Recent multi-teacher distillation methods have successfully unified the encoders of several foundation models into a single encoder capable of competitive performance on core computer vision tasks, such as classification, segmentation, and depth estimation. This led us to ask: Could similar success be achieved when the pool of teachers also includes vision models specialized in diverse tasks across 2D and 3D perception? In this paper, we define and investigate the problem of heterogeneous teacher distillation, or co-distillation — a challenging multi-teacher distillation scenario where teacher models vary significantly in both (a) their design objectives and (b) the data they were trained on. We explore strategies for data sharing and encoding teacher-specific information and as a result, we obtain a single encoder that excels in challenging tasks spanning 3D understanding, 3D human perception, and 2D vision. The resulting model exhibits strong generalization capabilities and performs on par with its teachers, each one state-of-the-art for a specialized task. Notably, our model outperforms all known methods on the Map-free Visual Relocalization dataset with a highly compact encoder.
Heterogeneous teacher distillation
| Mert Bulent Sariyildiz, Philippe Weinzaepfel, Thomas Lucas, Pau De Jorge, Diane Larlus, Yannis Kalantidis |
| The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, Tennessee, USA, 11-15 June, 2025 |
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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