Fanman Meng

GFR: Generic feature representations for class incremental learning

2023 · Neurocomputing

Class incremental learning (CIL) aims to continuously learn new classes while maintaining discrimination for old classes with sequentially coming data. Due to the lack of old-class samples, existing CIL methods fail to learn discriminative representations …

CafeBoost: Causal Feature Boost to Eliminate Task-Induced Bias for Class Incremental Learning

2023

Continual learning requires a model to incrementally learn a sequence of tasks and aims to predict well on all the learned tasks so far, which notoriously suffers from the catastrophic forgetting problem. In this paper, …

GRSDet: Learning to Generate Local Reverse Samples for Few-shot Object Detection

2023 · arXiv (Cornell University)

Few-shot object detection (FSOD) aims to achieve object detection only using a few novel class training data. Most of the existing methods usually adopt a transfer-learning strategy to construct the novel class distribution by transferring …

IoU-CLIP: IoU-Aware Language-Image Model Tuning for Open Vocabulary Object Detection

2024

Open vocabulary object detection (OVD), which detects novel categories through detectors trained on base categories, has achieved remarkable advancement attributable to large-scale vision-language models, such as CLIP. The prior OVD works mainly focused on improving …