Towards Student Actions in Classroom Scenes: New Dataset and Baseline

• URL: http://arxiv.org/abs/2409.00926v2
• Date: Fri, 07 Mar 2025 07:00:24 GMT
• Title: Towards Student Actions in Classroom Scenes: New Dataset and Baseline
• Authors: Zhuolin Tan, Chenqiang Gao, Anyong Qin, Ruixin Chen, Tiecheng Song, Feng Yang, Deyu Meng,
• Abstract summary: Student Action Video dataset consists of 4,324 carefully trimmed video clips from 758 different classrooms, annotated with 15 distinct student actions.<n>Compared to existing action detection datasets, the SAV dataset stands out by providing a wide range of real classroom scenarios.<n>Our method demonstrates excellent performance with a mean Average Precision (mAP) of 67.9% and 27.4% on the SAV and AVA datasets, respectively.
• Score: 43.268586725768465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Analyzing student actions is an important and challenging task in educational research. Existing efforts have been hampered by the lack of accessible datasets to capture the nuanced action dynamics in classrooms. In this paper, we present a new multi-label Student Action Video (SAV) dataset, specifically designed for action detection in classroom settings. The SAV dataset consists of 4,324 carefully trimmed video clips from 758 different classrooms, annotated with 15 distinct student actions. Compared to existing action detection datasets, the SAV dataset stands out by providing a wide range of real classroom scenarios, high-quality video data, and unique challenges, including subtle movement differences, dense object engagement, significant scale differences, varied shooting angles, and visual occlusion. These complexities introduce new opportunities and challenges to advance action detection methods. To benchmark this, we propose a novel baseline method based on a visual transformer, designed to enhance attention to key local details within small and dense object regions. Our method demonstrates excellent performance with a mean Average Precision (mAP) of 67.9% and 27.4% on the SAV and AVA datasets, respectively. This paper not only provides the dataset but also calls for further research into AI-driven educational tools that may transform teaching methodologies and learning outcomes. The code and dataset are released at https://github.com/Ritatanz/SAV.

