Detecting Human-to-Human-or-Object (H2O) Interactions with DIABOLO

@article{Orcesi2021DetectingH,
  title={Detecting Human-to-Human-or-Object (H2O) Interactions with DIABOLO},
  author={Astrid Orcesi and Romaric Audigier and Fritz Poka Toukam and Bertrand Luvison},
  journal={2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)},
  year={2021},
  pages={1-8}
}
Detecting human interactions is crucial for human behavior analysis. Many methods have been proposed to deal with Human-to-Object Interaction (HOI) detection, i.e., detecting in an image which person and object interact together and classifying the type of interaction. However, Human-to-Human Interactions, such as social and violent interactions, are generally not considered in available HOI training datasets. As we think these types of interactions cannot be ignored and decorrelated from HOI… 
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SHOWING 1-10 OF 31 REFERENCES

Detecting and Recognizing Human-Object Interactions

A novel model is proposed that learns to predict an action-specific density over target object locations based on the appearance of a detected person and efficiently infers interaction triplets in a clean, jointly trained end-to-end system the authors call InteractNet.

Learning Human-Object Interaction Detection Using Interaction Points

This paper proposes a novel fully-convolutional approach that directly detects the interactions between human-object pairs and predicts interaction points, which directly localize and classify the inter-action.

Learning to Detect Human-Object Interactions

Experiments demonstrate that the proposed Human-Object Region-based Convolutional Neural Networks (HO-RCNN), by exploiting human-object spatial relations through Interaction Patterns, significantly improves the performance of HOI detection over baseline approaches.

Transferable Interactiveness Knowledge for Human-Object Interaction Detection

The core idea is to exploit an Interactiveness Network to learn the general interactiveness knowledge from multiple HOI datasets and perform Non-Interaction Suppression before HOI classification in inference and extensively evaluate the proposed method on HICO-DET and V-COCO datasets.

iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection

This paper proposes an instance-centric attention module that learns to dynamically highlight regions in an image conditioned on the appearance of each instance and allows an attention-based network to selectively aggregate features relevant for recognizing HOIs.

ConsNet: Learning Consistency Graph for Zero-Shot Human-Object Interaction Detection

ConsNet is proposed, a knowledge-aware framework that explicitly encodes the relations among objects, actions and interactions into an undirected graph called consistency graph, and exploits Graph Attention Networks (GATs) to propagate knowledge among HOI categories as well as their constituents.

HICO: A Benchmark for Recognizing Human-Object Interactions in Images

An in-depth analysis of representative current approaches is performed and it is shown that DNNs enjoy a significant edge and that semantic knowledge can significantly improve HOI recognition, especially for uncommon categories.

DIRV: Dense Interaction Region Voting for End-to-End Human-Object Interaction Detection

A novel one-stage HOI detection approach DIRV is proposed, based on a new concept called interaction region for the HOI problem, that outperforms existing state-of-the-arts methods by a large margin with the highest inference speed and lightest network architecture.

Human Object Interaction Detection via Multi-level Conditioned Network

A novel multi-level conditioned network that fuses extra spatial-semantic knowledge with visual features to enhance the reasoning capablity of CNN is proposed and is superior to the state-of-the-arts.

HCVRD: A Benchmark for Large-Scale Human-Centered Visual Relationship Detection

A webly-supervised approach to these problems is proposed and it is demonstrated that the proposed model provides a strong baseline on the authors' HCVRD dataset.