stp2y

arXiv:2405.13097v1 Announce Type: new Abstract: In real-world road scenes, diverse material properties lead to complex light reflection phenomena, making accurate color reproduction crucial for enhancing the realism and safety of simulated driving environments. However, existing methods often struggle to capture the full spectrum of lighting effects, particularly in dynamic scenarios where viewpoint changes induce significant material color variations. To address this challenge, we introduce NieR (Normal-Based Lighting Scene Rendering), a novel framework that takes into account the nuances of light reflection on diverse material surfaces, leading to more precise rendering. To simulate the lighting synthesis process, we present the LD…

[Submitted on 10 May 2024] View a PDF of the paper titled An Assessment of Model-On-Model Deception, by Julius Heitkoetter and 2 other authors View PDF Abstract:The trustworthiness of highly capable language models is put at risk when they are able to produce deceptive outputs. Moreover, when models are vulnerable to deception it undermines reliability. In this paper, we introduce a method to investigate complex, model-on-model deceptive scenarios. We create a dataset of over 10,000 misleading explanations by asking Llama-2 7B, 13B, 70B, and GPT-3.5 to justify the wrong answer for questions in the MMLU. We find that, when models…

In this article, we will modify the FasterViT model for semantic segmentation. FasterViT is a family of CNN-Transformer hybrid models for deep learning based computer vision tasks. The FasterViT models are faster and more accurate on several computer vision benchmarks, particularly the ImageNet dataset. We can also modify the model for semantic segmentation to get excellent results on a custom dataset. Although it is not straightforward and requires several changes to the architecture, it is possible. In this article, we will cover the architectural details and the changes we must make to the FasterViT model for semantic segmentation. Figure 1.…