Related papers

• Oriented Tiny Object Detection: A Dataset, Benchmark, and Dynamic Unbiased Learning [51.170479006249195]
  We introduce a new dataset, benchmark, and a dynamic coarse-to-fine learning scheme in this study. Our proposed dataset, AI-TOD-R, features the smallest object sizes among all oriented object detection datasets. We present a benchmark spanning a broad range of detection paradigms, including both fully-supervised and label-efficient approaches.
  arXiv  Detail & Related papers  (2024-12-16T09:14:32Z)
• Dense Video Object Captioning from Disjoint Supervision [77.47084982558101]
  We propose a new task and model for dense video object captioning. This task unifies spatial and temporal localization in video. We show how our model improves upon a number of strong baselines for this new task.
  arXiv  Detail & Related papers  (2023-06-20T17:57:23Z)
• OCTScenes: A Versatile Real-World Dataset of Tabletop Scenes for Object-Centric Learning [41.09407455527254]
  We propose a versatile real-world dataset of tabletop scenes for object-centric learning called OCTScenes. OCTScenes contains 5000 tabletop scenes with a total of 15 objects. It is meticulously designed to serve as a benchmark for comparing, evaluating, and analyzing object-centric learning methods.
  arXiv  Detail & Related papers  (2023-06-16T08:26:57Z)
• Uncertainty Aware Active Learning for Reconfiguration of Pre-trained Deep Object-Detection Networks for New Target Domains [0.0]
  Object detection is one of the most important and fundamental aspects of computer vision tasks. To obtain training data for object detection model efficiently, many datasets opt to obtain their unannotated data in video format. Annotating every frame from a video is costly and inefficient since many frames contain very similar information for the model to learn from. In this paper, we proposed a novel active learning algorithm for object detection models to tackle this problem.
  arXiv  Detail & Related papers  (2023-03-22T17:14:10Z)
• Revisiting Deep Active Learning for Semantic Segmentation [37.3546941940388]
  We show that the data distribution is decisive for the performance of the various active learning objectives proposed in the literature. We demonstrate that the integration of semi-supervised learning with active learning can improve performance when the two objectives are aligned.
  arXiv  Detail & Related papers  (2023-02-08T14:23:37Z)
• Weakly Supervised Two-Stage Training Scheme for Deep Video Fight Detection Model [0.0]
  Fight detection in videos is an emerging deep learning application with today's prevalence of surveillance systems and streaming media. Previous work has largely relied on action recognition techniques to tackle this problem. We design the fight detection model as a composition of an action-aware feature extractor and an anomaly score generator.
  arXiv  Detail & Related papers  (2022-09-23T08:29:16Z)
• Mitigating Representation Bias in Action Recognition: Algorithms and Benchmarks [76.35271072704384]
  Deep learning models perform poorly when applied to videos with rare scenes or objects. We tackle this problem from two different angles: algorithm and dataset. We show that the debiased representation can generalize better when transferred to other datasets and tasks.
  arXiv  Detail & Related papers  (2022-09-20T00:30:35Z)
• NoisyActions2M: A Multimedia Dataset for Video Understanding from Noisy Labels [33.659146748289444]
  We create a benchmark dataset consisting of around 2 million videos with associated user-generated annotations and other meta information. We show how a network pretrained on the proposed dataset can help against video corruption and label noise in downstream datasets.
  arXiv  Detail & Related papers  (2021-10-13T16:12:18Z)
• Salient Objects in Clutter [130.63976772770368]
  This paper identifies and addresses a serious design bias of existing salient object detection (SOD) datasets. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. We propose a new high-quality dataset and update the previous saliency benchmark.
  arXiv  Detail & Related papers  (2021-05-07T03:49:26Z)
• Few-Shot Learning for Video Object Detection in a Transfer-Learning Scheme [70.45901040613015]
  We study the new problem of few-shot learning for video object detection. We employ a transfer-learning framework to effectively train the video object detector on a large number of base-class objects and a few video clips of novel-class objects.
  arXiv  Detail & Related papers  (2021-03-26T20:37:55Z)
• Learning Disentangled Representations of Video with Missing Data [17.34839550557689]
  We present Disentangled Imputed Video autoEncoder (DIVE), a deep generative model that imputes and predicts future video frames in the presence of missing data. Specifically, DIVE introduces a missingness latent variable, disentangles the hidden video representations into static and dynamic appearance, pose, and missingness factors for each object. On a moving MNIST dataset with various missing scenarios, DIVE outperforms the state of the art baselines by a substantial margin.
  arXiv  Detail & Related papers  (2020-06-23T23:54:49Z)
• Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation [57.68890534164427]
  In this work, we ask if we may leverage semi-supervised learning in unlabeled video sequences and extra images to improve the performance on urban scene segmentation. We simply predict pseudo-labels for the unlabeled data and train subsequent models with both human-annotated and pseudo-labeled data. Our Naive-Student model, trained with such simple yet effective iterative semi-supervised learning, attains state-of-the-art results at all three Cityscapes benchmarks.
  arXiv  Detail & Related papers  (2020-05-20T18:00:05Z)
• ZSTAD: Zero-Shot Temporal Activity Detection [107.63759089583382]
  We propose a novel task setting called zero-shot temporal activity detection (ZSTAD), where activities that have never been seen in training can still be detected. We design an end-to-end deep network based on R-C3D as the architecture for this solution. Experiments on both the THUMOS14 and the Charades datasets show promising performance in terms of detecting unseen activities.
  arXiv  Detail & Related papers  (2020-03-12T02:40:36Z)
• Stance Detection Benchmark: How Robust Is Your Stance Detection? [65.91772010586605]
  Stance Detection (StD) aims to detect an author's stance towards a certain topic or claim. We introduce a StD benchmark that learns from ten StD datasets of various domains in a multi-dataset learning setting. Within this benchmark setup, we are able to present new state-of-the-art results on five of the datasets.
  arXiv  Detail & Related papers  (2020-01-06T13:37:51Z)

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.