Video

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 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 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 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.

Egocentric Dataset of the University of Barcelona – Segmentation (EDUB-Seg) is a dataset for egocentric event segmentation acquired by the Narrative Clip, which takes a picture every 30 seconds. The dataset contains a total of 18,735 images captured by 7 different users during overall 20 days. To ensure diversity, all users were wearing the camera in different contexts: while attending a conference, on holiday, during the weekend, and during the week.

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.

HowTo100M Adverbs is a subset from HowTo100M with mined adverbs from 83 tasks in HowTo100M. The annotations were obtained from automatically transcribed narrations of instructional videos. The dataset contains originally 5,824 clips annotated with action-adverb pairs from 72 verbs and 6 adverbs. Source: How Do You Do It? Fine-Grained Action Understanding with Pseudo-Adverbs

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.

💡 Description A new benchmark, Multi-Phase, Multi-Transition, and Multi-Scenery Video Object Segmentation (M$^3$-VOS), to verify the ability of models to understand object phases, which consists of 479 high-resolution videos spanning over 10 distinct everyday scenarios. We collected 205,181 masks, with an average track duration of 14.27s. M$^3$-VOS covers 120+ categories of objects across 6 phases within 14 scenarios, encompassing 23 specific phase transitions. - Venue: CVPR2025 - Repository: Tool 🛠️, Page🏠 - Paper: arxiv.org/html/2412.13803v2 - Point of Contact: Jiaxin Li , Zixuan Chen

This is a dataset for video deinterlacing problem. The dataset contains 28 video sequences. Each sequence's length is 60 frames. Resolution of all video sequences is 1920x1080. TFF interlacing was used to get interlaced data from GT.

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.

Collects dense per-video-shot concept annotations. Source: Query-Focused Video Summarization: Dataset, Evaluation, and A Memory Network Based Approach

Our RealMCVSR dataset provides real-world HD video triplets concurrently recorded by Apple iPhone 12 Pro Max equipped with triple cameras having fixed focal lengths: ultra-wide (30mm), wide-angle (59mm), and telephoto (147mm). To concurrently record video triplets, we built an iOS app that provides full control over exposure parameters (i.e., shutter speed and ISO) of the cameras. For recording each scene, we set the cameras in the auto-exposure mode, where the shutter speeds of the three cameras are synced to avoid varying motion blur across a video triplet. ISOs are adjusted accordingly for each camera to pick up the same exposure. Each video is saved in the MOV format using HEVC/H.265 encoding with the HD resolution (1080 x 1920). The dataset contains triplets of 161 video clips with 23,107 frames in total. The video triplets are split into training, validation, and testing sets, each of which has 137, 8, and 16 triplets with 19,426, 1,141, and 2,540 frames, respectively.

Contains 5,193 video summaries of popular movies and TV series. SyMoN captures naturalistic storytelling videos for human audience made by human creators, and has higher story coverage and more frequent mental-state references than similar video-language story datasets.

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

Contains 140 videos with multiple human created summaries, which were acquired in a controlled experiment. Source: Diversity-aware Multi-Video Summarization

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

Youku-mPLUG is a large Chinese high-quality video-language dataset which is collected from Youku.com, a well-known Chinese video-sharing website, with strict criteria of safety, diversity, and quality. It contains 10 million video-text pairs for pre-training and 0.3 millon videos for downstream benchmarks covering Video-Text Retrieval, Video Captioning and Video Category Classification. Source: Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks Image Source: Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks

ActivityNet Adverbs is a subset from the ActivityNet dataset with extracted verb-adverb annotations. ActivityNet Adverbs contains 20 adverbs appearing across 114 actions, forming 643 unique action-adverb pairs in 3,099 video clips. Source: How Do You Do It? Fine-Grained Action Understanding with Pseudo-Adverbs

Caltech Fish Counting Dataset (CFC) is a large-scale dataset for detecting, tracking, and counting fish in sonar videos. This dataset contains over 1,500 videos sourced from seven different sonar cameras.

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

The Deep Fakes Dataset is a collection of "in the wild" portrait videos for deepfake detection. The videos in the dataset are diverse real-world samples in terms of the source generative model, resolution, compression, illumination, aspect-ratio, frame rate, motion, pose, cosmetics, occlusion, content, and context. They originate from various sources such as news articles, forums, apps, and research presentations; totalling up to 142 videos, 32 minutes, and 17 GBs. Synthetic videos are matched with their original counterparts when possible. Source: Deepfakes dataset

DeepSportradar is a benchmark suite of computer vision tasks, datasets and benchmarks for automated sport understanding. DeepSportradar currently supports four challenging tasks related to basketball: ball 3D localization, camera calibration, player instance segmentation and player re-identification. For each of the four tasks, a detailed description of the dataset, objective, performance metrics, and the proposed baseline method are provided. Source: DeepSportradar-v1: Computer Vision Dataset for Sports Understanding with High Quality Annotations Image Source: https://github.com/deepsportradar/instance-segmentation-challenge

Ego4D-HCap is a hierarchical video captioning dataset comprised of a three-tier hierarchy of captions: short clip-level captions, medium-length video segment descriptions, and long-range video-level summaries. To construct Ego4D-HCap, we leverage Ego4D, the largest publicly available egocentric video dataset. While Ego4D comes with time-stamped atomic captions and video-segment descriptions spanning up to 5 minutes, it lacks video-level summaries for longer video durations. To address this issue, we annotate a subset of 8,267 Ego4D videos with long-range video summaries, each spanning up to two hours. This enhancement provides a three-level hierarchy of captions.