Content for workshops on computer vision @ HPI's AI Service Center

Harnessing Large Language Models for Curated Code Reviews

Implementation of TSDS: Data Selection for Task-Specific Model Finetuning. An optimal-transport framework for selecting domain-specific and task-specific training data to improve LLM finetuning and instruction tuning.

HyperView curates datasets and provides model introspection in hyperbolic and Euclidean geometries.

[ACL 2024 (Findings)] ICC: Quantifying Image Caption Concreteness for Multimodal Dataset Curation

Image description/tagging tool

NAACL 2025 | How Can We Diagnose and Treat Bias in Large Language Models for Clinical Decision-Making?

An image deduplication GUI, made for image generation models dataset deduplication using CLIP.

Manage and process paired RGB and depth images, with options to view, export, and exclude images using various colormaps.

Biomedical Image Processing BAP (Scientific Research Project) - Piri Reis University

Dynamic cluster-based data sampling for efficient and long-tail-aware vision-language model pre-training.

AIWG training-complete framework — corpus-to-dataset pipeline with SKILL.md agentic surface and optional Python runtime backend. Marketplace plugin for AIWG.

Comprehensive framework for curating and validating biomedical datasets for clinical AI applications

Pipeline for querying and turning NASA's ADS publications metadata into curated, analysis-ready datasets, topic maps, and citation networks.

A dataset curator for lora training for person loras

Two-stage video captioning pipeline: a Vision-Language model produces a rich description, then a text-only LM rewrites it through a task-specific prompt (e.g. for LoRA training datasets, retrieval, summarization).

Workflow and validation toolkit for human review of wildlife AI outputs

ML/AI Data Curation Functional Setup

End-to-end object detection project for telecom infrastructure using real-world field data with challenging conditions such as rust, occlusion, and adverse weather

A local-first self-improvement runtime for language systems. Record. Learn. Rewrite.