1.5k citations · 1.8k across the 30 of their papers we have counts for
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DINO-Foresight: Looking into the Future with DINO
Efstathios Karypidis, Ioannis Kakogeorgiou, Spyros Gidaris +1
Predicting future dynamics is crucial for applications like autonomous driving and robotics, where understanding the environment is key. Existing pixel-level methods are computatio…
No Train, all Gain: Self-Supervised Gradients Improve Deep Frozen Representations
Walter Simoncini, Spyros Gidaris, Andrei Bursuc +1
This paper introduces FUNGI, Features from UNsupervised GradIents, a method to enhance the features of transformer encoders by leveraging self-supervised gradients. Our method is s…
Valeo4Cast: A Modular Approach to End-to-End Forecasting
Yihong Xu, Éloi Zablocki, Alexandre Boulch +11
Motion forecasting is crucial in autonomous driving systems to anticipate the future trajectories of surrounding agents such as pedestrians, vehicles, and traffic signals. In end-t…
OccFeat: Self-supervised Occupancy Feature Prediction for Pretraining BEV Segmentation Networks
Sophia Sirko-Galouchenko, Alexandre Boulch, Spyros Gidaris +4
We introduce a self-supervised pretraining method, called OccFeat, for camera-only Bird's-Eye-View (BEV) segmentation networks. With OccFeat, we pretrain a BEV network via occupanc…
POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images
Antonin Vobecky, Oriane Siméoni, David Hurych +4
We describe an approach to predict open-vocabulary 3D semantic voxel occupancy map from input 2D images with the objective of enabling 3D grounding, segmentation and retrieval of f…