Interactiveness Field in Human-Object Interactions

• URL: http://arxiv.org/abs/2204.07718v1
• Date: Sat, 16 Apr 2022 05:09:25 GMT
• Title: Interactiveness Field in Human-Object Interactions
• Authors: Xinpeng Liu, Yong-Lu Li, Xiaoqian Wu, Yu-Wing Tai, Cewu Lu, Chi-Keung Tang
• Abstract summary: We introduce a previously overlooked interactiveness bimodal prior: given an object in an image, after pairing it with the humans, the generated pairs are either mostly non-interactive, or mostly interactive. We propose new energy constraints based on the cardinality and difference in the inherent "interactiveness field" underlying interactive versus non-interactive pairs. Our method can detect more precise pairs and thus significantly boost HOI detection performance.
• Score: 89.13149887013905
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human-Object Interaction (HOI) detection plays a core role in activity understanding. Though recent two/one-stage methods have achieved impressive results, as an essential step, discovering interactive human-object pairs remains challenging. Both one/two-stage methods fail to effectively extract interactive pairs instead of generating redundant negative pairs. In this work, we introduce a previously overlooked interactiveness bimodal prior: given an object in an image, after pairing it with the humans, the generated pairs are either mostly non-interactive, or mostly interactive, with the former more frequent than the latter. Based on this interactiveness bimodal prior we propose the "interactiveness field". To make the learned field compatible with real HOI image considerations, we propose new energy constraints based on the cardinality and difference in the inherent "interactiveness field" underlying interactive versus non-interactive pairs. Consequently, our method can detect more precise pairs and thus significantly boost HOI detection performance, which is validated on widely-used benchmarks where we achieve decent improvements over state-of-the-arts. Our code is available at https://github.com/Foruck/Interactiveness-Field.

Related papers

• Disentangled Interaction Representation for One-Stage Human-Object Interaction Detection [70.96299509159981]
  Human-Object Interaction (HOI) detection is a core task for human-centric image understanding. Recent one-stage methods adopt a transformer decoder to collect image-wide cues that are useful for interaction prediction. Traditional two-stage methods benefit significantly from their ability to compose interaction features in a disentangled and explainable manner.
  arXiv  Detail & Related papers  (2023-12-04T08:02:59Z)
• HODN: Disentangling Human-Object Feature for HOI Detection [51.48164941412871]
  We propose a Human and Object Disentangling Network (HODN) to model the Human-Object Interaction (HOI) relationships explicitly. Considering that human features are more contributive to interaction, we propose a Human-Guide Linking method to make sure the interaction decoder focuses on the human-centric regions. Our proposed method achieves competitive performance on both the V-COCO and the HICO-Det Linking datasets.
  arXiv  Detail & Related papers  (2023-08-20T04:12:50Z)
• Detecting Human-to-Human-or-Object (H2O) Interactions with DIABOLO [29.0200561485714]
  We propose a new interaction dataset to deal with both types of human interactions: Human-to-Human-or-Object (H2O) In addition, we introduce a novel taxonomy of verbs, intended to be closer to a description of human body attitude in relation to the surrounding targets of interaction. We propose DIABOLO, an efficient subject-centric single-shot method to detect all interactions in one forward pass.
  arXiv  Detail & Related papers  (2022-01-07T11:00:11Z)
• RR-Net: Injecting Interactive Semantics in Human-Object Interaction Detection [40.65483058890176]
  Latest end-to-end HOI detectors are short of relation reasoning, which leads to inability to learn HOI-specific interactive semantics for predictions. We first present a progressive Relation-aware Frame, which brings a new structure and parameter sharing pattern for interaction inference. Based on modules above, we construct an end-to-end trainable framework named Relation Reasoning Network (abbr. RR-Net)
  arXiv  Detail & Related papers  (2021-04-30T14:03:10Z)
• DIRV: Dense Interaction Region Voting for End-to-End Human-Object Interaction Detection [53.40028068801092]
  We propose a novel one-stage HOI detection approach based on a new concept called interaction region for the HOI problem. Unlike previous methods, our approach concentrates on the densely sampled interaction regions across different scales for each human-object pair. In order to compensate for the detection flaws of a single interaction region, we introduce a novel voting strategy.
  arXiv  Detail & Related papers  (2020-10-02T13:57:58Z)
• A Graph-based Interactive Reasoning for Human-Object Interaction Detection [71.50535113279551]
  We present a novel graph-based interactive reasoning model called Interactive Graph (abbr. in-Graph) to infer HOIs. We construct a new framework to assemble in-Graph models for detecting HOIs, namely in-GraphNet. Our framework is end-to-end trainable and free from costly annotations like human pose.
  arXiv  Detail & Related papers  (2020-07-14T09:29:03Z)
• Learning Human-Object Interaction Detection using Interaction Points [140.0200950601552]
  We propose a novel fully-convolutional approach that directly detects the interactions between human-object pairs. Our network predicts interaction points, which directly localize and classify the inter-action. Experiments are performed on two popular benchmarks: V-COCO and HICO-DET.
  arXiv  Detail & Related papers  (2020-03-31T08:42:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.