Workshop on Robust Recognition in the Open World

at the British Machine Vision Conference (BMVC) 2024

When a recognition AI, realized by a deep neural network (DNN), faces the open world, it will inevitably deal with unexpected scenes. This includes unknown objects and unknown environments / domains that are out of distribution (OOD) with respect to the data encountered during training. DNNs typically suffer from significant degradation of performance when facing OOD objects or domain shifts. This can be seen as the main obstacle for the application of AI-driven perception in medicine, automated driving and open world robotics.

With the advent of vision transformers, large-scale foundation models and vision-language models, a new perspective towards significant progress on this set of problems arises. We invite researchers to submit their original or previously published works on methods and datasets that study and expand the capabilities of DNNs for recognition in an open world.

Join us on 27 November 2024 in shaping the future of AI-driven computer vision. We are looking forward to your innovative contributions.

Speakers

Tentative Schedule

Workshop Date: 27 November 2024

Location: Scottish Exhibition Centre, Glasgow

Time Topic
9:00 - 9:15 Welcome
9:15 - 9:50 Robust Recognition with Image Decomposition – Jiri Matas
9:50 - 10:25 Plenary 2 – Petra Bevandic
10:25 - 10:40 Contributed talk 1:
1) "Unsupervised Feature Orthogonalization for Learning Distortion-Invariant Representations" - Daniel Bogdoll, Noël Ollick, Tim Joseph, Svetlana Pavlitska, J. Marius Zoellner
Short break
11:00 - 12:00 Contributed talks 2-5:
2) "Unsupervised Feature Orthogonalization for Learning Distortion-Invariant Representations" - Sebastian Doerrich, Francesco Di Salvo, Christian Ledig

3) "Hybrid Video Anomaly Detection for Autonomous Driving" - Daniel Bogdoll, Jan Imhof, Tim Joseph, Svetlana Pavlitska, J. Marius Zoellner

4) "Unsupervised Class Incremental Learning using Empty Classes" - Svenja Uhlemeyer, Julian Lienen, Youssef Shoeb, Eyke Hüllermeier, Hanno Gottschalk

5) "Uncertainty and Prediction Quality Estimation for Semantic Segmentation via Graph Neural Networks" - Edgar Heinert, Stephan Tilgner, Timo Palm, Matthias Rottmann
Lunch break
13:30 - 14:05 Object-wise Anomaly Detection in Complex Scenes – Toby Breckon
14:05 – 14:50 Contributed talks 6-8:
6) "Impact of Surface Reflections in Maritime Obstacle Detection" - Samed YALÇIN, Hazım Kemal Ekenel

7) "Patchhealer: Counterfactual Image Segment Transplants with chest X-ray domain check" - Hakan Lane, Michal Valko, Grace Guo, Veda Sahaja Bandi, Nandini Lokesh Reddy, Stefan Kramer

8) "A Study on Unsupervised Domain Adaptation for Semantic Segmentation in the Era of Vision-Language Models" - Manuel Schwonberg, Claus Werner, Hanno Gottschalk, Carsten Meyer
14:50 – 15:30 Round table discussion
Short break
15:45 - 16:30 Challenge session – OOD tracking on videos
16:30 – 17:05 What is the best paradigm to robustly recognize objects under challenging circumstances – Robert Geirhos
17:05 - 17:30 Best paper award, wrap up and closing remarks

Challenge

For this workshop, we invite participants to tackle the challenge of Open-World Object Detection and Tracking. Develop AI models that excel in detecting and tracking objects in diverse and unpredictable environments, addressing out-of-distribution (OOD) objects and domain shifts.

Organizers

Hermann Blum

Hermann Blum

Hanno Gottschalk

Hanno Gottschalk

Kira Maag

Kira Maag

Matthias Rottmann

Matthias Rottmann

Siniša Šegvić

Siniša Šegvić