Leveraging Human-Machine Interactions for Computer Vision Dataset Quality Enhancement

Large-scale datasets for single-label multi-class classification, such as \emph{ImageNet-1k}, have been instrumental in advancing deep learning and computer vision. However, a critical and often understudied aspect is the comprehensive quality assessment of these datasets, especially regarding potential multi-label annotation errors. In this paper, we introduce a lightweight, user-friendly, and scalable framework that synergizes human and machine intelligence for efficient dataset validation and quality enhancement. We term this novel framework \emph{Multilabelfy}. Central to Multilabelfy is an adaptable web-based platform that systematically guides annotators through the re-evaluation process, effectively leveraging human-machine interactions to enhance dataset quality. By using Multilabelfy on the ImageNetV2 dataset, we found that approximately $47.88\%$ of the images contained at least two labels, underscoring the need for more rigorous assessments of such influential datasets. Furthermore, our analysis showed a negative correlation between the number of potential labels per image and model top-1 accuracy, illuminating a crucial factor in model evaluation and selection. Our open-source framework, Multilabelfy, offers a convenient, lightweight solution for dataset enhancement, emphasizing multi-label proportions. This study tackles major challenges in dataset integrity and provides key insights into model performance evaluation. Moreover, it underscores the advantages of integrating human expertise with machine capabilities to produce more robust models and trustworthy data development. The source code for Multilabelfy will be available at https://github.com/esla/Multilabelfy. \keywords{Computer Vision \and Dataset Quality Enhancement \and Dataset Validation \and Human-Computer Interaction \and Multi-label Annotation.}

ViPLO: Vision Transformer based Pose-Conditioned Self-Loop Graph for Human-Object Interaction Detection

Human-Object Interaction (HOI) detection, which localizes and infers relationships between human and objects, plays an important role in scene understanding. Although two-stage HOI detectors have advantages of high efficiency in training and inference, they suffer from lower performance than one-stage methods due to the old backbone networks and the lack of considerations for the HOI perception process of humans in the interaction classifiers. In this paper, we propose Vision Transformer based Pose-Conditioned Self-Loop Graph (ViPLO) to resolve these problems. First, we propose a novel feature extraction method suitable for the Vision Transformer backbone, called masking with overlapped area (MOA) module. The MOA module utilizes the overlapped area between each patch and the given region in the attention function, which addresses the quantization problem when using the Vision Transformer backbone. In addition, we design a graph with a pose-conditioned self-loop structure, which updates the human node encoding with local features of human joints. This allows the classifier to focus on specific human joints to effectively identify the type of interaction, which is motivated by the human perception process for HOI. As a result, ViPLO achieves the state-of-the-art results on two public benchmarks, especially obtaining a +2.07 mAP performance gain on the HICO-DET dataset. The source codes are available at https://github.com/Jeeseung-Park/ViPLO.