Human object interaction (HOI) detection plays a crucial role in human-centric scene understanding and serves as a fundamental building-block for many vision tasks. One generalizable and scalable strategy for HOI detection is to use weak supervision, learning from image-level annotations only. This is inherently challenging due to ambiguous human-object associations, large search space of detecting HOIs and highly noisy training signal. A promising strategy to address those challenges is to exploit knowledge from large-scale pretrained models (e.g., CLIP), but a direct knowledge distillation strategy~\citep{liao2022gen} does not perform well on the weakly-supervised setting. In contrast, we develop a CLIP-guided HOI representation capable of incorporating the prior knowledge at both image level and HOI instance level, and adopt a self-taught mechanism to prune incorrect human-object associations. Experimental results on HICO-DET and V-COCO show that our method outperforms the previous works by a sizable margin, showing the efficacy of our HOI representation.
School of Electronic and Information Engineering, South China University of Technology（华南理工大学电子与信息学院）；Pazhou Lab（琶洲实验室）；JD Explore Academy at JD.com（京东探索研究院）
Human-Object Interaction (HOI) detection is important to human-centric scene understanding tasks. Existing works tend to assume that the same verb has similar visual characteristics in different HOI categories, an approach that ignores the diverse semantic meanings of the verb. To address this issue, in this paper, we propose a novel Polysemy Deciphering Network (PD-Net) that decodes the visual polysemy of verbs for HOI detection in three distinct ways. First, we refine features for HOI detection to be polysemyaware through the use of two novel modules: namely, Language Prior-guided Channel At…
CL-HOI: Cross-Level Human-Object Interaction Distillation from Vision Large Language Models
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Schaeffler Hub for Advanced Research at NTU（舍弗勒-南洋理工大学高级研究中心）
Recently, significant progress has been achieved in deep image matting. Most of the classical image matting methods are time-consuming and require an ideal trimap which is difficult to attain in practice. A high efficient image matting method based on a weakly annotated mask is in demand for mobile applications. In this paper, we propose a novel method based on Deep Learning and Guided Filter, called Inductive Guided Filter, which can tackle the real-time general image matting task on mobile devices. We design a lightweight hourglass network to parameterize the original Guided Filter method th…
Zhejiang University（浙江大学）；State Key Laboratory of Industrial Control Technology, Zhejiang University（浙江大学工业控制技术国家重点实验室）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）
Human-Object Interaction (HOI) detection is an essential task to understand human-centric images from a fine-grained perspective. Although end-to-end HOI detection models thrive, their paradigm of parallel human/object detection and verb class prediction loses two-stage methods' merit: object-guided hierarchy. The object in one HOI triplet gives direct clues to the verb to be predicted. In this paper, we aim to boost end-to-end models with object-guided statistical priors. Specifically, We propose to utilize a Verb Semantic Model (VSM) and use semantic aggregation to profit from this object-gu…
Temporal Activity Detection aims to predict activity classes per frame, in contrast to video-level predictions in Activity Classification (i.e., Activity Recognition). Due to the expensive frame-level annotations required for detection, the scale of detection datasets is limited. Thus, commonly, previous work on temporal activity detection resorts to fine-tuning a classification model pretrained on large-scale classification datasets (e.g., Kinetics-400). However, such pretrained models are not ideal for downstream detection, due to the disparity between the pretraining and the downstream fine…
Human Object Interaction (HOI) detection is a challenging task that requires to distinguish the interaction between a human-object pair. Attention based relation parsing is a popular and effective strategy utilized in HOI. However, current methods execute relation parsing in a "bottom-up" manner. 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. Therefore, we introduce a novel knowledge-guided top-down attention into HOI, and propose to model the relation parsing as a "look and search" process: execute …
Human-object interaction (HOI) detection aims to extract interacting human-object pairs and their interaction categories from a given natural image. Even though the labeling effort required for building HOI detection datasets is inherently more extensive than for many other computer vision tasks, weakly-supervised directions in this area have not been sufficiently explored due to the difficulty of learning human-object interactions with weak supervision, rooted in the combinatorial nature of interactions over the object and predicate space. In this paper, we tackle HOI detection with the weake…
