# FreeA: Human-object Interaction Detection using Free Annotation Labels

arXiv: https://arxiv.org/abs/2403.01840 · HTML: https://mlsanity.com/paper/2403.01840/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 40 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 30 of 33)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Learning to Detect Human-Object Interactions](https://mlsanity.com/paper/1702.05448/) (2017)
- [Detecting and Recognizing Human-Object Interactions](https://mlsanity.com/paper/1704.07333/) (2017)
- [QPIC: Query-Based Pairwise Human-Object Interaction Detection with Image-Wide Contextual Information](https://mlsanity.com/paper/2103.05399/) (2021)
- [PPDM: Parallel Point Detection and Matching for Real-time Human-Object Interaction Detection](https://mlsanity.com/paper/1912.12898/) (2019)
- [End-to-End Human Object Interaction Detection with HOI Transformer](https://mlsanity.com/paper/2103.04503/) (2021)
- [iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection](https://mlsanity.com/paper/1808.10437/) (2018)
- [GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection](https://mlsanity.com/paper/2203.13954/) (2022)
- [VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions](https://mlsanity.com/paper/2003.05541/) (2020)
- [Detailed 2D-3D Joint Representation for Human-Object Interaction](https://mlsanity.com/paper/2004.08154/) (2020)
- [Spatially Conditioned Graphs for Detecting Human-Object Interactions](https://mlsanity.com/paper/2012.06060/) (2020)
- [Pose-aware Multi-level Feature Network for Human Object Interaction Detection](https://mlsanity.com/paper/1909.08453/) (2019)
- [HOI Analysis: Integrating and Decomposing Human-Object Interaction](https://mlsanity.com/paper/2010.16219/) (2020)
- [Detecting Human-Object Interaction via Fabricated Compositional Learning](https://mlsanity.com/paper/2103.08214/) (2021)
- [Affordance Transfer Learning for Human-Object Interaction Detection](https://mlsanity.com/paper/2104.02867/) (2021)
- [HOICLIP: Efficient Knowledge Transfer for HOI Detection with Vision-Language Models](https://mlsanity.com/paper/2303.15786/) (2023)
- [Exploring Predicate Visual Context in Detecting Human-Object Interactions](https://mlsanity.com/paper/2308.06202/) (2023)
- [ConsNet: Learning Consistency Graph for Zero-Shot Human-Object Interaction Detection](https://mlsanity.com/paper/2008.06254/) (2020)
- [RLIPv2: Fast Scaling of Relational Language-Image Pre-training](https://mlsanity.com/paper/2308.09351/) (2023)
- [Relational Context Learning for Human-Object Interaction Detection](https://mlsanity.com/paper/2304.04997/) (2023)
- [PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN](https://mlsanity.com/paper/1708.01956/) (2017)
- [Improving Human-Object Interaction Detection via Phrase Learning and Label Composition](https://mlsanity.com/paper/2112.07383/) (2021)
- [Explanation-based Weakly-supervised Learning of Visual Relations with Graph Networks](https://mlsanity.com/paper/2006.09562/) (2020)
- [ACP++: Action Co-occurrence Priors for Human-Object Interaction Detection](https://mlsanity.com/paper/2109.04047/) (2021)
- [Weakly-supervised HOI Detection via Prior-guided Bi-level Representation Learning](https://mlsanity.com/paper/2303.01313/) (2023)
- [Detecting Human-Object Interaction with Mixed Supervision](https://mlsanity.com/paper/2011.04971/) (2020)

## Abstract (arXiv)

Recent human-object interaction (HOI) detection methods depend on extensively annotated image datasets, which require a significant amount of manpower. In this paper, we propose a novel self-adaptive, language-driven HOI detection method, termed FreeA. This method leverages the adaptability of the text-image model to generate latent HOI labels without requiring manual annotation. Specifically, FreeA aligns image features of human-object pairs with HOI text templates and employs a knowledge-based masking technique to decrease improbable interactions. Furthermore, FreeA implements a proposed method for matching interaction correlations to increase the probability of actions associated with a particular action, thereby improving the generated HOI labels. Experiments on two benchmark datasets showcase that FreeA achieves state-of-the-art performance among weakly supervised HOI competitors. Our proposal gets +13.29 (159%uparrow) mAP and +17.30 (98%uparrow) mAP than the newest ``Weakly'' supervised model, and +7.19 (28%uparrow) mAP and +14.69 (34%uparrow) mAP than the latest ``Weakly+'' supervised model, respectively, on HICO-DET and V-COCO datasets, more accurate in localizing and classifying the interactive actions. The source code will be made public.