Counting in the 2020s: Binned Representations and Inclusive Performance Measures for Deep Crowd Counting Approaches

• URL: http://arxiv.org/abs/2204.04653v1
• Date: Sun, 10 Apr 2022 11:02:13 GMT
• Title: Counting in the 2020s: Binned Representations and Inclusive Performance Measures for Deep Crowd Counting Approaches
• Authors: Sravya Vardhani Shivapuja, Ashwin Gopinath, Ayush Gupta, Ganesh Ramakrishnan, Ravi Kiran Sarvadevabhatla
• Abstract summary: We modify the training pipeline to accommodate the knowledge of dataset skew. We propose a novel smoothed Bayesian binning approach to enable principled and balanced minibatch sampling. We introduce additional performance measures which are more inclusive and throw light on various comparative performance aspects of the deep networks.
• Score: 21.595568866609067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The data distribution in popular crowd counting datasets is typically heavy tailed and discontinuous. This skew affects all stages within the pipelines of deep crowd counting approaches. Specifically, the approaches exhibit unacceptably large standard deviation wrt statistical measures (MSE, MAE). To address such concerns in a holistic manner, we make two fundamental contributions. Firstly, we modify the training pipeline to accommodate the knowledge of dataset skew. To enable principled and balanced minibatch sampling, we propose a novel smoothed Bayesian binning approach. More specifically, we propose a novel cost function which can be readily incorporated into existing crowd counting deep networks to encourage bin-aware optimization. As the second contribution, we introduce additional performance measures which are more inclusive and throw light on various comparative performance aspects of the deep networks. We also show that our binning-based modifications retain their superiority wrt the newly proposed performance measures. Overall, our contributions enable a practically useful and detail-oriented characterization of performance for crowd counting approaches.

Related papers

• Building Trust in Black-box Optimization: A Comprehensive Framework for Explainability [1.3812010983144802]
  Surrogate Optimization (SO) is a common resolution, yet its proprietary nature leads to a lack of explainability and transparency. We propose emphInclusive Explainability Metrics for Surrogate Optimization (IEMSO) These metrics enhance the transparency, trustworthiness, and explainability of the SO approaches.
  arXiv  Detail & Related papers  (2024-10-18T16:20:17Z)
• Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation [69.60321475454843]
  We propose DPCPL, the first pre-training and prompt-tuning paradigm tailored for Multi-Behavior Sequential Recommendation. In the pre-training stage, we propose a novel Efficient Behavior Miner (EBM) to filter out the noise at multiple time scales. Subsequently, we propose to tune the pre-trained model in a highly efficient manner with the proposed Customized Prompt Learning (CPL) module.
  arXiv  Detail & Related papers  (2024-08-21T06:48:38Z)
• Probabilistic Contrastive Learning for Long-Tailed Visual Recognition [78.70453964041718]
  Longtailed distributions frequently emerge in real-world data, where a large number of minority categories contain a limited number of samples. Recent investigations have revealed that supervised contrastive learning exhibits promising potential in alleviating the data imbalance. We propose a novel probabilistic contrastive (ProCo) learning algorithm that estimates the data distribution of the samples from each class in the feature space.
  arXiv  Detail & Related papers  (2024-03-11T13:44:49Z)
• Robust Zero-Shot Crowd Counting and Localization With Adaptive Resolution SAM [55.93697196726016]
  We propose a simple yet effective crowd counting method by utilizing the Segment-Everything-Everywhere Model (SEEM) We show that SEEM's performance in dense crowd scenes is limited, primarily due to the omission of many persons in high-density areas. Our proposed method achieves the best unsupervised performance in crowd counting, while also being comparable to some supervised methods.
  arXiv  Detail & Related papers  (2024-02-27T13:55:17Z)
• Adaptive End-to-End Metric Learning for Zero-Shot Cross-Domain Slot Filling [2.6056468338837457]
  Slot filling poses a critical challenge to handle a novel domain whose samples are never seen during training. Most prior works deal with this problem in a two-pass pipeline manner based on metric learning. We propose a new adaptive end-to-end metric learning scheme for the challenging zero-shot slot filling.
  arXiv  Detail & Related papers  (2023-10-23T19:01:16Z)
• Adaptive Cross Batch Normalization for Metric Learning [75.91093210956116]
  Metric learning is a fundamental problem in computer vision. We show that it is equally important to ensure that the accumulated embeddings are up to date. In particular, it is necessary to circumvent the representational drift between the accumulated embeddings and the feature embeddings at the current training iteration.
  arXiv  Detail & Related papers  (2023-03-30T03:22:52Z)
• PANet: Perspective-Aware Network with Dynamic Receptive Fields and Self-Distilling Supervision for Crowd Counting [63.84828478688975]
  We propose a novel perspective-aware approach called PANet to address the perspective problem. Based on the observation that the size of the objects varies greatly in one image due to the perspective effect, we propose the dynamic receptive fields (DRF) framework. The framework is able to adjust the receptive field by the dilated convolution parameters according to the input image, which helps the model to extract more discriminative features for each local region.
  arXiv  Detail & Related papers  (2021-10-31T04:43:05Z)
• Wisdom of (Binned) Crowds: A Bayesian Stratification Paradigm for Crowd Counting [16.09823718637455]
  We analyze the performance of crowd counting approaches across standard datasets at per strata level and in aggregate. Our contributions represent a nuanced, statistically balanced and fine-grained characterization of performance for crowd counting approaches.
  arXiv  Detail & Related papers  (2021-08-19T16:50:31Z)
• Multi-Resolution Fusion and Multi-scale Input Priors Based Crowd Counting [20.467558675556173]
  The paper proposes a new multi-resolution fusion based end-to-end crowd counting network. Three input priors are introduced to serve as an efficient and effective alternative to the PRM module. The proposed approach also has better generalization capability with the best results during the cross-dataset experiments.
  arXiv  Detail & Related papers  (2020-10-04T19:30:13Z)
• Learning to Count in the Crowd from Limited Labeled Data [109.2954525909007]
  We focus on reducing the annotation efforts by learning to count in the crowd from limited number of labeled samples. Specifically, we propose a Gaussian Process-based iterative learning mechanism that involves estimation of pseudo-ground truth for the unlabeled data.
  arXiv  Detail & Related papers  (2020-07-07T04:17:01Z)

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.