DeeperForensics

DeeperForensics is a large-scale face forgery detection dataset with 60, 000 videos constituted by a total of 17.6 million frames, 10 times larger than existing datasets of the same kind. Extensive perturbations are applied to obtain a more challenging benchmark of larger scale and higher diversity. All source videos in DeeperForensics are carefully collected, and fake videos are generated by a newly proposed end-to-end face swapping framework.

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FashionGAN Dataset

New annotations (languages and segmentation maps) on the subset of the DeepFashion dataset. The data is used in our ICCV 2017 paper "Be Your Own Prada: Fashion Synthesis with Structural Coherence".

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Visual Discriminative Question Generation (VDQG) Dataset

The dataset contains 11202 ambiguous image pairs collected from Visual Genome. Each image pair is annotated with 4.6 discriminative questions and 5.9 non-discriminative questions on average. The dataset is used in our ICCV 2017 paper "Learning to Disambiguate by Asking Discriminative Questions".

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WildLife Documentary (WLD) Dataset

The dataset contains 15 documentary films that are downloaded from YouTube, whose durations vary from 9 minutes to as long as 50 minutes, and the total number of frames is more than 747,000. More than 4000 object tracklets of 65 categories are annotated. The dataset is used in our CVPR 2017 paper "Discover and Learn New Objects from Documentaries".

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Expression in-the-Wild (ExpW) Dataset

We built a new database named as Expression in-the-Wild (ExpW) dataset that contains 91,793 faces manually labeled with expressions. Each of the face images was manually annotated as one of the seven basic expression categories: “angry”, “disgust”, “fear”, “happy”, “sad”, “surprise”, or “neutral”. The number of images in ExpW is larger and the face variations are more diverse than many existing databases. The dataset is used in our paper "From Facial Expression Recognition to Interpersonal Relation Prediction".

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Pedestrian Color Naming Dataset

To facilitate the learning of evaluation of pedestrian color naming, we build a new large-scale dataset, named Pedestrian Color Naming (PCN) dataset, which contains 14,213 images, each of which hand-labeled with color label for each pixel. All images in the PCN dataset are obtained from the Market- 1501 dataset.

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WIDER ATTRIBUTE Dataset

WIDER ATTRIBUTE dataset is a human attribute recognition benchmark dataset, of which images are selected from the publicly available WIDER dataset. There are a total of 13789 images. We annotate a bounding box for each person in these images, but no more than 20 people (with top resolutions) in a crowd image, resulting in 57524 boxes in total and 4+ boxes per image on average. For each bounding box, we label 14 distinct human attributes, resulting in a total of 805336 labels.

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General 100 Dataset

General-100 dataset contains 100 bmp-format images (with no compression). We used this dataset in our FSRCNN ECCV 2016 paper. The size of these 100 images ranges from 710 x 704 (large) to 131 x 112 (small). They are all of good quality with clear edges but fewer smooth regions (e.g., sky and ocean), thus are very suitable for the super-resolution training.

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WIDER FACE Dataset

WIDER FACE dataset is a face detection benchmark dataset, of which images are selected from the publicly available WIDER dataset. We choose 32,203 images and label 393,703 faces with a high degree of variability in scale, pose and occlusion as depicted in the sample images. WIDER FACE dataset is organized based on 61 event classes. For each event class, we randomly select 40%/10%/50% data as training, validation and testing sets. We adopt the same evaluation metric employed in the PASCAL VOC dataset. Similar to MALF and Caltech datasets, we do not release bounding box ground truth for the test images. Users are required to submit final prediction files, which we shall proceed to evaluate.

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Social Relation Dataset

The dataset is used in our ICCV 2015 paper. We define the social relation traits based on the interpersonal circle proposed by Kiesler, where human relations are divided into 16 segments Each segment has its opposite side in the circle, such as 'friendly and hostile'. To investigate the detectability of social relations from a pair of face images, we build a new dataset, containing 8,306 images chosen from web and movies. Each image is labelled with faces’ bounding boxes and their pairwise relations. This is the first face dataset measuring social relation traits and it is challenging because of large face variations including poses, occlusions, and illuminations.

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The Comprehensive Cars (CompCars) Dataset

The dataset is used in our CVPR paper. The Comprehensive Cars (CompCars) dataset contains data from two scenarios, including images from web-nature and surveillance-nature. The web-nature data contains 163 car makes with 1,716 car models. There are a total of 136,726 images capturing the entire cars and 27,618 images capturing the car parts. The full car images are labeled with bounding boxes and viewpoints. Each car model is labeled with five attributes, including maximum speed, displacement, number of doors, number of seats, and type of car. The surveillance-nature data contains 50,000 car images captured in the front view.

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Multi-Task Facial Landmark (MTFL) Dataset

The dataset is used in our ECCV paper for training a multi-task deep model of facial landmark detection. It consists 12,995 face images, each of which is annotated with bounding box and five landmarks, i.e. centers of the eyes, nose, corners of the mouth. In addition, it includes related tasks annotations, including 'smiling', 'wearing glasses', 'gender', and 'head pose'.

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PEdesTrian Attribute (PETA) Dataset

The dataset is by far the largest of its kind, covering more than 60 attributes on 19000 images. In comparison with existing datasets, PETA is more diverse and challenging in terms of imagery variations and complexity.

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QMUL Junction Dataset

A busy traffic dataset for research on activity analysis and behaviour understanding.

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