Temporal Action Localization

This dataset is an OSN-transmitted (Online Social Network) version of the Columbia dataset. Unfortunately, OSNs automatically apply operations like compression and resizing, which reduce valuable information necessary for image forgery detection. As a result, this dataset presents a greater challenge for forgery detection compared to the original non-OSN-transmitted version. The dataset is available here: https://github.com/HighwayWu/ImageForensicsOSN - more specifically: https://drive.google.com/file/d/1uMNZdhX3bYAZNcVGlkCvrnj5lSLW1ld5/view?usp=sharing and was presented in: [Wu et al., 2022] Wu, H., Zhou, J., Tian, J., Liu, J., and Qiao, Y. (2022). Robust image forgery detection against transmission over online social networks. IEEE Transactions on Information Forensics and Security.

This dataset is an OSN-transmitted (Online Social Network) version of the DSO dataset. Unfortunately, OSNs automatically apply operations like compression and resizing, which reduce valuable information necessary for image forgery detection. As a result, this dataset presents a greater challenge for forgery detection compared to the original non-OSN-transmitted version. The dataset is available here: https://github.com/HighwayWu/ImageForensicsOSN - more specifically: https://drive.google.com/file/d/1uMNZdhX3bYAZNcVGlkCvrnj5lSLW1ld5/view?usp=sharing and was presented in: [Wu et al., 2022] Wu, H., Zhou, J., Tian, J., Liu, J., and Qiao, Y. (2022). Robust image forgery detection against transmission over online social networks. IEEE Transactions on Information Forensics and Security.

This dataset is an OSN-transmitted (Online Social Network) version of the DSO dataset. Unfortunately, OSNs automatically apply operations like compression and resizing, which reduce valuable information necessary for image forgery detection. As a result, this dataset presents a greater challenge for forgery detection compared to the original non-OSN-transmitted version. The dataset is available here: https://github.com/HighwayWu/ImageForensicsOSN - more specifically: https://drive.google.com/file/d/1uMNZdhX3bYAZNcVGlkCvrnj5lSLW1ld5/view?usp=sharing and was presented in: [Wu et al., 2022] Wu, H., Zhou, J., Tian, J., Liu, J., and Qiao, Y. (2022). Robust image forgery detection against transmission over online social networks. IEEE Transactions on Information Forensics and Security.

This dataset is an OSN-transmitted (Online Social Network) version of the DSO dataset. Unfortunately, OSNs automatically apply operations like compression and resizing, which reduce valuable information necessary for image forgery detection. As a result, this dataset presents a greater challenge for forgery detection compared to the original non-OSN-transmitted version. The dataset is available here: https://github.com/HighwayWu/ImageForensicsOSN - more specifically: https://drive.google.com/file/d/1uMNZdhX3bYAZNcVGlkCvrnj5lSLW1ld5/view?usp=sharing and was presented in: [Wu et al., 2022] Wu, H., Zhou, J., Tian, J., Liu, J., and Qiao, Y. (2022). Robust image forgery detection against transmission over online social networks. IEEE Transactions on Information Forensics and Security.

This dataset is an OSN-transmitted (Online Social Network) version of the DSO dataset. Unfortunately, OSNs automatically apply operations like compression and resizing, which reduce valuable information necessary for image forgery detection. As a result, this dataset presents a greater challenge for forgery detection compared to the original non-OSN-transmitted version. The dataset is available here: https://github.com/HighwayWu/ImageForensicsOSN - more specifically: https://drive.google.com/file/d/1uMNZdhX3bYAZNcVGlkCvrnj5lSLW1ld5/view?usp=sharing and was presented in: [Wu et al., 2022] Wu, H., Zhou, J., Tian, J., Liu, J., and Qiao, Y. (2022). Robust image forgery detection against transmission over online social networks. IEEE Transactions on Information Forensics and Security.

