المفاهيم الأساسية
Multimodal resamplers pretrained with contrastive learning and language modeling objectives do not inherently capture fine-grained spatial information, despite their effectiveness in coarse-grained vision-language tasks.
الملخص
The paper investigates the ability of multimodal resamplers to encode spatial information, which is crucial for fine-grained vision-language tasks. The authors use diagnostic classifiers to probe two different resampler modules, Q-Former from BLIP-2 and InstructBLIP, on three spatial understanding tasks: RefCOCOg, Visual Spatial Reasoning (VSR), and Region Cell Matching (RCM).
The results show that the resamplers perform poorly on these tasks when kept frozen, indicating a lack of spatial information in their representations. However, when the resamplers are fine-tuned jointly with the probing classifiers, a significant performance boost is observed, suggesting that the compression achieved by the resamplers can in principle encode the requisite spatial information.
The authors further analyze the results and find that the resamplers tend to focus more on central entities within the image, ignoring positional outliers. They also observe that the resamplers struggle the most with encoding directional and adjacency relationships, while performing better on topological relations.
The authors conclude that the pretraining objectives of the resamplers, which are primarily based on contrastive learning and language modeling, are not sufficient to facilitate fine-grained spatial understanding. They suggest that more object-aware pretraining objectives are needed to enable resamplers to better encode spatial information.
الإحصائيات
The area of the bounding box positively correlates with the performance of the frozen Q-Former on RefCOCOg.
The distance of the bounding box from the center of the image negatively correlates with the performance of the frozen Q-Former and InstructBLIP Q-Former on RefCOCOg.
اقتباسات
"Our results show that this information is largely absent from the resampler output when kept frozen during training of the classifiers."
"However, when the resampler and classifier are trained jointly, we observe a significant performance boost."
"This shows that the compression achieved by the resamplers can in principle encode the requisite spatial information, but that more object-aware objectives are needed at the pretraining stage to facilitate this capability."