# GTNet:Guided Transformer Network for Detecting Human-Object Interactions

Source: [GTNet:Guided Transformer Network for Detecting Human-Object Interactions](<https://www.sota2.com/research/paper/68fa70951e209131cea4bfc5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:44:59.954Z

Authors: A S M Iftekhar, Satish Kumar, R. Austin McEver, Suya You, B. S. Manjunath

Published: 2021-08-02T02:06:33.000Z

## Abstract

The human-object interaction (HOI) detection task refers to localizing humans, localizing objects, and predicting the interactions between each human-object pair. HOI is considered one of the fundamental steps in truly understanding complex visual scenes. For detecting HOI, it is important to utilize relative spatial configurations and object semantics to find salient spatial regions of images that highlight the interactions between human object pairs. This issue is addressed by the novel self-attention based guided transformer network, GTNet. GTNet encodes this spatial contextual information in human and object visual features via self-attention while achieving state of the art results on both the V-COCO and HICO-DET datasets. Code will be made available online.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2108.00596>)

[PDF](<https://arxiv.org/pdf/2108.00596.pdf>)