70 citations · 255 across the 86 of their papers we have counts for
17 papers · 1 filter
SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised Learning
Yue Fan, Anna Kukleva, Dengxin Dai +1
Semi-supervised learning (SSL) methods effectively leverage unlabeled data to improve model generalization. However, SSL models often underperform in open-set scenarios, where unla…
Wakening Past Concepts without Past Data: Class-Incremental Learning from Online Placebos
Yaoyao Liu, Yingying Li, Bernt Schiele +1
Not forgetting old class knowledge is a key challenge for class-incremental learning (CIL) when the model continuously adapts to new classes. A common technique to address this is…
DARTH: Holistic Test-time Adaptation for Multiple Object Tracking
Mattia Segu, Bernt Schiele, Fisher Yu
Multiple object tracking (MOT) is a fundamental component of perception systems for autonomous driving, and its robustness to unseen conditions is a requirement to avoid life-criti…
HowToCaption: Prompting LLMs to Transform Video Annotations at Scale
Nina Shvetsova, Anna Kukleva, Xudong Hong +3
Instructional videos are a common source for learning text-video or even multimodal representations by leveraging subtitles extracted with automatic speech recognition systems (ASR…
In-Style: Bridging Text and Uncurated Videos with Style Transfer for Text-Video Retrieval
Nina Shvetsova, Anna Kukleva, Bernt Schiele +1
Large-scale noisy web image-text datasets have been proven to be efficient for learning robust vision-language models. However, when transferring them to the task of video retrieva…
Certified Robust Models with Slack Control and Large Lipschitz Constants
Max Losch, David Stutz, Bernt Schiele +1
Despite recent success, state-of-the-art learning-based models remain highly vulnerable to input changes such as adversarial examples. In order to obtain certifiable robustness aga…