103 research outputs found
Task Difficulty Aware Parameter Allocation & Regularization for Lifelong Learning
Parameter regularization or allocation methods are effective in overcoming
catastrophic forgetting in lifelong learning. However, they solve all tasks in
a sequence uniformly and ignore the differences in the learning difficulty of
different tasks. So parameter regularization methods face significant
forgetting when learning a new task very different from learned tasks, and
parameter allocation methods face unnecessary parameter overhead when learning
simple tasks. In this paper, we propose the Parameter Allocation &
Regularization (PAR), which adaptively select an appropriate strategy for each
task from parameter allocation and regularization based on its learning
difficulty. A task is easy for a model that has learned tasks related to it and
vice versa. We propose a divergence estimation method based on the
Nearest-Prototype distance to measure the task relatedness using only features
of the new task. Moreover, we propose a time-efficient relatedness-aware
sampling-based architecture search strategy to reduce the parameter overhead
for allocation. Experimental results on multiple benchmarks demonstrate that,
compared with SOTAs, our method is scalable and significantly reduces the
model's redundancy while improving the model's performance. Further qualitative
analysis indicates that PAR obtains reasonable task-relatedness.Comment: Accepted by CVPR2023. Code is available at
https://github.com/WenjinW/PA
Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering
The pre-training-fine-tuning paradigm based on layout-aware multimodal
pre-trained models has achieved significant progress on document image question
answering. However, domain pre-training and task fine-tuning for additional
visual, layout, and task modules prevent them from directly utilizing
off-the-shelf instruction-tuning language foundation models, which have
recently shown promising potential in zero-shot learning. Contrary to aligning
language models to the domain of document image question answering, we align
document image question answering to off-the-shell instruction-tuning language
foundation models to utilize their zero-shot capability. Specifically, we
propose layout and task aware instruction prompt called LATIN-Prompt, which
consists of layout-aware document content and task-aware descriptions. The
former recovers the layout information among text segments from OCR tools by
appropriate spaces and line breaks. The latter ensures that the model generates
answers that meet the requirements, especially format requirements, through a
detailed description of task. Experimental results on three benchmarks show
that LATIN-Prompt can improve the zero-shot performance of instruction-tuning
language foundation models on document image question answering and help them
achieve comparable levels to SOTAs based on the pre-training-fine-tuning
paradigm. Quantitative analysis and qualitative analysis demonstrate the
effectiveness of LATIN-Prompt. We provide the code in supplementary and will
release the code to facilitate future research.Comment: Code is available at https://github.com/WenjinW/LATIN-Promp
Worker Activity Recognition in Smart Manufacturing Using IMU and sEMG Signals with Convolutional Neural Networks
In a smart manufacturing system involving workers, recognition of the worker\u27s activity can be used for quantification and evaluation of the worker\u27s performance, as well as to provide onsite instructions with augmented reality. In this paper, we propose a method for activity recognition using Inertial Measurement Unit (IMU) and surface electromyography (sEMG) signals obtained from a Myo armband. The raw 10-channel IMU signals are stacked to form a signal image. This image is transformed into an activity image by applying Discrete Fourier Transformation (DFT) and then fed into a Convolutional Neural Network (CNN) for feature extraction, resulting in a high-level feature vector. Another feature vector representing the level of muscle activation is evaluated with the raw 8-channel sEMG signals. Then these two vectors are concatenated and used for work activity classification. A worker activity dataset is established, which at present contains 6 common activities in assembly tasks, i.e., grab tool/part, hammer nail, use power-screwdriver, rest arm, turn screwdriver, and use wrench. The developed CNN model is evaluated on this dataset and achieves 98% and 87% recognition accuracy in the half-half and leave-one-out experiments, respectively
Real-Time Assembly Operation Recognition with Fog Computing and Transfer Learning for Human-Centered Intelligent Manufacturing
In a human-centered intelligent manufacturing system, every element is to assist the operator in achieving the optimal operational performance. The primary task of developing such a human-centered system is to accurately understand human behavior. In this paper, we propose a fog computing framework for assembly operation recognition, which brings computing power close to the data source in order to achieve real-time recognition. For data collection, the operator\u27s activity is captured using visual cameras from different perspectives. For operation recognition, instead of directly building and training a deep learning model from scratch, which needs a huge amount of data, transfer learning is applied to transfer the learning abilities to our application. A worker assembly operation dataset is established, which at present contains 10 sequential operations in an assembly task of installing a desktop CNC machine. The developed transfer learning model is evaluated on this dataset and achieves a recognition accuracy of 95% in the testing experiments
Action Recognition in Manufacturing Assembly using Multimodal Sensor Fusion
Production innovations are occurring faster than ever. Manufacturing workers thus need to frequently learn new methods and skills. In fast changing, largely uncertain production systems, manufacturers with the ability to comprehend workers\u27 behavior and assess their operation performance in near real-time will achieve better performance than peers. Action recognition can serve this purpose. Despite that human action recognition has been an active field of study in machine learning, limited work has been done for recognizing worker actions in performing manufacturing tasks that involve complex, intricate operations. Using data captured by one sensor or a single type of sensor to recognize those actions lacks reliability. The limitation can be surpassed by sensor fusion at data, feature, and decision levels. This paper presents a study that developed a multimodal sensor system and used sensor fusion methods to enhance the reliability of action recognition. One step in assembling a Bukito 3D printer, which composed of a sequence of 7 actions, was used to illustrate and assess the proposed method. Two wearable sensors namely Myo-armband captured both Inertial Measurement Unit (IMU) and electromyography (EMG) signals of assembly workers. Microsoft Kinect, a vision based sensor, simultaneously tracked predefined skeleton joints of them. The collected IMU, EMG, and skeleton data were respectively used to train five individual Convolutional Neural Network (CNN) models. Then, various fusion methods were implemented to integrate the prediction results of independent models to yield the final prediction. Reasons for achieving better performance using sensor fusion were identified from this study
ERNIE-mmLayout: Multi-grained MultiModal Transformer for Document Understanding
Recent efforts of multimodal Transformers have improved Visually Rich
Document Understanding (VrDU) tasks via incorporating visual and textual
information. However, existing approaches mainly focus on fine-grained elements
such as words and document image patches, making it hard for them to learn from
coarse-grained elements, including natural lexical units like phrases and
salient visual regions like prominent image regions. In this paper, we attach
more importance to coarse-grained elements containing high-density information
and consistent semantics, which are valuable for document understanding. At
first, a document graph is proposed to model complex relationships among
multi-grained multimodal elements, in which salient visual regions are detected
by a cluster-based method. Then, a multi-grained multimodal Transformer called
mmLayout is proposed to incorporate coarse-grained information into existing
pre-trained fine-grained multimodal Transformers based on the graph. In
mmLayout, coarse-grained information is aggregated from fine-grained, and then,
after further processing, is fused back into fine-grained for final prediction.
Furthermore, common sense enhancement is introduced to exploit the semantic
information of natural lexical units. Experimental results on four tasks,
including information extraction and document question answering, show that our
method can improve the performance of multimodal Transformers based on
fine-grained elements and achieve better performance with fewer parameters.
Qualitative analyses show that our method can capture consistent semantics in
coarse-grained elements.Comment: Accepted by ACM Multimedia 202
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