Chengjie Wang

DIRL: Domain-Invariant Representation Learning for Generalizable Semantic Segmentation

2022 · Proceedings of the AAAI Conference on Artificial Intelligence

Model generalization to the unseen scenes is crucial to real-world applications, such as autonomous driving, which requires robust vision systems. To enhance the model generalization, domain generalization through learning the domain-invariant representation has been widely …

Delving into Transformer for Incremental Semantic Segmentation

2022 · arXiv (Cornell University)

Incremental semantic segmentation(ISS) is an emerging task where old model is updated by incrementally adding new classes. At present, methods based on convolutional neural networks are dominant in ISS. However, studies have shown that such …

Learning Multi-view Anomaly Detection with Efficient Adaptive Selection

2024 · arXiv (Cornell University)

This study explores the recently proposed and challenging multi-view Anomaly Detection (AD) task. Single-view tasks will encounter blind spots from other perspectives, resulting in inaccuracies in sample-level prediction. Therefore, we introduce the Multi-View Anomaly Detection …

Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation

2025 · arXiv (Cornell University)

This paper focus on few-shot object detection~(FSOD) and instance segmentation~(FSIS), which requires a model to quickly adapt to novel classes with a few labeled instances. The existing methods severely suffer from bias classification because of …