Yibing Zhan

Dual-branch Density Ratio Estimation for Signed Network Embedding

2022 · Proceedings of the ACM Web Conference 2022

Signed network embedding (SNE) has received considerable attention in recent years. A mainstream idea of SNE is to learn node representations by estimating the ratio of sampling densities. Though achieving promising performance, these methods based …

Not All Instances Contribute Equally: Instance-adaptive Class Representation Learning for Few-Shot Visual Recognition

2022 · arXiv (Cornell University)

Few-shot visual recognition refers to recognize novel visual concepts from a few labeled instances. Many few-shot visual recognition methods adopt the metric-based meta-learning paradigm by comparing the query representation with class representations to predict the …

Exploring Sparsity in Graph Transformers

2023 · arXiv (Cornell University)

Graph Transformers (GTs) have achieved impressive results on various graph-related tasks. However, the huge computational cost of GTs hinders their deployment and application, especially in resource-constrained environments. Therefore, in this paper, we explore the feasibility …

Towards Alleviating Text-to-Image Retrieval Hallucination for CLIP in Zero-shot Learning

2024 · arXiv (Cornell University)

Pretrained cross-modal models, for instance, the most representative CLIP, have recently led to a boom in using pre-trained models for cross-modal zero-shot tasks, considering the generalization properties. However, we analytically discover that CLIP suffers from …

Joint Input and Output Coordination for Class-Incremental Learning

2024 · arXiv (Cornell University)

Incremental learning is nontrivial due to severe catastrophic forgetting. Although storing a small amount of data on old tasks during incremental learning is a feasible solution, current strategies still do not 1) adequately address the …