FLAG3D is a large-scale 3D fitness activity dataset with language instruction containing 180K sequences of 60 categories. FLAG3D features the following three aspects: 1) accurate and dense 3D human pose captured from advanced MoCap system to handle the complex activity and large movement, 2) detailed and professional language instruction to describe how to perform a specific activity, 3) versatile video resources from a high-tech MoCap system, rendering software, and cost-effective smartphones in natural environments. Source: FLAG3D: A 3D Fitness Activity Dataset with Language Instruction Image Source: https://arxiv.org/pdf/2212.04638v1.pdf

The FR-FS dataset contains 417 videos collected from FIV dataset and Pingchang 2018 Winter Olympic Games. FR-FS contains the critical movements of the athlete’s take-off, rotation, and landing. Among them, 276 are smooth landing videos, and 141 are fall videos. To test the generalization performance of our proposed model, we randomly select 50% of the videos from the fall and landing videos as the training set and the testing set.

This is the dataset for knowledge editing. It contains six tasks: ZsRE, $Wiki_{recent}$, $Wiki_{counterfact}$, WikiBio, ConvSent and Sanitation. This repo shows the former 4 tasks and you can get the data for ConvSent and Sanitation from their original papers.

This dataset is an OSN-transmitted (Online Social Network) version of the NIST dataset (https://www.nist.gov/itl/iad/mig/nimble-challenge-2017-evaluation). Unfortunately, OSNs automatically apply operations like compression and resizing, which reduce valuable information necessary for image forgery detection. As a result, this dataset presents a greater challenge for forgery detection compared to the original non-OSN-transmitted version. The dataset is available here: https://github.com/HighwayWu/ImageForensicsOSN - more specifically: https://drive.google.com/file/d/1uMNZdhX3bYAZNcVGlkCvrnj5lSLW1ld5/view?usp=sharing and was presented in: [Wu et al., 2022] Wu, H., Zhou, J., Tian, J., Liu, J., and Qiao, Y. (2022). Robust image forgery detection against transmission over online social networks. IEEE Transactions on Information Forensics and Security.

This dataset is an OSN-transmitted (Online Social Network) version of the NIST dataset (https://www.nist.gov/itl/iad/mig/nimble-challenge-2017-evaluation). Unfortunately, OSNs automatically apply operations like compression and resizing, which reduce valuable information necessary for image forgery detection. As a result, this dataset presents a greater challenge for forgery detection compared to the original non-OSN-transmitted version. The dataset is available here: https://github.com/HighwayWu/ImageForensicsOSN - more specifically: https://drive.google.com/file/d/1uMNZdhX3bYAZNcVGlkCvrnj5lSLW1ld5/view?usp=sharing and was presented in: [Wu et al., 2022] Wu, H., Zhou, J., Tian, J., Liu, J., and Qiao, Y. (2022). Robust image forgery detection against transmission over online social networks. IEEE Transactions on Information Forensics and Security.

This dataset is an OSN-transmitted (Online Social Network) version of the NIST dataset (https://www.nist.gov/itl/iad/mig/nimble-challenge-2017-evaluation). Unfortunately, OSNs automatically apply operations like compression and resizing, which reduce valuable information necessary for image forgery detection. As a result, this dataset presents a greater challenge for forgery detection compared to the original non-OSN-transmitted version. The dataset is available here: https://github.com/HighwayWu/ImageForensicsOSN - more specifically: https://drive.google.com/file/d/1uMNZdhX3bYAZNcVGlkCvrnj5lSLW1ld5/view?usp=sharing and was presented in: [Wu et al., 2022] Wu, H., Zhou, J., Tian, J., Liu, J., and Qiao, Y. (2022). Robust image forgery detection against transmission over online social networks. IEEE Transactions on Information Forensics and Security.

This dataset is an OSN-transmitted (Online Social Network) version of the NIST dataset (https://www.nist.gov/itl/iad/mig/nimble-challenge-2017-evaluation). Unfortunately, OSNs automatically apply operations like compression and resizing, which reduce valuable information necessary for image forgery detection. As a result, this dataset presents a greater challenge for forgery detection compared to the original non-OSN-transmitted version. The dataset is available here: https://github.com/HighwayWu/ImageForensicsOSN - more specifically: https://drive.google.com/file/d/1uMNZdhX3bYAZNcVGlkCvrnj5lSLW1ld5/view?usp=sharing and was presented in: [Wu et al., 2022] Wu, H., Zhou, J., Tian, J., Liu, J., and Qiao, Y. (2022). Robust image forgery detection against transmission over online social networks. IEEE Transactions on Information Forensics and Security.

