4 citations · 9 across the 15 of their papers we have counts for
6 papers · 1 filter
LLM meets Vision-Language Models for Zero-Shot One-Class Classification
Yassir Bendou, Giulia Lioi, Bastien Pasdeloup +4
We consider the problem of zero-shot one-class visual classification, extending traditional one-class classification to scenarios where only the label of the target class is availa…
A Novel Benchmark for Few-Shot Semantic Segmentation in the Era of Foundation Models
Reda Bensaid, Vincent Gripon, François Leduc-Primeau +3
Few-shot semantic segmentation (FSS) is a crucial challenge in computer vision, driving extensive research into a diverse range of methods, from advanced meta-learning techniques t…
Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning
Yassir Bendou, Vincent Gripon, Bastien Pasdeloup +4
In the realm of few-shot learning, foundation models like CLIP have proven effective but exhibit limitations in cross-domain robustness especially in few-shot settings. Recent work…
DNN Quantization with Attention
Ghouthi Boukli Hacene, Lukas Mauch, Stefan Uhlich +1
Low-bit quantization of network weights and activations can drastically reduce the memory footprint, complexity, energy consumption and latency of Deep Neural Networks (DNNs). Howe…
Efficient Hardware Implementation of Incremental Learning and Inference on Chip
Ghouthi Boukli Hacene, Vincent Gripon, Nicolas Farrugia +2
In this paper, we tackle the problem of incrementally learning a classifier, one example at a time, directly on chip. To this end, we propose an efficient hardware implementation o…
Transfer Incremental Learning using Data Augmentation
Ghouthi Boukli Hacene, Vincent Gripon, Nicolas Farrugia +2
Deep learning-based methods have reached state of the art performances, relying on large quantity of available data and computational power. Such methods still remain highly inappr…