SPAMming Labels: Efficient Annotations for the Trackers of Tomorrow

• URL: http://arxiv.org/abs/2404.11426v3
• Date: Tue, 01 Oct 2024 15:34:30 GMT
• Title: SPAMming Labels: Efficient Annotations for the Trackers of Tomorrow
• Authors: Orcun Cetintas, Tim Meinhardt, Guillem Brasó, Laura Leal-Taixé,
• Abstract summary: SPAM is a video label engine that provides high-quality labels with minimal human intervention. We use a unified graph formulation to address the annotation of both detections and identity association for tracks across time. We demonstrate that trackers trained on SPAM labels achieve comparable performance to those trained on human annotations.
• Score: 35.76243023101549
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Increasing the annotation efficiency of trajectory annotations from videos has the potential to enable the next generation of data-hungry tracking algorithms to thrive on large-scale datasets. Despite the importance of this task, there are currently very few works exploring how to efficiently label tracking datasets comprehensively. In this work, we introduce SPAM, a video label engine that provides high-quality labels with minimal human intervention. SPAM is built around two key insights: i) most tracking scenarios can be easily resolved. To take advantage of this, we utilize a pre-trained model to generate high-quality pseudo-labels, reserving human involvement for a smaller subset of more difficult instances; ii) handling the spatiotemporal dependencies of track annotations across time can be elegantly and efficiently formulated through graphs. Therefore, we use a unified graph formulation to address the annotation of both detections and identity association for tracks across time. Based on these insights, SPAM produces high-quality annotations with a fraction of ground truth labeling cost. We demonstrate that trackers trained on SPAM labels achieve comparable performance to those trained on human annotations while requiring only $3-20\%$ of the human labeling effort. Hence, SPAM paves the way towards highly efficient labeling of large-scale tracking datasets. We release all models and code.

Related papers

• Decoupled Spatio-Temporal Consistency Learning for Self-Supervised Tracking [12.910676293067231]
  We present a Self-Supervised Tracking framework named textbftracker designed to eliminate the need of box annotations.<n>We show that tracker surpasses textitOTA self-supervised tracking methods, achieving an improvement of more than 25.3%, 20.4%, and 14.8% in AUC (AO) score on the GOT10K, LaSOT, TrackingNet datasets, respectively.
  arXiv  Detail & Related papers  (2025-07-29T09:04:03Z)
• Pseudo-labelling meets Label Smoothing for Noisy Partial Label Learning [8.387189407144403]
  We motivate weakly supervised learning as an effective learning paradigm for problems where curating perfectly annotated datasets is expensive.<n>We focus on Partial Learning (PLL), a weakly-supervised learning paradigm where each training instance is paired with a set of candidate labels.<n>We present a framework that initially assigns pseudo-labels to images by exploiting the noisy partial labels through a weighted nearest neighbour algorithm.
  arXiv  Detail & Related papers  (2024-02-07T13:32:47Z)
• A Self Supervised StyleGAN for Image Annotation and Classification with Extremely Limited Labels [35.43549147657739]
  We propose SS-StyleGAN, a self-supervised approach for image annotation and classification suitable for extremely small annotated datasets. We show that the proposed method attains strong classification results using small labeled datasets of sizes 50 and even 10.
  arXiv  Detail & Related papers  (2023-12-26T09:46:50Z)
• A Light-weight, Effective and Efficient Model for Label Aggregation in Crowdsourcing [26.699587663952975]
  Label aggregation (LA) has emerged as a standard procedure to post-process crowdsourced labels. In this paper, we treat LA as a dynamic system and model it as a Dynamic Bayesian network. We derive two light-weight algorithms, LAtextsuperscriptonepass and LAtextsuperscripttwopass, which can effectively and efficiently estimate worker qualities and true labels.
  arXiv  Detail & Related papers  (2022-11-19T11:13:03Z)
• LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds [62.49198183539889]
  We propose a label-efficient semantic segmentation pipeline for outdoor scenes with LiDAR point clouds. Our method co-designs an efficient labeling process with semi/weakly supervised learning. Our proposed method is even highly competitive compared to the fully supervised counterpart with 100% labels.
  arXiv  Detail & Related papers  (2022-10-14T19:13:36Z)
• Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets [90.61266099147053]
  We investigate efficient annotation strategies for collecting multi-class classification labels for a large collection of images. We propose modifications and best practices aimed at minimizing human labeling effort. Simulated experiments on a 125k image subset of the ImageNet100 show that it can be annotated to 80% top-1 accuracy with 0.35 annotations per image on average.
  arXiv  Detail & Related papers  (2021-04-26T16:29:32Z)
• Adaptive Self-training for Few-shot Neural Sequence Labeling [55.43109437200101]
  We develop techniques to address the label scarcity challenge for neural sequence labeling models. Self-training serves as an effective mechanism to learn from large amounts of unlabeled data. meta-learning helps in adaptive sample re-weighting to mitigate error propagation from noisy pseudo-labels.
  arXiv  Detail & Related papers  (2020-10-07T22:29:05Z)
• Temporal Calibrated Regularization for Robust Noisy Label Learning [60.90967240168525]
  Deep neural networks (DNNs) exhibit great success on many tasks with the help of large-scale well annotated datasets. However, labeling large-scale data can be very costly and error-prone so that it is difficult to guarantee the annotation quality. We propose a Temporal Calibrated Regularization (TCR) in which we utilize the original labels and the predictions in the previous epoch together.
  arXiv  Detail & Related papers  (2020-07-01T04:48:49Z)
• Labelling unlabelled videos from scratch with multi-modal self-supervision [82.60652426371936]
  unsupervised labelling of a video dataset does not come for free from strong feature encoders. We propose a novel clustering method that allows pseudo-labelling of a video dataset without any human annotations. An extensive analysis shows that the resulting clusters have high semantic overlap to ground truth human labels.
  arXiv  Detail & Related papers  (2020-06-24T12:28:17Z)

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.