TGA: Two-level Group Attention for Assembly State Detection

2020-10-12 · Hangfan Liu, Yongzhi Su, Jason Rambach, Alain Pagani

Assembly state detection, i.e., object state detection, has a critical meaning in computer vision tasks, especially in AR assisted assembly. Unlike other object detection problems, the visual difference between different object states can be subtle. For the better learning of such subtle appearance difference, we proposed a two-level group attention module (TGA), which consists of inter-group attention and intro-group attention. The relationship between feature groups as well as the representation within each feature group is simultaneously enhanced. We embedded the proposed TGA module in a popular object detector and evaluated it on two new datasets related to object state estimation. The result shows that our proposed attention module outperforms the baseline attention module.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Objectobject-detection Object DetectionState EstimationVocal Bursts Valence Prediction

Methods 이 논문이 사용한 방법론

ReLU How Do I Communicate to Expedia? How Do I Communicate to Expedia? – Call ☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056 for Live Support & Special Travel…

Batch Normalization 설명 없음

Residual Connection 설명 없음

Average Pooling 설명 없음

Dilated Convolution 설명 없음

TridentNet Block A TridentNet Block is a feature extractor used in object detection models. Instead of feeding in multi-scale inputs like the image pyramid, in a…

Max Pooling Max Pooling is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually…

Global Average Pooling Global Average Pooling is a pooling operation designed to replace fully connected layers in classical CNNs. The idea is to generate one feature map for each corresponding…

Find the Assembly Mistakes: Error Segmentation for Industrial Applications

2024-08-23 · Dan Lehman, Tim J. Schoonbeek, Shao-Hsuan Hung, Jacek Kustra 외

Recognizing errors in assembly and maintenance procedures is valuable for industrial applications, since it can increase worker efficiency and prevent unplanned down-time. Although assembly state recognition is gaining a…

Change Detection

Bimanual XR Specification of Relative and Absolute Assembly Hierarchies for Teleoperation

2026-03-02 · Benjamin Yang, Xichen He, Charlie Zou, Jen-Shuo Liu 외 arxiv

We present a bimanual XR interaction approach for specifying remote assembly tasks as hierarchies of relative and absolute object constraints that specify high-level teleoperation goals for robots. Grabbing one object in…

Segregated Temporal Assembly Recurrent Networks for Weakly Supervised Multiple Action Detection

2018-11-19 · Yunlu Xu, Chengwei Zhang, Zhanzhan Cheng, Jianwen Xie 외

This paper proposes a segregated temporal assembly recurrent (STAR) network for weakly-supervised multiple action detection. The model learns from untrimmed videos with only supervision of video-level labels and makes pr…

Action Detection Multiple Action Detection

Nova: Generative Language Models for Assembly Code with Hierarchical Attention and Contrastive Learning

2023-11-22 · Nan Jiang, Chengxiao Wang, Kevin Liu, Xiangzhe Xu 외

Binary code analysis is the foundation of crucial tasks in the security domain; thus building effective binary analysis techniques is more important than ever. Large language models (LLMs) although have brought impressiv…

Code Generation Code Translation Compiler Optimization Contrastive Learning+2

ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation

2024-03-25 · Hannah Schieber, Shiyu Li, Niklas Corell, Philipp Beckerle 외

In medical and industrial domains, providing guidance for assembly processes can be critical to ensure efficiency and safety. Errors in assembly can lead to significant consequences such as extended surgery times and pro…

6D Pose Estimation Object object-detection Object Detection+1