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Works18 from public data
- NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding1,846
This work introduces a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames, and investigates a novel one-shot 3D activity recognition problem on this dataset.
- Video pornography detection through deep learning techniques and motion information140
The premise that incorporating motion information in the models can alleviate the problem of mapping skin exposure to pornographic content, and advances the bar on automated pornography detection with the use of motion information and deep learning architectures is based on.
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- Interaction Relational Network for Mutual Action Recognition97
This work proposes a simpler yet very powerful architecture, named Interaction Relational Network, which utilizes minimal prior knowledge about the structure of the human body to identify by itself how to relate the body parts from the individuals interacting and is capable of paramount sequential relational reasoning.
- Leveraging deep neural networks to fight child pornography in the age of social media88
Cutting-edge data-driven concepts and deep convolutional neural networks are leveraged to harness enough characterization aspects from a wide range of images and point out the presence of child pornography content in an image.
- Pornography classification: The hidden clues in video space–time82
A space-temporal interest point detector and descriptor called TRoF is introduced, custom-tailored for efficient (low processing time and memory footprint) and effective and effective (high classification accuracy and low false negative rate) motion description, particularly suited to the task at hand.
- Skeleton-based relational reasoning for group activity analysis58
This paper leverages the skeleton information to learn the interactions between the individuals straight from it, and proposes the proposed method GIRN, multiple relationship types are inferred from independent modules, that describe the relations between the joints pair-by-pair.
- Multimodal data fusion for sensitive scene localization33
This work proposes a novel multimodal fusion approach to sensitive scene localization, which can be applied to diverse types of sensitive content, without the need for step modifications (general purpose).
- Where is my puppy? Retrieving lost dogs by facial features31
This paper contrasts four ready-to-use human facial recognizers to two original solutions based upon convolutional neural networks: BARK (inspired in architecture-optimized networks employed for human facial recognition) and WOOF (based upon off-the-shelf OverFeat features), showing that dog facial recognition is not a trivial extension of human facial Recognition.
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- RECOD at MediaEval 2014: Violent Scenes Detection Task.15
This paper presents the RECOD approaches used in the MediaEval 2014 Violent Scenes Detection task, based on the combination of visual, audio, and text features using a fusion scheme.
- Interaction Recognition Through Body Parts Relation Reasoning14
This work proposes a more simple yet very powerful architecture, named Interaction Relational Network, which utilizes minimal prior knowledge about the structure of the data, and drives the network to learn to identify how to relate the body parts of the persons interacting, in order to better discriminate among the possible interactions.
- Direct high-throughput deconvolution of non-canonical bases via nanopore sequencing and bootstrapped learning8
It is demonstrated that XNA templates containing non-canonical bases can be directly and robustly sequenced on a MinION sequencer from Oxford Nanopore Technologies to obtain signal data that is significantly distinct from DNA controls.
- RECOD at MediaEval 2015: Affective Impact of Movies Task7
This paper presents the approach used by the RECOD team to address the challenges provided in the MediaEval 2015 Affective Impact of Movies Task, and designed various video classiers using dierent approaches, ranging from majority voting to machine-learned techniques on the training dataset.
- Pornographic cartoon video detection through deep neural networks1
This work evaluates how state-of-the-art solutions for natural videos (with humans) perform in cartoons and proposes a new method with higher accuracy, showing that treating cartoons independently can improve sensitive content filtering.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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