Foundation Model-Powered 3D Few-Shot Class Incremental Learning via Training-free Adaptor
Recent advances in deep learning for processing point clouds hold increased interest in Few-Shot Class Incremental Learning (FSCIL) for 3D computer vision. This paper introduces a new method to tackle the Few-Shot Continual Incremental Learning (FSCIL) problem in 3D point cloud environments. We leverage a foundational 3D model trained extensively on point cloud data. Drawing from recent improvements in foundation models, known for their ability to work well across different tasks, we propose a novel strategy that does not require additional training to adapt to new tasks. Our approach uses a dual cache system: first, it uses previous test samples based on how confident the model was in its predictions to prevent forgetting, and second, it includes a small number of new task samples to prevent overfitting. This dynamic adaptation ensures strong performance across different learning tasks without needing lots of fine-tuning. We tested our approach on datasets like ModelNet, ShapeNet, ScanObjectNN, and CO3D, showing that it outperforms other FSCIL methods and demonstrating its effectiveness and versatility. The code is available at \url{https://github.com/ahmadisahar/ACCV_FCIL3D}.
Code (1)
Tasks
class-incremental learningClass Incremental LearningFew-Shot Class-Incremental LearningIncremental LearningSimilar Papers 제목 키워드 기반
Few-shot Tuning of Foundation Models for Class-incremental Learning
For the first time, we explore few-shot tuning of vision foundation models for class-incremental learning. Unlike existing few-shot class incremental learning (FSCIL) methods, which train an encoder on a base session to …
class-incremental learningClass Incremental LearningContinual LearningFew-Shot Class-Incremental Learning+1Knowledge Adaptation Network for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) aims to incrementally recognize new classes using a few samples while maintaining the performance on previously learned classes. One of the effective methods to solve this chal…
class-incremental learningClass Incremental LearningFew-Shot Class-Incremental LearningIncremental LearningA New Benchmark for Few-Shot Class-Incremental Learning: Redefining the Upper Bound
Class-incremental learning (CIL) aims to continuously adapt to emerging classes while retaining knowledge of previously learned ones. Few-shot class-incremental learning (FSCIL) presents an even greater challenge which r…
class-incremental learningClass Incremental LearningFew-Shot Class-Incremental LearningIncremental Learning12 mJ per Class On-Device Online Few-Shot Class-Incremental Learning
Few-Shot Class-Incremental Learning (FSCIL) enables machine learning systems to expand their inference capabilities to new classes using only a few labeled examples, without forgetting the previously learned classes. Cla…
class-incremental learningClass Incremental LearningContinual LearningFew-Shot Class-Incremental Learning+2Modular Embedding Recomposition for Incremental Learning
The advent of pre-trained Vision-Language Models (VLMs) has significantly transformed Continual Learning (CL), mainly due to their zero-shot classification abilities. Such proficiency makes VLMs well-suited for real-worl…
Incremental LearningContinual Learning