Human Parsing

The PASCAL Context dataset is an extension of the PASCAL VOC 2010 detection challenge, and it contains pixel-wise labels for all training images. It contains more than 400 classes (including the original 20 classes plus backgrounds from PASCAL VOC segmentation), divided into three categories (objects, stuff, and hybrids). Many of the object categories of this dataset are too sparse and; therefore, a subset of 59 frequent classes are usually selected for use. Source: Image Segmentation Using Deep Learning:A Survey Image Source: https://cs.stanford.edu/~roozbeh/pascal-context/

PASCAL-Part is a set of additional annotations for PASCAL VOC 2010. It goes beyond the original PASCAL object detection task by providing segmentation masks for each body part of the object. For categories that do not have a consistent set of parts (e.g., boat), it provides the silhouette annotation. It can also serve as a set for human semantic part segmentation: It contains multiple humans per image in unconstrained poses and occlusions (1,716 for training and 1,817 for testing). It provides careful pixel-wise annotations for six body parts (i.e., head, torso, upper/lower-arms, and upper-/lower-legs). Source: The Ultimate Theory of Human Parsing Image Source: https://www.researchgate.net/profile/Zhedong_Zheng/publication/328123707/figure/fig4/AS:704683136016384@1545020960225/Qualitative-parsing-results-on-the-Pascal-Person-Part-dataset.png

4D-DRESS is the first real-world 4D dataset of human clothing, capturing 64 human outfits in more than 520 motion sequences. These sequences include a) high-quality 4D textured scans; for each scan, we annotate b) vertex-level semantic labels, thereby obtaining c) the corresponding garment meshes and fitted SMPL(-X) body meshes. Totally, 4D-DRESS captures dynamic motions of 4 dresses, 28 lower, 30 upper, and 32 outer garments. For each garment, we also provide its canonical template mesh to benefit the future human clothing study.

The MHP dataset contains multiple persons captured in real-world scenes with pixel-level fine-grained semantic annotations in an instance-aware setting. Source: Multiple-Human Parsing in the Wild Image Source: Li et al

CCIHP dataset is devoted to fine-grained description of people in the wild with localized & characterized semantic attributes. It contains 20 attribute classes and 20 characteristic classes split into 3 categories (size, pattern and color). The dataset has been introduced in this paper: Loesch, A., & Audigier, R. (2021, September). Describe me if you can! Characterized instance-level human parsing. In 2021 IEEE International Conference on Image Processing (ICIP) (pp. 2528-2532). IEEE. The annotations were made with Pixano, an opensource, smart annotation tool for computer vision applications: https://pixano.cea.fr/

To evaluate our proposed strategy of asynchronous communication for LLMs, we run games of Mafia with human players, incorporating an LLM-based agent as an additional player, within an asynchronous chat environment. The data includes all players messages and votes including timestamps, game management messages (e.g., announcements of the beginning and end of phases), in addition to records related to the agent, such as the prompts that were provided at each timestamp. While prior datasets for Mafia exist, ours is the first to integrate an LLM agent, allowing analysis of human-LLM interactions. Detailed analysis of the data is available in our paper: Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia Games