7 papers
Noise-Level Diffusion Guidance: Well Begun is Half Done
Harvey Mannering, Zhiwu Huang, Adam Prugel-Bennett
Diffusion models have achieved state-of-the-art image generation. However, the random Gaussian noise used to start the diffusion process influences the final output, causing variat…
CornerPoint3D: Look at the Nearest Corner Instead of the Center
Ruixiao Zhang, Runwei Guan, Xiangyu Chen +2
3D object detection aims to predict object centers, dimensions, and rotations from LiDAR point clouds. Despite its simplicity, LiDAR captures only the near side of objects, making…
Concept-Based Explainable Artificial Intelligence: Metrics and Benchmarks
Halil Ibrahim Aysel, Xiaohao Cai, Adam Prugel-Bennett
Concept-based explanation methods, such as concept bottleneck models (CBMs), aim to improve the interpretability of machine learning models by linking their decisions to human-unde…
Rethinking Deep Thinking: Stable Learning of Algorithms using Lipschitz Constraints
Jay Bear, Adam Prügel-Bennett, Jonathon Hare
Iterative algorithms solve problems by taking steps until a solution is reached. Models in the form of Deep Thinking (DT) networks have been demonstrated to learn iterative algorit…
Revisiting Cross-Domain Problem for LiDAR-based 3D Object Detection
Ruixiao Zhang, Juheon Lee, Xiaohao Cai +1
Deep learning models such as convolutional neural networks and transformers have been widely applied to solve 3D object detection problems in the domain of autonomous driving. Whil…
Penny-Wise and Pound-Foolish in Deepfake Detection
Yabin Wang, Zhiwu Huang, Su Zhou +2
The diffusion of deepfake technologies has sparked serious concerns about its potential misuse across various domains, prompting the urgent need for robust detection methods. Despi…