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A human-to-human Chinese dialog dataset (about 10k dialogs, 156k utterances), which contains multiple sequential dialogs for every pair of a recommendation seeker (user) and a recommender (bot).
DrivingStereo contains over 180k images covering a diverse set of driving scenarios, which is hundreds of times larger than the KITTI Stereo dataset. High-quality labels of disparity are produced by a model-guided filtering strategy from multi-frame LiDAR points.
The Double-Sided Braille Image dataset (DSBI) is a large-scale dataset for Braille image recognition. It has detailed Braille recto dots, verso dots and Braille cells annotation.
A novel benchmark dataset that includes a manually annotated point cloud for over 260 million laser scanning points into 100'000 (approx.) assets from Dublin LiDAR point cloud [12] in 2015. Objects are labelled into 13 classes using hierarchical levels of detail from large (i.e., building, vegetation and ground) to refined (i.e., window, door and tree) elements.
This dataset provides a large number of training and testing example which is sufficient for a deep learning approach to address Dunhuang Grotto Painting restoration.
The 'Deutsche Welle corpus for Information Extraction' (DWIE) is a multi-task dataset that combines four main Information Extraction (IE) annotation sub-tasks: (i) Named Entity Recognition (NER), (ii) Coreference Resolution, (iii) Relation Extraction (RE), and (iv) Entity Linking. DWIE is conceived as an entity-centric dataset that describes interactions and properties of conceptual entities on the level of the complete document.
DynaSent is an English-language benchmark task for ternary (positive/negative/neutral) sentiment analysis. DynaSent combines naturally occurring sentences with sentences created using the open-source Dynabench Platform, which facilities human-and-model-in-the-loop dataset creation. DynaSent has a total of 121,634 sentences, each validated by five crowdworkers.
The Drive&Act dataset is a state of the art multi modal benchmark for driver behavior recognition. The dataset includes 3D skeletons in addition to frame-wise hierarchical labels of 9.6 Million frames captured by 6 different views and 3 modalities (RGB, IR and depth).
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Kaiming He
X. Zhang
Shaoqing Ren
This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
Nikhila Ravi
Wan-Yen Lo
Ross B. Girshick
Laura Gustafson
Chloé Rolland
+7The Segment Anything Model (SAM) is introduced: a new task, model, and dataset for image segmentation, and its zero-shot performance is impressive – often competitive with or even superior to prior fully supervised results.
S. McKinney
Todor Markov
Jacob Menick
I. Sutskever
N. Keskar
+274GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs, is developed, a Transformer-based model pre-trained to predict the next token in a document which exhibits human-level performance on various professional and academic benchmarks.
Zewar Shah
Shan Zhiyong
Adnan
This project aims to provide an efficient and robust real-time emotion identification framework that makes use of paralinguistic factors such as intensity, pitch, and MFCC and employs Diffusion Map to reduce data redundancy and high dimensionality.
Noam Shazeer
Ashish Vaswani
Lukasz Kaiser
Jakob Uszkoreit
Niki Parmar
+3A new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely is proposed, which generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.
Yuhai Wu
This chapter presents techniques for statistical machine learning using Support Vector Machines (SVM) to recognize the patterns and classify them, predicting structured objects using SVM, k-nearest neighbor method for classification, and Naive Bayes classifiers.