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Self-augmented Gaussian Splatting with Structure-aware Masks for Sparse-view 3D Reconstruction
12 Aug

Self-augmented Gaussian Splatting with Structure-aware Masks for Sparse-view 3D Reconstruction

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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PLUGH: A Benchmark for Spatial Understanding and Reasoning in Large Language Models
12 Aug

PLUGH: A Benchmark for Spatial Understanding and Reasoning in Large Language Models

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arXiv:2408.04648v1 Announce Type: new Abstract: We present PLUGH (https://www.urbandictionary.com/define.php?term=plugh), a modern benchmark that currently consists of 5 tasks, each with 125 input texts extracted from 48 different games and representing 61 different (non-isomorphic) spatial graphs to assess the abilities of Large Language Models (LLMs) for spatial understanding and reasoning. Our evaluation of API-based and open-sourced LLMs shows that while some commercial LLMs exhibit strong reasoning abilities, open-sourced competitors can demonstrate almost the same level of quality; however, all models still have significant room for improvement. We identify typical reasons for LLM failures and discuss possible ways to deal with them.…
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Performance Metric for Multiple Anomaly Score Distributions with Discrete Severity Levels
12 Aug

Performance Metric for Multiple Anomaly Score Distributions with Discrete Severity Levels

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol
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One Shot is Enough for Sequential Infrared Small Target Segmentation
12 Aug

One Shot is Enough for Sequential Infrared Small Target Segmentation

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arXiv:2408.04823v1 Announce Type: new Abstract: Infrared small target sequences exhibit strong similarities between frames and contain rich contextual information, which motivates us to achieve sequential infrared small target segmentation with minimal data. Inspired by the success of large segmentation models led by Segment Anything Model (SAM) across various downstream tasks, we propose a one-shot and training-free method that perfectly adapts SAM's zero-shot generalization capabilities to sequential infrared small target segmentation. Given one annotated frame as a reference, our method can accurately segment small targets in other frames of the sequence. Specifically, we first obtain a confidence map through local feature…
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Distinguishing Chatbot from Human
12 Aug

Distinguishing Chatbot from Human

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arXiv:2408.04647v1 Announce Type: new Abstract: There have been many recent advances in the fields of generative Artificial Intelligence (AI) and Large Language Models (LLM), with the Generative Pre-trained Transformer (GPT) model being a leading "chatbot." LLM-based chatbots have become so powerful that it may seem difficult to differentiate between human-written and machine-generated text. To analyze this problem, we have developed a new dataset consisting of more than 750,000 human-written paragraphs, with a corresponding chatbot-generated paragraph for each. Based on this dataset, we apply Machine Learning (ML) techniques to determine the origin of text (human or chatbot). Specifically, we consider two…
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Towards improving Alzheimer’s intervention: a machine learning approach for biomarker detection through combining MEG and MRI pipelines
12 Aug

Towards improving Alzheimer’s intervention: a machine learning approach for biomarker detection through combining MEG and MRI pipelines

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[Submitted on 9 Aug 2024] View a PDF of the paper titled Towards improving Alzheimer's intervention: a machine learning approach for biomarker detection through combining MEG and MRI pipelines, by Alwani Liyana Ahmad and 5 other authors View PDF Abstract:MEG are non invasive neuroimaging techniques with excellent temporal and spatial resolution, crucial for studying brain function in dementia and Alzheimer Disease. They identify changes in brain activity at various Alzheimer stages, including preclinical and prodromal phases. MEG may detect pathological changes before clinical symptoms, offering potential biomarkers for intervention. This study evaluates classification techniques using MEG features to distinguish between…
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Rethinking Multiple Instance Learning: Developing an Instance-Level Classifier via Weakly-Supervised Self-Training
12 Aug

Rethinking Multiple Instance Learning: Developing an Instance-Level Classifier via Weakly-Supervised Self-Training

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arXiv:2408.04813v1 Announce Type: new Abstract: Multiple instance learning (MIL) problem is currently solved from either bag-classification or instance-classification perspective, both of which ignore important information contained in some instances and result in limited performance. For example, existing methods often face difficulty in learning hard positive instances. In this paper, we formulate MIL as a semi-supervised instance classification problem, so that all the labeled and unlabeled instances can be fully utilized to train a better classifier. The difficulty in this formulation is that all the labeled instances are negative in MIL, and traditional self-training techniques used in semi-supervised learning tend to…
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Efficacy of Large Language Models in Systematic Reviews
12 Aug

Efficacy of Large Language Models in Systematic Reviews

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arXiv:2408.04646v1 Announce Type: new Abstract: This study investigates the effectiveness of Large Language Models (LLMs) in interpreting existing literature through a systematic review of the relationship between Environmental, Social, and Governance (ESG) factors and financial performance. The primary objective is to assess how LLMs can replicate a systematic review on a corpus of ESG-focused papers. We compiled and hand-coded a database of 88 relevant papers published from March 2020 to May 2024. Additionally, we used a set of 238 papers from a previous systematic review of ESG literature from January 2015 to February 2020. We evaluated two current state-of-the-art LLMs,…
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On the Geometry of Deep Learning
12 Aug

On the Geometry of Deep Learning

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[Submitted on 9 Aug 2024] View a PDF of the paper titled On the Geometry of Deep Learning, by Randall Balestriero and 2 other authors View PDF HTML (experimental) Abstract:In this paper, we overview one promising avenue of progress at the mathematical foundation of deep learning: the connection between deep networks and function approximation by affine splines (continuous piecewise linear functions in multiple dimensions). In particular, we will overview work over the past decade on understanding certain geometrical properties of a deep network's affine spline mapping, in particular how it tessellates its input space. As we will see, the affine…
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UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling
12 Aug

UniBench: Visual Reasoning Requires Rethinking Vision-Language Beyond Scaling

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arXiv:2408.04810v1 Announce Type: new Abstract: Significant research efforts have been made to scale and improve vision-language model (VLM) training approaches. Yet, with an ever-growing number of benchmarks, researchers are tasked with the heavy burden of implementing each protocol, bearing a non-trivial computational cost, and making sense of how all these benchmarks translate into meaningful axes of progress. To facilitate a systematic evaluation of VLM progress, we introduce UniBench: a unified implementation of 50+ VLM benchmarks spanning a comprehensive range of carefully categorized capabilities from object recognition to spatial awareness, counting, and much more. We showcase the utility of UniBench for…
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