The TCG dataset is used to evaluate Traffic Control Gesture recognition for autonomous driving. The dataset is based on 3D body skeleton input to perform traffic control gesture classification on every time step. The dataset consists of 250 sequences from several actors, ranging from 16 to 90 seconds per sequence. Source: https://arxiv.org/pdf/2007.16072.pdf Image Source: https://github.com/againerju/tcg_recognition

TinyVIRAT contains natural low-resolution activities. The actions in TinyVIRAT videos have multiple labels and they are extracted from surveillance videos which makes them realistic and more challenging. Source: TinyVIRAT: Low-resolution Video Action Recognition

Covers 5 generic driving scenarios, with a total of 25 distinct action classes. It contains more than 15K full HD, 5s long videos acquired in various driving conditions, weathers, daytimes and environments, complemented with a common and realistic set of sensor measurements. This amounts to more than 2.25M frames, each annotated with an action label, corresponding to 600 samples per action class. Source: VIENA2: A Driving Anticipation Dataset

Consists of two pedestrian trajectory datasets, CITR dataset and DUT dataset, so that the pedestrian motion models can be further calibrated and verified, especially when vehicle influence on pedestrians plays an important role. CITR dataset consists of experimentally designed fundamental VCI scenarios (front, back, and lateral VCIs) and provides unique ID for each pedestrian, which is suitable for exploring a specific aspect of VCI. DUT dataset gives two ordinary and natural VCI scenarios in crowded university campus, which can be used for more general purpose VCI exploration. Source: Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus

The Composable activities dataset consists of 693 videos that contain activities in 16 classes performed by 14 actors. Each activity is composed of 3 to 11 atomic actions. RGB-D data for each sequence is captured using a Microsoft Kinect sensor and estimate position of relevant body joints. The dataset provides annotations of the activity for each video and the actions for each of the four human parts (left/right arm and leg) for each frame in every video. Source: Discriminative Hierarchical Modeling of Spatio-Temporally Composable Human Activities Image Source: https://ialillo.sitios.ing.uc.cl/ActionsCVPR2014/

A novel dataset that represents complex conversational interactions between two individuals via 3D pose. 8 pairwise interactions describing 7 separate conversation based scenarios were collected using two Kinect depth sensors. Source: From Pose to Activity: Surveying Datasets and Introducing CONVERSE

DECADE is a large-scale dataset of ego-centric videos from a dog's perspective as well as her corresponding movements. Source: Who Let The Dogs Out? Modeling Dog Behavior From Visual Data

Largest, first-of-its-kind, in-the-wild, fine-grained workout/exercise posture analysis dataset, covering three different exercises: BackSquat, Barbell Row, and Overhead Press. Seven different types of exercise errors are covered. Unlabeled data is also provided to facilitate self-supervised learning.

HDM05 is a MoCap (motion capture) dataset. It contains more than three hours of systematically recorded and well-documented motion capture data in the C3D as well as in the ASF/AMC data format. HDM05 contains almost 2337 sequences with 130 motion classes performed by 5 different actors. Source: http://resources.mpi-inf.mpg.de/HDM05/ Image Source: https://arxiv.org/pdf/1908.05750.pdf

A dataset for benchmarking action recognition algorithms in natural environments, while making use of 3D information. The dataset contains around 650 video clips, across 14 classes. In addition, two state of the art action recognition algorithms are extended to make use of the 3D data, and five new interest point detection strategies are also proposed, that extend to the 3D data. Source: Hollywood 3D: Recognizing Actions in 3D Natural Scenes

Replay data from human players and AI agents navigating in a 3D game environment. Introduced in "Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation" [ICML 2021] to learn how to evaluate humanlike behavior in agents.

