Parallel Reasoning Network for Human-Object Interaction Detection

• URL: http://arxiv.org/abs/2301.03510v1
• Date: Mon, 9 Jan 2023 17:00:34 GMT
• Title: Parallel Reasoning Network for Human-Object Interaction Detection
• Authors: Huan Peng, Fenggang Liu, Yangguang Li, Bin Huang, Jing Shao, Nong Sang, Changxin Gao
• Abstract summary: We propose a new transformer-based method named Parallel Reasoning Network(PR-Net) PR-Net constructs two independent predictors for instance-level localization and relation-level understanding. Our PR-Net has achieved competitive results on HICO-DET and V-COCO benchmarks.
• Score: 53.422076419484945
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human-Object Interaction (HOI) detection aims to learn how human interacts with surrounding objects. Previous HOI detection frameworks simultaneously detect human, objects and their corresponding interactions by using a predictor. Using only one shared predictor cannot differentiate the attentive field of instance-level prediction and relation-level prediction. To solve this problem, we propose a new transformer-based method named Parallel Reasoning Network(PR-Net), which constructs two independent predictors for instance-level localization and relation-level understanding. The former predictor concentrates on instance-level localization by perceiving instances' extremity regions. The latter broadens the scope of relation region to reach a better relation-level semantic understanding. Extensive experiments and analysis on HICO-DET benchmark exhibit that our PR-Net effectively alleviated this problem. Our PR-Net has achieved competitive results on HICO-DET and V-COCO benchmarks.

Related papers

• Qualitative Prediction of Multi-Agent Spatial Interactions [5.742409080817885]
  We present and benchmark three new approaches to model and predict multi-agent interactions in dense scenes. The proposed solutions take into account static and dynamic context to predict individual interactions. They exploit an input- and a temporal-attention mechanism, and are tested on medium and long-term time horizons.
  arXiv  Detail & Related papers  (2023-06-30T18:08:25Z)
• Knowledge Guided Bidirectional Attention Network for Human-Object Interaction Detection [3.0915392100355192]
  We argue that the independent use of the bottom-up parsing strategy in HOI is counter-intuitive and could lead to the diffusion of attention. We introduce a novel knowledge-guided top-down attention into HOI, and propose to model the relation parsing as a "look and search" process. We implement the process via unifying the bottom-up and top-down attention in a single encoder-decoder based model.
  arXiv  Detail & Related papers  (2022-07-16T16:42:49Z)
• ACP++: Action Co-occurrence Priors for Human-Object Interaction Detection [102.9428507180728]
  A common problem in the task of human-object interaction (HOI) detection is that numerous HOI classes have only a small number of labeled examples. We observe that there exist natural correlations and anti-correlations among human-object interactions. We present techniques to learn these priors and leverage them for more effective training, especially on rare classes.
  arXiv  Detail & Related papers  (2021-09-09T06:02:50Z)
• 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)
• Reformulating HOI Detection as Adaptive Set Prediction [25.44630995307787]
  We reformulate HOI detection as an adaptive set prediction problem. We propose an Adaptive Set-based one-stage framework (AS-Net) with parallel instance and interaction branches. Our method outperforms previous state-of-the-art methods without any extra human pose and language features.
  arXiv  Detail & Related papers  (2021-03-10T10:40:33Z)
• Detecting Human-Object Interactions with Action Co-occurrence Priors [108.31956827512376]
  A common problem in human-object interaction (HOI) detection task is that numerous HOI classes have only a small number of labeled examples. We observe that there exist natural correlations and anti-correlations among human-object interactions. We present techniques to learn these priors and leverage them for more effective training, especially in rare classes.
  arXiv  Detail & Related papers  (2020-07-17T02:47:45Z)
• Novel Human-Object Interaction Detection via Adversarial Domain Generalization [103.55143362926388]
  We study the problem of novel human-object interaction (HOI) detection, aiming at improving the generalization ability of the model to unseen scenarios. The challenge mainly stems from the large compositional space of objects and predicates, which leads to the lack of sufficient training data for all the object-predicate combinations. We propose a unified framework of adversarial domain generalization to learn object-invariant features for predicate prediction.
  arXiv  Detail & Related papers  (2020-05-22T22:02:56Z)
• 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)
• Cascaded Human-Object Interaction Recognition [175.60439054047043]
  We introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding. At each stage, an instance localization network progressively refines HOI proposals and feeds them into an interaction recognition network. With our carefully-designed human-centric relation features, these two modules work collaboratively towards effective interaction understanding.
  arXiv  Detail & Related papers  (2020-03-09T17:05:04Z)

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.