Early Fusion of Features for Semantic Segmentation

This paper introduces a novel segmentation framework that integrates a classifier network with a reverse HRNet architecture for efficient image segmentation. Our approach utilizes a ResNet-50 backbone, pretrained in a semi-supervised manner, to generate feature maps at various scales. These maps are then processed by a reverse HRNet, which is adapted to handle varying channel dimensions through 1x1 convolutions, to produce the final segmentation output. We strategically avoid fine-tuning the backbone network to minimize memory consumption during training. Our methodology is rigorously tested across several benchmark datasets including Mapillary Vistas, Cityscapes, CamVid, COCO, and PASCAL-VOC2012, employing metrics such as pixel accuracy and mean Intersection over Union (mIoU) to evaluate segmentation performance. The results demonstrate the effectiveness of our proposed model in achieving high segmentation accuracy, indicating its potential for various applications in image analysis. By leveraging the strengths of both the ResNet-50 and reverse HRNet within a unified framework, we present a robust solution to the challenges of image segmentation.

Adding guardrails to advanced chatbots

Generative AI models continue to become more powerful. The launch of ChatGPT in November 2022 has ushered in a new era of AI. ChatGPT and other similar chatbots have a range of capabilities, from answering student homework questions to creating music and art. There are already concerns that humans may be replaced by chatbots for a variety of jobs. Because of the wide spectrum of data chatbots are built on, we know that they will have human errors and human biases built into them. These biases may cause significant harm and/or inequity toward different subpopulations. To understand the strengths and weakness of chatbot responses, we present a position paper that explores different use cases of ChatGPT to determine the types of questions that are answered fairly and the types that still need improvement. We find that ChatGPT is a fair search engine for the tasks we tested; however, it has biases on both text generation and code generation. We find that ChatGPT is very sensitive to changes in the prompt, where small changes lead to different levels of fairness. This suggests that we need to immediately implement "corrections" or mitigation strategies in order to improve fairness of these systems. We suggest different strategies to improve chatbots and also advocate for an impartial review panel that has access to the model parameters to measure the levels of different types of biases and then recommends safeguards that move toward responses that are less discriminatory and more accurate.