Gregory Dudek

PhotoBot: Reference-Guided Interactive Photography via Natural Language

Oliver Limoyo

Jimmy Li

Dmitriy Rivkin

Jonathan Kelly

We introduce PhotoBot, a framework for fully automated photo acquisition based on an interplay between high-level human language guidance an… (voir plus)d a robot photographer. We propose to communicate photography suggestions to the user via reference images that are selected from a curated gallery. We leverage a visual language model (VLM) and an object detector to characterize the reference images via textual descriptions and then use a large language model (LLM) to retrieve relevant reference images based on a user’s language query through text-based reasoning. To correspond the reference image and the observed scene, we exploit pretrained features from a vision transformer capable of capturing semantic similarity across marked appearance variations. Using these features, we compute suggested pose adjustments for an RGB-D camera by solving a perspective-n-point (PnP) problem. We demonstrate our approach using a manipulator equipped with a wrist camera. Our user studies show that photos taken by PhotoBot are often more aesthetically pleasing than those taken by users themselves, as measured by human feedback. We also show that PhotoBot can generalize to other reference sources such as paintings.

2024-01-19

ArXiv (prépublication)

Hallucination Detection and Hallucination Mitigation: An Investigation

Junliang Luo

Tianyu Li

Di Wu

M. Jenkin

Steve Liu

2024-01-16

ArXiv (prépublication)

Hallucination Detection and Hallucination Mitigation: An Investigation

Junliang Luo

Tianyu Li

Di Wu

M. Jenkin

Steve Liu

Large language models (LLMs), including ChatGPT, Bard, and Llama, have achieved remarkable successes over the last two years in a range of d… (voir plus)ifferent applications. In spite of these successes, there exist concerns that limit the wide application of LLMs. A key problem is the problem of hallucination. Hallucination refers to the fact that in addition to correct responses, LLMs can also generate seemingly correct but factually incorrect responses. This report aims to present a comprehensive review of the current literature on both hallucination detection and hallucination mitigation. We hope that this report can serve as a good reference for both engineers and researchers who are interested in LLMs and applying them to real world tasks.

2024-01-16

ArXiv (prépublication)

Hallucination Detection and Hallucination Mitigation: An Investigation

Junliang Luo

Tianyu Li

Di Wu

M. Jenkin

Steve Liu

Large language models (LLMs), including ChatGPT, Bard, and Llama, have achieved remarkable successes over the last two years in a range of d… (voir plus)ifferent applications. In spite of these successes, there exist concerns that limit the wide application of LLMs. A key problem is the problem of hallucination. Hallucination refers to the fact that in addition to correct responses, LLMs can also generate seemingly correct but factually incorrect responses. This report aims to present a comprehensive review of the current literature on both hallucination detection and hallucination mitigation. We hope that this report can serve as a good reference for both engineers and researchers who are interested in LLMs and applying them to real world tasks.

2024-01-16

ArXiv (prépublication)