Computer Vision

  Machine learning

Searching on Manifolds - Ahmet Iscen (
This presentation focuses on similarity search on manifolds. The first part of the presentation investigates diffusion, a mechanism that captures the image manifold in the feature space. Despite the success of deep learning on representing images for instance-based image retrieval, recent studies show that the learned representations still lie on manifolds in a high dimensional space. Experimentally, we observe a significant boost in performance of image retrieval with compact CNN descriptors on standard benchmarks, especially when the query object covers only a small part of the image. The second part of the presentation focuses on hard training example mining for unsupervised metric learning. Experimentally, we show that our learned models are on par or are outperforming prior models that are fully or partially supervised.

Tolga Birdal (
The topic to be added.

Language: English

- 17:45 - 18:00 - Your arrival
- 18:00 - 18:40 - First talk
- 18:40 - 18:50 - Short break
- 18:50 - 19:30 - Second talk
- 19:30 - 22:00 - Networking in Bitcoin Coffee