# Multi-Scale Deformable Transformers for Student Learning Behavior Detection in Smart Classroom

arXiv: https://arxiv.org/abs/2410.07834 · HTML: https://mlsanity.com/paper/2410.07834/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 93 references that are in the map.
- First posted on arXiv 10 Oct 2024.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Soft-NMS -- Improving Object Detection With One Line of Code](https://mlsanity.com/paper/1704.04503/) (2017)
- [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015)
- [Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation](https://mlsanity.com/paper/2206.02777/) (2022)
- [Coordinate Attention for Efficient Mobile Network Design](https://mlsanity.com/paper/2103.02907/) (2021)
- [Efficient Long-Range Attention Network for Image Super-resolution](https://mlsanity.com/paper/2203.06697/) (2022)
- [Large Selective Kernel Network for Remote Sensing Object Detection](https://mlsanity.com/paper/2303.09030/) (2023)
- [Battle of the Backbones: A Large-Scale Comparison of Pretrained Models across Computer Vision Tasks](https://mlsanity.com/paper/2310.19909/) (2023)
- [Abs-CAM: A Gradient Optimization Interpretable Approach for Explanation of Convolutional Neural Networks](https://mlsanity.com/paper/2207.03648/) (2022)
- [Knowledge Relation Rank Enhanced Heterogeneous Learning Interaction Modeling for Neural Graph Forgetting Knowledge Tracing](https://mlsanity.com/paper/2304.03945/) (2023)
- [Knowledge Graph Enhanced Intelligent Tutoring System Based on Exercise Representativeness and Informativeness](https://mlsanity.com/paper/2307.15076/) (2023)
- [Spatio-Temporal Representation Learning Enhanced Source Cell-phone Recognition from Speech Recordings](https://mlsanity.com/paper/2208.12753/) (2022)

## Abstract (arXiv)

The integration of Artificial Intelligence into the modern educational system is rapidly evolving, particularly in monitoring student behavior in classrooms, a task traditionally dependent on manual observation. This conventional method is notably inefficient, prompting a shift toward more advanced solutions like computer vision. However, existing target detection models face significant challenges such as occlusion, blurring, and scale disparity, which are exacerbated by the dynamic and complex nature of classroom settings. Furthermore, these models must adeptly handle multiple target detection. To overcome these obstacles, we introduce the Student Learning Behavior Detection with Multi-Scale Deformable Transformers (SCB-DETR), an innovative approach that utilizes large convolutional kernels for upstream feature extraction, and multi-scale feature fusion. This technique significantly improves the detection capabilities for multi-scale and occluded targets, offering a robust solution for analyzing student behavior. SCB-DETR establishes an end-to-end framework that simplifies the detection process and consistently outperforms other deep learning methods. Employing our custom Student Classroom Behavior (SCBehavior) Dataset, SCB-DETR achieves a mean Average Precision (mAP) of 0.626, which is a 1.5% improvement over the baseline model's mAP and a 6% increase in AP50. These results demonstrate SCB-DETR's superior performance in handling the uneven distribution of student behaviors and ensuring precise detection in dynamic classroom environments.