Researcher explains how to build applied multimodal projects: own datasets, metrics, models
mariyaivasileva · x · 2026-09-21
Responding to a question about breadth vs. depth, researcher Mariya Ivasileva walks through her applied vision-language project: since no off-the-shelf solution existed, she sourced her own dataset, defined and validated her own metrics, and proposed her own model. Her approach: decompose the problem step by step — how to represent abstract visual relationships, learn embeddings that encode them, find where they fall short, and improve efficiency — with the goal of a solution that satisfies the application's constraints rather than deep coverage of every subproblem.
More from Research
- HF's Merve Noyan shares beginner guides for zero-shot classifiers, says many LLM tasks never needed them — ceciletamura · 2026-09-21
- Two independent results suggest multimodal models still need SSL backbone features — kalomaze · 2026-09-21
- Researchers formally verify the Kubernetes control plane with a compositional CORE spec — tianyin_xu · 2026-09-21
- Astra shows any 3D/4D prior can be distilled into VLMs, a new embodied AI paradigm — mariyaivasileva · 2026-09-21
- ReCouPLe: Reason-Augmented Preference Learning Boosts Reward Accuracy 1.5x Under Shift — burny_tech · 2026-09-21
- ModernBERT's 8192 context: 18 of 28 layers are actually 128-token sliding windows — AIQuanting · 2026-09-21