Hoi2Threat: An Interpretable Threat Detection Method for Human Violence Scenarios Guided by Human-Object Interaction
organization= School of Mechatronical Engineering, Beijing Institute of Technology , postcode= 100081 , city= Beijing , country= China；organization= School of Computer Scienece \& Technology, Beijing Insitute of Technology , postcode= 100081 , city= Beijing , country= China；organization= School of Automation, Beijing Insitute of Technology , postcode= 100081 , city= Beijing , country= China；organization= School of Information
Efficient Adaptive Human-Object Interaction Detection with Concept-guided Memory
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；Adobe Research（Adobe研究院）；National Institute of Health Data Science, Peking University（北京大学健康医疗大数据国家研究院）
Univ Le Mans（勒芒大学）；CNRS（法国国家科学研究中心）；IRISA（IRISA研究所）；King’s College London（伦敦国王学院）；Univ Rennes（雷恩大学）；Inria（法国国家信息与自动化研究所）
Human object interaction (HOI) detection is an important task in image understanding and reasoning. It is in a form of HOI triplet <human; verb; object>, requiring bounding boxes for human and object, and action between them for the task completion. In other words, this task requires strong supervision for training that is however hard to procure. A natural solution to overcome this is to pursue weakly-supervised learning, where we only know the presence of certain HOI triplets in images but their exact location is unknown. Most weakly-supervised learning methods do not make provision for leve…
Pests and diseases are relevant factors for production losses in agriculture and, therefore, promote a huge investment in the prevention and detection of its causative agents. In many countries, Integrated Pest Management is the most widely used process to prevent and mitigate the damages caused by pests and diseases in citrus crops. However, its results are credited by humans who visually inspect the orchards in order to identify the disease symptoms, insects and mite pests. In this context, we design a weakly supervised learning process guided by saliency maps to automatically select regions…
The Chinese University of Hong Kong（香港中文大学）；Shanghai Jiao Tong University（上海交通大学）
Human-object interaction (HOI) detection requires a large amount of annotated data. Current algorithms suffer from insufficient training samples and category imbalance within datasets. To increase data efficiency, in this paper, we propose an efficient and effective data augmentation method called DecAug for HOI detection. Based on our proposed object state similarity metric, object patterns across different HOIs are shared to augment local object appearance features without changing their state. Further, we shift spatial correlation between humans and objects to other feasible configurations …
Open-Vocabulary HOI Detection with Interaction-aware Prompt and Concept Calibration
Wangxuan Institute of Computer Technology, Peking University（北京大学王璇计算机技术研究所）；National Institute of Health Data Science, Peking University（北京大学国家健康数据科学研究院）
SenseTime Group Ltd.（商汤集团有限公司）；Tsinghua University（清华大学）
Human-object interactions (HOI) recognition and pose estimation are two closely related tasks. Human pose is an essential cue for recognizing actions and localizing the interacted objects. Meanwhile, human action and their interacted objects' localizations provide guidance for pose estimation. In this paper, we propose a turbo learning framework to perform HOI recognition and pose estimation simultaneously. First, two modules are designed to enforce message passing between the tasks, i.e. pose aware HOI recognition module and HOI guided pose estimation module. Then, these two modules form a cl…
Seoul National University（首尔国立大学）；V.DO Inc（V.DO公司）
In this work, we introduce a novel weakly supervised object detection (WSOD) paradigm to detect objects belonging to rare classes that have not many examples using transferable knowledge from human-object interactions (HOI). While WSOD shows lower performance than full supervision, we mainly focus on HOI as the main context which can strongly supervise complex semantics in images. Therefore, we propose a novel module called RRPN (relational region proposal network) which outputs an object-localizing attention map only with human poses and action verbs. In the source domain, we fully train an o…
OOD-HOI: Text-Driven 3D Whole-Body Human-Object Interactions Generation Beyond Training Domains
University of Chinese Academy of Sciences（中国科学院大学）；Shandong University（山东大学）；University of Science and Technology Beijing（北京科技大学）；Centre for Artificial Intelligence and Robotics, HKISI, CAS（中国科学院香港智能研究所人工智能与机器人中心）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Given a natural language query, a phrase grounding system aims to localize mentioned objects in an image. In weakly supervised scenario, mapping between image regions (i.e., proposals) and language is not available in the training set. Previous methods address this deficiency by training a grounding system via learning to reconstruct language information contained in input queries from predicted proposals. However, the optimization is solely guided by the reconstruction loss from the language modality, and ignores rich visual information contained in proposals and useful cues from external kno…
