HUMBI: A Large Multiview Dataset of Human Body Expressions and Benchmark Challenge

Yoon, Jae Shin; Yu, Zhixuan; Park, Jaesik; Park, Hyun Soo

doi:10.1109/tpami.2021.3138762

Cited by 12 publications

(2 citation statements)

References 104 publications

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“…Metrics such as precision, recall, and F1-score have been extensively used to quantify the performance improvements achieved through various techniques [37]. The use of benchmark datasets allowed for consistent and comparative evaluation of different methods, ensuring that improvements were not dataset-specific but generalizable across various contexts [38]. Cross-validation techniques provided robust insights into the reliability and stability of the proposed methods by testing them across multiple data splits [7,39].…”

Section: Evaluating the Effectiveness Of Mitigation Techniquesmentioning

confidence: 99%

Mitigating Structural Hallucination in Large Language Models with Local Diffusion

Kiritani,

Kayano

2024

Preprint

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Large language models (LLMs) often produce text with inaccuracies, logical inconsistencies, or fabricated information, known as structural hallucinations, which undermine their reliability and trustworthiness. Implementing local diffusion mechanisms within the Mistral LLM architecture has demonstrated significant potential in addressing these issues, enhancing both the accuracy and coherence of the generated text. The modified model exhibited substantial improvements across various performance metrics, including accuracy, precision, recall, and F1 score, validated through rigorous statistical testing. The architectural adjustments, involving the integration of diffusion layers, facilitated better information propagation and reduced the occurrence of structurally flawed outputs. Quantitative analyses highlighted the model's enhanced performance, while qualitative comparisons revealed its improved structural integrity and factual accuracy. Additionally, error analysis revealed a notable reduction in the frequency of factual and logical errors, further affirming the effectiveness of the local diffusion approach. The findings reveal the transformative potential of local diffusion in mitigating structural hallucinations and advancing the field of natural language processing.

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Section: Evaluating the Effectiveness Of Mitigation Techniquesmentioning

confidence: 99%

Mitigating Structural Hallucination in Large Language Models with Local Diffusion

Kiritani,

Kayano

2024

Preprint

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“…Currently there exist many datasets providing RGB images and 3D pose annotations in both singleperson and multi-person scenarios. Given the ease of data collection, there are numerous single-person 2D [Joo et al 2021;Kolotouros et al 2019;Lin et al 2014] and 3D datasets [Cai et al 2022;Chatzitofis et al 2020;Fieraru et al 2021a,b;Ionescu et al 2014;Mehta et al 2017;Ofli et al 2013;Sigal et al 2010;Trumble et al 2017;Von Marcard et al 2018;Yoon et al 2021], featuring diverse actors, actions, and modalities. However, training and validation data are relatively scarce in multi-person scenarios.…”

Section: Related Workmentioning

confidence: 99%

Reconstructing Close Human Interactions from Multiple Views

Shuai,

Yu,

Zhou

et al. 2023

ACM Trans. Graph.

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This paper addresses the challenging task of reconstructing the poses of multiple individuals engaged in close interactions, captured by multiple calibrated cameras. The difficulty arises from the noisy or false 2D keypoint detections due to inter-person occlusion, the heavy ambiguity in associating keypoints to individuals due to the close interactions, and the scarcity of training data as collecting and annotating motion data in crowded scenes is resource-intensive. We introduce a novel system to address these challenges. Our system integrates a learning-based pose estimation component and its corresponding training and inference strategies. The pose estimation component takes multi-view 2D keypoint heatmaps as input and reconstructs the pose of each individual using a 3D conditional volumetric network. As the network doesn't need images as input, we can leverage known camera parameters from test scenes and a large quantity of existing motion capture data to synthesize massive training data that mimics the real data distribution in test scenes. Extensive experiments demonstrate that our approach significantly surpasses previous approaches in terms of pose accuracy and is generalizable across various camera setups and population sizes. The code is available on our project page: https://github.com/zju3dv/CloseMoCap.

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ETH-XGaze: A Large Scale Dataset for Gaze Estimation Under Extreme Head Pose and Gaze Variation

Zhang

Park

Beeler

et al. 2020

Lecture Notes in Computer Science

200

113

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HUMBI: A Large Multiview Dataset of Human Body Expressions and Benchmark Challenge

Cited by 12 publications

References 104 publications

Mitigating Structural Hallucination in Large Language Models with Local Diffusion

Mitigating Structural Hallucination in Large Language Models with Local Diffusion

Reconstructing Close Human Interactions from Multiple Views

ETH-XGaze: A Large Scale Dataset for Gaze Estimation Under Extreme Head Pose and Gaze Variation

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