vision Language Models
Researchers from KAIST have developed CoLLaVO, a model merging language and vision capabilities to improve object-level image understanding. By introducing the Crayon Prompt, which utilizes panoptic color maps to guide attention to objects, and employing Dual QLoRA to balance learning from crayon instructions and visual prompts, CoLLaVO achieves substantial advancements in zero-shot vision language tasks. This innovative approach maintains object-level understanding while enhancing complex task performance. CoLLaVO-7B demonstrates superior zero-shot performance compared to existing models, marking a significant stride in effectively bridging language and vision domains.
CoLLaVO’s Innovative Architecture
CoLLaVO’s architecture integrates a vision encoder, Crayon Prompt, backbone MLM, and MLP connectors. The vision encoder, CLIP, aids in image understanding, while the MLM, InternLM-7B, supports multilingual instruction tuning. The Crayon Prompt, generated from a panoptic color map, incorporates semantic and numbering queries to represent objects in the image. Crayon Prompt Tuning (CPT) aligns this prompt with the MLM to enhance object-level understanding. Crayon Prompt-based Instruction Tuning (CIT) leverages visual instruction tuning datasets and crayon instructions for complex VL tasks. Dual QLoRA manages object-level understanding and VL performance during training to maintain both capabilities effectively.
Impact and Significance
The image understanding capabilities of current VLMs were found to be strongly correlated with their zero-shot performance on vision language tasks. It suggests that prioritizing basic image understanding is crucial for VLMs to excel at vision language tasks. The CoLLaVO incorporates instruction tuning with Crayon Prompt, a visual prompt tuning scheme based on panoptic color maps. CoLLaVO achieved a significant leap in numerous vision language benchmarks in a zero-shot setting, demonstrating enhanced object-level image understanding. The study mentions commendable scores achieved across all zero-shot tasks, indicating the model’s effectiveness.
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