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About this paper
Nahid Alam, Leema Krishna Murali, Siddhant Bharadwaj, Patrick Liu, Timothy Chung, Drishti Sharma, Akshata A, Kranthi Kiran and 2 more
An investigation into the "spatial blindspot" of vision-language models (VLMs) reveals that their ability to understand spatial relationships is significantly influenced by image encoder choice and the integration of 2D positional encodings. Using the LLaVA framework, the study shows that the AIMv2 encoder generally improves spatial reasoning across several benchmarks, and 2D-RoPE offers advantages in specific relational reasoning tasks.
Key insights
- The choice of image encoder, particularly its pretraining objective, fundamentally impacts a VLM's spatial reasoning ability, with dense feature learning (e.g., AIMv2) being crucial for fine-grained tasks.
- Explicitly preserving 2D structural information via methods like 2D-RoPE can enhance spatial reasoning, but its effectiveness varies depending on the underlying image encoder architecture.
- Image encoders optimized primarily for global semantic alignment (like CLIP) exhibit inherent limitations in fine-grained spatial understanding, even with added 2D positional cues.
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