The task of Human-Object Interaction~(HOI) detection could be divided into two core problems, i.e., human-object association and interaction understanding. In this paper, we reveal and address the disadvantages of the conventional query-driven HOI detectors from the two aspects. For the association, previous two-branch methods suffer from complex and costly post-matching, while single-branch methods ignore the features distinction in different tasks. We propose Guided-Embedding Network~(GEN) to attain a two-branch pipeline without post-matching. In GEN, we design an instance decoder to detect …
University of British Columbia（不列颠哥伦比亚大学）；Vector Institute for AI（Vector人工智能研究所）
Learning how to localize and separate individual object sounds in the audio channel of the video is a difficult task. Current state-of-the-art methods predict audio masks from artificially mixed spectrograms, known as Mix-and-Separate framework. We propose an audio-visual co-segmentation, where the network learns both what individual objects look and sound like, from videos labeled with only object labels. Unlike other recent visually-guided audio source separation frameworks, our architecture can be learned in an end-to-end manner and requires no additional supervision or bounding box proposa…
National University of Singapore（新加坡国立大学）；University of Minnesota（明尼苏达大学）
The recent advances in instance-level detection tasks lay strong foundation for genuine comprehension of the visual scenes. However, the ability to fully comprehend a social scene is still in its preliminary stage. In this work, we focus on detecting human-object interactions (HOIs) in social scene images, which is demanding in terms of research and increasingly useful for practical applications. To undertake social tasks interacting with objects, humans direct their attention and move their body based on their intention. Based on this observation, we provide a unique computational perspective…
EgoGrasp: World-Space Hand-Object Interaction Estimation from Egocentric Videos
Shanghai Jiao Tong University（上海交通大学）；The University of Hong Kong（香港大学）；University of Science and Technology of China（中国科学技术大学）；Imperial College London（伦敦帝国学院）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
Learning semantic segmentation models under image-level supervision is far more challenging than under fully supervised setting. Without knowing the exact pixel-label correspondence, most weakly-supervised methods rely on external models to infer pseudo pixel-level labels for training semantic segmentation models. In this paper, we aim to develop a single neural network without resorting to any external models. We propose a novel self-guided strategy to fully utilize features learned across multiple levels to progressively generate the dense pseudo labels. First, we use high-level features as …
Recent mainstream weakly supervised semantic segmentation (WSSS) approaches are mainly based on Class Activation Map (CAM) generated by a CNN (Convolutional Neural Network) based image classifier. In this paper, we propose a novel transformer-based framework, named Semantic Guided Activation Transformer (SemFormer), for WSSS. We design a transformer-based Class-Aware AutoEncoder (CAAE) to extract the class embeddings for the input image and learn class semantics for all classes of the dataset. The class embeddings and learned class semantics are then used to guide the generation of activation …
No More Sibling Rivalry: Debiasing Human-Object Interaction Detection
ShanghaiTech University（上海科技大学）；School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Biomedical Engineering, Tsinghua University（清华大学生物医学工程学院）
University of Maryland, College Park（马里兰大学帕克分校）；DeepMind（深度思维）；Adobe Research（奥多比研究院）
We introduce count-guided weakly supervised localization (C-WSL), an approach that uses per-class object count as a new form of supervision to improve weakly supervised localization (WSL). C-WSL uses a simple count-based region selection algorithm to select high-quality regions, each of which covers a single object instance during training, and improves existing WSL methods by training with the selected regions. To demonstrate the effectiveness of C-WSL, we integrate it into two WSL architectures and conduct extensive experiments on VOC2007 and VOC2012. Experimental results show that C-WSL lea…
Beijing University of Posts and Telecommunications（北京邮电大学）
Attention mechanisms is frequently used to learn the discriminative features for better feature representations. In this paper, we extend the attention mechanism to the task of weakly supervised object localization (WSOL) and propose the dual-attention guided dropblock module (DGDM), which aims at learning the informative and complementary visual patterns for WSOL. This module contains two key components, the channel attention guided dropout (CAGD) and the spatial attention guided dropblock (SAGD). To model channel interdependencies, the CAGD ranks the channel attentions and treats the top-k a…