The OREBA dataset aims to provide a comprehensive multi-sensor recording of communal intake occasions for researchers interested in automatic detection of intake gestures. Two scenarios are included, with 100 participants for a discrete dish and 102 participants for a shared dish, totalling 9069 intake gestures. Available sensor data consists of synchronized frontal video and IMU with accelerometer and gyroscope for both hands.

UAV-GESTURE is a dataset for UAV control and gesture recognition. It is an outdoor recorded video dataset for UAV commanding signals with 13 gestures suitable for basic UAV navigation and command from general aircraft handling and helicopter handling signals. It contains 119 high-definition video clips consisting of 37,151 frames. Source: UAV-GESTURE: A Dataset for UAV Control and Gesture Recognition

3DYoga90 is organized within a three-level label hierarchy. It stands out as one of the most comprehensive open datasets, featuring the largest collection of RGB videos and 3D skeleton sequences among publicly available resources.

The Consented Activities of People (CAP) dataset is a fine grained activity dataset for visual AI research curated using the Visym Collector platform. The CAP dataset contains annotated videos of fine-grained activity classes of consented people. Videos are recorded from mobile devices around the world from a third person viewpoint looking down on the scene from above, containing subjects performing every day activities. Videos are annotated with bounding box tracks around the primary actor along with temporal start/end frames for each activity instance, and distributed in vipy json format. An interactive visualization and video summary is available for review in the dataset distribution site.

HalluEditBench is a comprehensive benchmark for evaluating knowledge editing methods' effectiveness in correcting real-world hallucinations. HalluEdit features a rigorously constructed dataset spanning nine domains and 26 topics. It evaluates methods across five dimensions: Efficacy, Generalization, Portability, Locality, and Robustness.

I2-2000FPS is the first high-speed video dataset offering an unprecedented temporal resolution of 2000 frames per second (fps). Captured using the commercially available Chronos 1.4 high-speed CMOS camera, the dataset includes a diverse range of objects varying in size, shape, orientation, and motion, as well as various camera movements. This dataset is designed to enable research in areas such as motion analysis, object tracking, and scene understanding at extreme temporal resolutions. Potential applications span fields like sports analysis, robotics, autonomous navigation, and high-speed videography.

Designed to evaluate the open view classification problem under the surveillance environment. In total, MCAD contains 14,298 action samples from 18 action categories, which are performed by 20 subjects and independently recorded with 5 cameras. Source: Multi-Camera Action Dataset for Cross-Camera Action Recognition Benchmarking

The MultiviewC dataset mainly contributes to multiview cattle action recognition, 3D objection detection and tracking. We build a novel synthetic dataset MultiviewC through UE4 based on real cattle video dataset which is offered by CISRO. The format of our data set has been adjusted on the basis of MultiviewX for set-up, annotation and files structure.

NTU-X is an extended version of popular NTU dataset.

Occ-Traj120 is a trajectory dataset that contains occupancy representations of different local-maps with associated trajectories. This dataset contains 400 locally-structured maps with occupancy representation and roughly around 120K trajectories in total. Source: https://github.com/soraxas/Occ-Traj120 Image Source: https://github.com/soraxas/Occ-Traj120

RISE is a large-scale video dataset for Recognizing Industrial Smoke Emissions. A citizen science approach was adopted to collaborate with local community members to annotate whether a video clip has smoke emissions. The dataset contains 12,567 clips from 19 distinct views from cameras that monitored three industrial facilities. These daytime clips span 30 days over two years, including all four seasons. Source: https://arxiv.org/abs/2005.06111 Image Source: https://github.com/CMU-CREATE-Lab/deep-smoke-machine

A curated and 3-D pose-annotated subset of RGB videos sourced from Kinetics-700, a large-scale action dataset. Source: Quo Vadis, Skeleton Action Recognition ?

A dataset derived from the recently introduced Mimetics dataset. Source: Quo Vadis, Skeleton Action Recognition ?