Xinghao Ding

Unsupervised Adversarial Graph Alignment with Graph Embedding

2019 · arXiv (Cornell University)

Graph alignment, also known as network alignment, is a fundamental task in social network analysis. Many recent works have relied on partially labeled cross-graph node correspondences, i.e., anchor links. However, due to the privacy and …

Harmonizing Transferability and Discriminability for Adapting Object Detectors

2020 · arXiv (Cornell University)

Recent advances in adaptive object detection have achieved compelling results in virtue of adversarial feature adaptation to mitigate the distributional shifts along the detection pipeline. Whilst adversarial adaptation significantly enhances the transferability of feature representations, …

Hard Class Rectification for Domain Adaptation

2020 · arXiv (Cornell University)

Domain adaptation (DA) aims to transfer knowledge from a label-rich and related domain (source domain) to a label-scare domain (target domain). Pseudo-labeling has recently been widely explored and used in DA. However, this line of …

Unity in Diversity: Multi-expert Knowledge Confrontation and Collaboration for Generalizable Vehicle Re-identification

2024 · arXiv (Cornell University)

Generalizable vehicle re-identification (ReID) seeks to develop models that can adapt to unknown target domains without the need for additional fine-tuning or retraining. Previous works have mainly focused on extracting domain-invariant features by aligning data …

Decompose and Attribute: Boosting Generalizable Open-Set Object Detection via Objectness Score

2026 · Proceedings of the AAAI Conference on Artificial Intelligence

Open-set object detection (OSOD) aims to recognize known object categories while localizing previously unseen instances. However, real-world scenarios often involve co-occurring domain shifts and novel object categories. Existing OSOD methods typically overlook domain shifts, relying …