Search Results for author:
Found 7 papers, 1 papers with code
Controllable Forgetting Mechanism for Few-Shot Class-Incremental Learning
A key challenge in these scenarios is balancing the trade-off between adapting to new, personalized classes and maintaining the performance of the model on the original, base classes.
class-incremental learning
Few-Shot Class-Incremental Learning
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Swiss DINO: Efficient and Versatile Vision Framework for On-device Personal Object Search
In this paper, we address a recent trend in robotic home appliances to include vision systems on personal devices, capable of personalizing the appliances on the fly.
Enhanced Model Robustness to Input Corruptions by Per-corruption Adaptation of Normalization Statistics
In this paper, we introduce Per-corruption Adaptation of Normalization statistics (PAN) to enhance the model robustness of vision systems.
Cross-Architecture Auxiliary Feature Space Translation for Efficient Few-Shot Personalized Object Detection
We introduce a conditional coarse-to-fine few-shot learner to refine the coarse predictions made by an efficient object detector, showing that using an off-the-shelf model leads to poor personalization due to neural collapse.
Object-conditioned Bag of Instances for Few-Shot Personalized Instance Recognition
Nowadays, users demand for increased personalization of vision systems to localize and identify personal instances of objects (e. g., my dog rather than dog) from a few-shot dataset only.
FFT-based Selection and Optimization of Statistics for Robust Recognition of Severely Corrupted Images
Improving model robustness in case of corrupted images is among the key challenges to enable robust vision systems on smart devices, such as robotic agents.
NNStreamer: Efficient and Agile Development of On-Device AI Systems
NNStreamer efficiently handles neural networks with complex data stream pipelines on devices, significantly improving the overall performance with minimal efforts.
