The Vision-Language Alignment Problem
Trace one image-caption pair through cosine scores, contrastive positives and negatives, a trainable projection, and retrieval evaluation.
Explore machine learning concepts related to Vision Language. Clear explanations and practical insights.
Trace one image-caption pair through cosine scores, contrastive positives and negatives, a trainable projection, and retrieval evaluation.
Measure the centroid gap between normalized image and text embeddings, then check why a smaller gap alone does not guarantee correct retrieval.
Explore a fixed compute budget with Chinchilla’s text-only loss fit, then see why multimodal scaling needs its own data, objectives, and evaluation metrics.
Trace frozen weights and trainable LoRA matrices, calculate adapter parameter counts, and compare bottleneck adapters, prefix tuning and BitFit for vision-language models.
CLIP, BLIP-2, LLaVA and Flamingo align images with text by different routes. Compare their objectives, bridges, data, and which parts train or stay frozen.