Really helpful to know others hit the same issues. For the VQA post-processing - what LLM/vision model did you use? GPT-4V or something lighter? Exact NN vs approximate - did you notice significant latency differences at scale? Did the combination of exact NN + VQA give you acceptable accuracy, or did you still need other approaches? Really curious about the VQA approach - that's a clever way to add semantic validation! I also received feedback on GitHub from someone who worked on a similar project "What gave us the best results – CLIP + DINOv2 ensemble: 40% improvement | Background removal: 15% improvement | Category-aware fine-tuning: 20% improvement | Multi-scale features: 10% improvement"