Open-World Human-Object Interaction Detection via Multi-modal Prompts

• URL: http://arxiv.org/abs/2406.07221v1
• Date: Tue, 11 Jun 2024 13:01:45 GMT
• Title: Open-World Human-Object Interaction Detection via Multi-modal Prompts
• Authors: Jie Yang, Bingliang Li, Ailing Zeng, Lei Zhang, Ruimao Zhang,
• Abstract summary: MP-HOI is a powerful Multi-modal Prompt-based HOI detector designed to leverage both textual descriptions for open-set generalization and visual exemplars for handling high ambiguity in descriptions. MP-HOI could serve as a generalist HOI detector, surpassing the HOI vocabulary of existing expert models by more than 30 times.
• Score: 26.355054079885463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we develop \textbf{MP-HOI}, a powerful Multi-modal Prompt-based HOI detector designed to leverage both textual descriptions for open-set generalization and visual exemplars for handling high ambiguity in descriptions, realizing HOI detection in the open world. Specifically, it integrates visual prompts into existing language-guided-only HOI detectors to handle situations where textual descriptions face difficulties in generalization and to address complex scenarios with high interaction ambiguity. To facilitate MP-HOI training, we build a large-scale HOI dataset named Magic-HOI, which gathers six existing datasets into a unified label space, forming over 186K images with 2.4K objects, 1.2K actions, and 20K HOI interactions. Furthermore, to tackle the long-tail issue within the Magic-HOI dataset, we introduce an automated pipeline for generating realistically annotated HOI images and present SynHOI, a high-quality synthetic HOI dataset containing 100K images. Leveraging these two datasets, MP-HOI optimizes the HOI task as a similarity learning process between multi-modal prompts and objects/interactions via a unified contrastive loss, to learn generalizable and transferable objects/interactions representations from large-scale data. MP-HOI could serve as a generalist HOI detector, surpassing the HOI vocabulary of existing expert models by more than 30 times. Concurrently, our results demonstrate that MP-HOI exhibits remarkable zero-shot capability in real-world scenarios and consistently achieves a new state-of-the-art performance across various benchmarks.

Related papers

• Spatio-Temporal Context Prompting for Zero-Shot Action Detection [13.22912547389941]
  We propose a method which can effectively leverage the rich knowledge of visual-language models to perform Person-Context Interaction. To address the challenge of recognizing distinct actions by multiple people at the same timestamp, we design the Interest Token Spotting mechanism. Our method achieves superior results compared to previous approaches and can be further extended to multi-action videos.
  arXiv  Detail & Related papers  (2024-08-28T17:59:05Z)
• Exploring Conditional Multi-Modal Prompts for Zero-shot HOI Detection [37.57355457749918]
  We introduce a novel framework for zero-shot HOI detection using Conditional Multi-Modal Prompts, namely CMMP. Unlike traditional prompt-learning methods, we propose learning decoupled vision and language prompts for interactiveness-aware visual feature extraction. Experiments demonstrate the efficacy of our detector with conditional multi-modal prompts, outperforming previous state-of-the-art on unseen classes of various zero-shot settings.
  arXiv  Detail & Related papers  (2024-08-05T14:05:25Z)
• HIMO: A New Benchmark for Full-Body Human Interacting with Multiple Objects [86.86284624825356]
  HIMO is a dataset of full-body human interacting with multiple objects. HIMO contains 3.3K 4D HOI sequences and 4.08M 3D HOI frames.
  arXiv  Detail & Related papers  (2024-07-17T07:47:34Z)
• Enhancing HOI Detection with Contextual Cues from Large Vision-Language Models [56.257840490146]
  ConCue is a novel approach for improving visual feature extraction in HOI detection. We develop a transformer-based feature extraction module with a multi-tower architecture that integrates contextual cues into both instance and interaction detectors.
  arXiv  Detail & Related papers  (2023-11-26T09:11:32Z)
• Detecting Any Human-Object Interaction Relationship: Universal HOI Detector with Spatial Prompt Learning on Foundation Models [55.20626448358655]
  This study explores the universal interaction recognition in an open-world setting through the use of Vision-Language (VL) foundation models and large language models (LLMs) Our design includes an HO Prompt-guided Decoder (HOPD), facilitates the association of high-level relation representations in the foundation model with various HO pairs within the image. For open-category interaction recognition, our method supports either of two input types: interaction phrase or interpretive sentence.
  arXiv  Detail & Related papers  (2023-11-07T08:27:32Z)
• Multi-Grained Multimodal Interaction Network for Entity Linking [65.30260033700338]
  Multimodal entity linking task aims at resolving ambiguous mentions to a multimodal knowledge graph. We propose a novel Multi-GraIned Multimodal InteraCtion Network $textbf(MIMIC)$ framework for solving the MEL task.
  arXiv  Detail & Related papers  (2023-07-19T02:11:19Z)
• Boosting Human-Object Interaction Detection with Text-to-Image Diffusion Model [22.31860516617302]
  We introduce DiffHOI, a novel HOI detection scheme grounded on a pre-trained text-image diffusion model. To fill in the gaps of HOI datasets, we propose SynHOI, a class-balance, large-scale, and high-diversity synthetic dataset. Experiments demonstrate that DiffHOI significantly outperforms the state-of-the-art in regular detection (i.e., 41.50 mAP) and zero-shot detection.
  arXiv  Detail & Related papers  (2023-05-20T17:59:23Z)
• MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text [58.655375327681774]
  We propose the first Multimodal Retrieval-Augmented Transformer (MuRAG) MuRAG accesses an external non-parametric multimodal memory to augment language generation. Our results show that MuRAG achieves state-of-the-art accuracy, outperforming existing models by 10-20% absolute on both datasets.
  arXiv  Detail & Related papers  (2022-10-06T13:58:03Z)
• MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesis [72.85526892440251]
  We introduce MetaGraspNet, a large-scale photo-realistic bin picking dataset constructed via physics-based metaverse synthesis. The proposed dataset contains 217k RGBD images across 82 different article types, with full annotations for object detection, amodal perception, keypoint detection, manipulation order and ambidextrous grasp labels for a parallel-jaw and vacuum gripper. We also provide a real dataset consisting of over 2.3k fully annotated high-quality RGBD images, divided into 5 levels of difficulties and an unseen object set to evaluate different object and layout properties.
  arXiv  Detail & Related papers  (2022-08-08T08:15:34Z)
• Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution [30.438041837029875]
  We propose a robust visual information extraction system (VIES) towards real-world scenarios. VIES is a unified end-to-end trainable framework for simultaneous text detection, recognition and information extraction. We construct a fully-annotated dataset called EPHOIE, which is the first Chinese benchmark for both text spotting and visual information extraction.
  arXiv  Detail & Related papers  (2021-01-24T11:05:24Z)

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.