6 citations · 15 across the 12 of their papers we have counts for
13 papers
Fast and Efficient Approximate Nearest Neighbor Search for High-Dimensional LLM Embeddings
Nico Hezel, Kai Uwe Barthel, Bruno Schilling +3
The annual SISAP Indexing Challenge benchmarks Approximate Nearest Neighbor Search (ANNS) algorithms under rigorous constraints. This paper presents our submissions for the 2026 ed…
Dynamic Exploration Graph: A Novel Approach for Efficient Nearest Neighbor Search in Evolving Multimedia Datasets
Nico Hezel, Kai Uwe Barthel, Bruno Schilling +2
Approximate Nearest Neighbor Search (ANNS) represents a fundamental problem in various applications (image-search, recommendation systems). While graph-based algorithms have demons…
An Exploration Graph with Continuous Refinement for Efficient Multimedia Retrieval
Nico Hezel, Kai Uwe Barthel, Konstantin Schall +1
As datasets and the dimensionality of feature vectors continue to grow, Approximate Nearest Neighbor Search (ANNS) in large multimedia databases becomes increasingly relevant. Grap…
Creating Sorted Grid Layouts with Gradient-based Optimization
Kai Uwe Barthel, Florian Tim Barthel, Peter Eisert +2
Visually sorted grid layouts provide an efficient method for organizing high-dimensional vectors in two-dimensional space by aligning spatial proximity with similarity relationship…
Permutation Learning with Only N Parameters: From SoftSort to Self-Organizing Gaussians
Kai Uwe Barthel, Florian Barthel, Peter Eisert
Sorting and permutation learning are key concepts in optimization and machine learning, especially when organizing high-dimensional data into meaningful spatial layouts. The Gumbel…
Optimizing CLIP Models for Image Retrieval with Maintained Joint-Embedding Alignment
Konstantin Schall, Kai Uwe Barthel, Nico Hezel +1
Contrastive Language and Image Pairing (CLIP), a transformative method in multimedia retrieval, typically trains two neural networks concurrently to generate joint embeddings for t…