1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CV2024
MulCPred: Learning Multi-modal Concepts for Explainable Pedestrian Action Prediction
Yan Feng, Alexander Carballo, Keisuke Fujii +3
Pedestrian action prediction is of great significance for many applications such as autonomous driving. However, state-of-the-art methods lack explainability to make trustworthy pr…
hep-th2024
Energy correlations and Planckian collisions
Hao Chen, Robin Karlsson, Alexander Zhiboedov
Energy correlations characterize the energy flux through detectors at infinity produced in a collision event. Remarkably, in holographic conformal field theories, they probe high-e…
cs.CV2023★ 1 cited
R-Cut: Enhancing Explainability in Vision Transformers with Relationship Weighted Out and Cut
Yingjie Niu, Ming Ding, Maoning Ge +3
Transformer-based models have gained popularity in the field of natural language processing (NLP) and are extensively utilized in computer vision tasks and multi-modal models such…