CORNELL NEWSROOM is a large dataset for training and evaluating summarization systems. It contains 1.3 million articles and summaries written by authors and editors in the newsrooms of 38 major publications. The summaries are obtained from search and social metadata between 1998 and 2017 and use a variety of summarization strategies combining extraction and abstraction. Source: CORNELL NEWSROOM

Node classification on Cornell with the fixed 48%/32%/20% splits provided by Geom-GCN.

Node classification on Cornell with 60%/20%/20% random splits for training/validation/test.

The Cornell eRulemaking Corpus – CDCP is an argument mining corpus annotated with argumentative structure information capturing the evaluability of arguments. The corpus consists of 731 user comments on Consumer Debt Collection Practices (CDCP) rule by the Consumer Financial Protection Bureau (CFPB); the resulting dataset contains 4931 elementary unit and 1221 support relation annotations. It is a resource for building argument mining systems that can not only extract arguments from unstructured text, but also identify what additional information is necessary for readers to understand and evaluate a given argument. Immediate applications include providing real-time feedback to commenters, specifying which types of support for which propositions can be added to construct better-formed arguments.

CHALET is a 3D house simulator with support for navigation and manipulation. Unlike existing systems, CHALET supports both a wide range of object manipulation, as well as supporting complex environemnt layouts consisting of multiple rooms. The range of object manipulations includes the ability to pick up and place objects, toggle the state of objects like taps or televesions, open or close containers, and insert or remove objects from these containers. In addition, the simulator comes with 58 rooms that can be combined to create houses, including 10 default house layouts. CHALET is therefore suitable for setting up challenging environments for various AI tasks that require complex language understanding and planning, such as navigation, manipulation, instruction following, and interactive question answering. Source: https://github.com/lil-lab/chalet Image Source: https://arxiv.org/pdf/1809.00786.pdf

Cornell-Box Dataset ## Download The CornellBox Dataset can be downloaded from this URL >https://viscom.datasets.uni-ulm.de/radu/dataset.zip ## Dataset The dataset contains correlation measurements, ToF depth images and ground truth depth images in .hdr format. The script simulate_noise_on_correlations.py can be used to simulate shot noise on the correlation images using the default arguments. ## Citing this work If you use this data in your work, please kindly cite the following paper: @InProceedings{schelling2022radu, author = {Schelling, Michael and Hermosilla, Pedro and Ropinski, Timo}, title = {{RADU} - Ray-Aligned Depth Update Convolutions for {ToF} Data Denoising}, booktitle = {Conference on Computer Vision and Patter Recognition (CVPR)}, year = {2022} } ## References The data was generated using the transient renderer of Jarabo et al. [1]. [1] Jarabo, A., Marco, J., Muñoz, A., Buisan, R., Jarosz, W., Gutierrez, D.: "A framework for transient rendering" ACM Transactions on Graphics, SIGGRAPH ASIA, (2014).

An extension of the Cornell-Box Time-of-Flight Dataset (https://paperswithcode.com/dataset/cb-tof) containing moving objects. It follows the same data structure.

This corpus contains a large metadata-rich collection of fictional conversations extracted from raw movie scripts: - 220,579 conversational exchanges between 10,292 pairs of movie characters - involves 9,035 characters from 617 movies - in total 304,713 utterances - movie metadata included: - genres - release year - IMDB rating - number of IMDB votes - IMDB rating - character metadata included: - gender (for 3,774 characters) - position on movie credits (3,321 characters) Source: Cornell Movie-Dialogs Corpus

A corpus of movie quotes, annotated with memorability information, in which one is able to control for both the speaker and the setting of the quotes. Source: You Had Me at Hello: How Phrasing Affects Memorability