Camera Lens
Camera lenses, in their broadest sense, are systems for focusing light to form images, impacting diverse fields from autonomous driving to artistic expression and even large language model (LLM) development. Current research focuses on improving image quality through computational aberration correction and depth-of-field control, often employing novel architectures like diffusion models and leveraging techniques from Riemannian geometry and network flows for enhanced performance. These advancements are crucial for improving the accuracy and reliability of computer vision systems, enabling new applications in various domains, and furthering our understanding of complex data representations.
Papers
Shedding More Light on Robust Classifiers under the lens of Energy-based Models
Mujtaba Hussain Mirza, Maria Rosaria Briglia, Senad Beadini, Iacopo Masi
Understanding Visual Feature Reliance through the Lens of Complexity
Thomas Fel, Louis Bethune, Andrew Kyle Lampinen, Thomas Serre, Katherine Hermann
New User Event Prediction Through the Lens of Causal Inference
Henry Shaowu Yuchi, Shixiang Zhu, Li Dong, Yigit M. Arisoy, Matthew C. Spencer
Artificial Leviathan: Exploring Social Evolution of LLM Agents Through the Lens of Hobbesian Social Contract Theory
Gordon Dai, Weijia Zhang, Jinhan Li, Siqi Yang, Chidera Onochie lbe, Srihas Rao, Arthur Caetano, Misha Sra
Exploring Layerwise Adversarial Robustness Through the Lens of t-SNE
Inês Valentim, Nuno Antunes, Nuno Lourenço
Seeing Through AI's Lens: Enhancing Human Skepticism Towards LLM-Generated Fake News
Navid Ayoobi, Sadat Shahriar, Arjun Mukherjee
Towards Understanding Task-agnostic Debiasing Through the Lenses of Intrinsic Bias and Forgetfulness
Guangliang Liu, Milad Afshari, Xitong Zhang, Zhiyu Xue, Avrajit Ghosh, Bidhan Bashyal, Rongrong Wang, Kristen Johnson
Assessing LLMs for Zero-shot Abstractive Summarization Through the Lens of Relevance Paraphrasing
Hadi Askari, Anshuman Chhabra, Muhao Chen, Prasant Mohapatra