1 code implementation • 15 Feb 2024 • Benedikt Alkin, Lukas Miklautz, Sepp Hochreiter, Johannes Brandstetter
The motivation behind MIM-Refiner is rooted in the insight that optimal representations within MIM models generally reside in intermediate layers.
Ranked #1 on Image Clustering on ImageNet
1 code implementation • 5 Feb 2024 • Andreas Stephan, Lukas Miklautz, Kevin Sidak, Jan Philip Wahle, Bela Gipp, Claudia Plant, Benjamin Roth
We, therefore, propose Text-Guided Image Clustering, i. e., generating text using image captioning and visual question-answering (VQA) models and subsequently clustering the generated text.
1 code implementation • 20 Apr 2023 • Johannes Lehner, Benedikt Alkin, Andreas Fürst, Elisabeth Rumetshofer, Lukas Miklautz, Sepp Hochreiter
In this work, we study how to combine the efficiency and scalability of MIM with the ability of ID to perform downstream classification in the absence of large amounts of labeled data.
Ranked #1 on Image Clustering on Imagenet-dog-15 (using extra training data)
no code implementations • 13 Oct 2022 • Lukas Miklautz, Martin Teuffenbach, Pascal Weber, Rona Perjuci, Walid Durani, Christian Böhm, Claudia Plant
Further, we propose DECCS (Deep Embedded Clustering with Consensus representationS), a deep consensus clustering method that learns a consensus representation by enhancing the embedded space to such a degree that all ensemble members agree on a common clustering result.