4 papers
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…
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…
PTSBench: A Comprehensive Post-Training Sparsity Benchmark Towards Algorithms and Models
Zining Wnag, Jinyang Guo, Ruihao Gong +5
With the increased attention to model efficiency, post-training sparsity (PTS) has become more and more prevalent because of its effectiveness and efficiency. However, there remain…
NC-NCD: Novel Class Discovery for Node Classification
Yue Hou, Xueyuan Chen, He Zhu +5
Novel Class Discovery (NCD) involves identifying new categories within unlabeled data by utilizing knowledge acquired from previously established categories. However, existing NCD…