4 papers
Generalize or Detect? Towards Robust Semantic Segmentation Under Multiple Distribution Shifts
Zhitong Gao, Bingnan Li, Mathieu Salzmann +1
In open-world scenarios, where both novel classes and domains may exist, an ideal segmentation model should detect anomaly classes for safety and generalize to new domains. However…
Diffusion-based Speech Enhancement with Schrödinger Bridge and Symmetric Noise Schedule
Siyi Wang, Siyi Liu, Andrew Harper +3
Recently, diffusion-based generative models have demonstrated remarkable performance in speech enhancement tasks. However, these methods still encounter challenges, including the l…
3D Single-object Tracking in Point Clouds with High Temporal Variation
Qiao Wu, Kun Sun, Pei An +3
The high temporal variation of the point clouds is the key challenge of 3D single-object tracking (3D SOT). Existing approaches rely on the assumption that the shape variation of t…
Hybrid diffusion models: combining supervised and generative pretraining for label-efficient fine-tuning of segmentation models
Bruno Sauvalle, Mathieu Salzmann
We are considering in this paper the task of label-efficient fine-tuning of segmentation models: We assume that a large labeled dataset is available and allows to train an accurate…