3 papers
cs.CV2026
Can Machines Really See Objects in Images? A Study Based on Syntactic Distance and Visual Self-Referential Instances
Xingyu Peng, Junran Wu, Yue Hou +9
Can a vision model truly see an object, or does it only fit surface-level visual cues? Following Wittgenstein's view that the limits of language are the limits of the world, we vie…
cs.LG2025
Molecular Graph Contrastive Learning with Line Graph
Xueyuan Chen, Shangzhe Li, Ruomei Liu +4
Trapped by the label scarcity in molecular property prediction and drug design, graph contrastive learning (GCL) came forward. Leading contrastive learning works show two kinds of…
cs.LG2024
Uncovering Capabilities of Model Pruning in Graph Contrastive Learning
Junran Wu, Xueyuan Chen, Shangzhe Li
Graph contrastive learning has achieved great success in pre-training graph neural networks without ground-truth labels. Leading graph contrastive learning follows the classical sc…