Single Domain Generalization for Crowd Counting

• URL: http://arxiv.org/abs/2403.09124v2
• Date: Fri, 5 Apr 2024 12:56:31 GMT
• Title: Single Domain Generalization for Crowd Counting
• Authors: Zhuoxuan Peng, S. -H. Gary Chan,
• Abstract summary: MPCount is a novel effective approach even for narrow source distribution. It stores diverse density values for density map regression and reconstructs domain-invariant features by means of only one memory bank. It is shown to significantly improve counting accuracy compared to the state of the art under diverse scenarios.
• Score: 11.212941297348268
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to its promising results, density map regression has been widely employed for image-based crowd counting. The approach, however, often suffers from severe performance degradation when tested on data from unseen scenarios, the so-called "domain shift" problem. To address the problem, we investigate in this work single domain generalization (SDG) for crowd counting. The existing SDG approaches are mainly for image classification and segmentation, and can hardly be extended to our case due to its regression nature and label ambiguity (i.e., ambiguous pixel-level ground truths). We propose MPCount, a novel effective SDG approach even for narrow source distribution. MPCount stores diverse density values for density map regression and reconstructs domain-invariant features by means of only one memory bank, a content error mask and attention consistency loss. By partitioning the image into grids, it employs patch-wise classification as an auxiliary task to mitigate label ambiguity. Through extensive experiments on different datasets, MPCount is shown to significantly improve counting accuracy compared to the state of the art under diverse scenarios unobserved in the training data characterized by narrow source distribution. Code is available at https://github.com/Shimmer93/MPCount.

Related papers

• Learning to Rank Patches for Unbiased Image Redundancy Reduction [80.93989115541966]
  Images suffer from heavy spatial redundancy because pixels in neighboring regions are spatially correlated. Existing approaches strive to overcome this limitation by reducing less meaningful image regions. We propose a self-supervised framework for image redundancy reduction called Learning to Rank Patches.
  arXiv  Detail & Related papers  (2024-03-31T13:12:41Z)
• Multimodal Unsupervised Domain Generalization by Retrieving Across the Modality Gap [11.96884248631201]
  We tackle the multimodal version of the unsupervised domain generalization problem. Our framework relies on the premise that the source dataset can be accurately and efficiently searched in a joint vision-language space. We show theoretically that cross-modal approximate nearest neighbor search suffers from low recall due to the large distance between text queries and the image centroids used for coarse quantization.
  arXiv  Detail & Related papers  (2024-02-06T21:29:37Z)
• Fine-grained Recognition with Learnable Semantic Data Augmentation [68.48892326854494]
  Fine-grained image recognition is a longstanding computer vision challenge. We propose diversifying the training data at the feature-level to alleviate the discriminative region loss problem. Our method significantly improves the generalization performance on several popular classification networks.
  arXiv  Detail & Related papers  (2023-09-01T11:15:50Z)
• High-fidelity Pseudo-labels for Boosting Weakly-Supervised Segmentation [17.804090651425955]
  Image-level weakly-supervised segmentation (WSSS) reduces the usually vast data annotation cost by surrogate segmentation masks during training. Our work is based on two techniques for improving CAMs; importance sampling, which is a substitute for GAP, and the feature similarity loss. We reformulate both techniques based on binomial posteriors of multiple independent binary problems. This has two benefits; their performance is improved and they become more general, resulting in an add-on method that can boost virtually any WSSS method.
  arXiv  Detail & Related papers  (2023-04-05T17:43:57Z)
• Semantic-Aware Domain Generalized Segmentation [67.49163582961877]
  Deep models trained on source domain lack generalization when evaluated on unseen target domains with different data distributions. We propose a framework including two novel modules: Semantic-Aware Normalization (SAN) and Semantic-Aware Whitening (SAW) Our approach shows significant improvements over existing state-of-the-art on various backbone networks.
  arXiv  Detail & Related papers  (2022-04-02T09:09:59Z)
• Semantic Distribution-aware Contrastive Adaptation for Semantic Segmentation [50.621269117524925]
  Domain adaptive semantic segmentation refers to making predictions on a certain target domain with only annotations of a specific source domain. We present a semantic distribution-aware contrastive adaptation algorithm that enables pixel-wise representation alignment. We evaluate SDCA on multiple benchmarks, achieving considerable improvements over existing algorithms.
  arXiv  Detail & Related papers  (2021-05-11T13:21:25Z)
• Distribution Matching for Crowd Counting [51.90971145453012]
  We show that imposing Gaussians to annotations hurts generalization performance. We propose to use Distribution Matching for crowd COUNTing (DM-Count) In terms of Mean Absolute Error, DM-Count outperforms the previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-09-28T04:57:23Z)
• Image Fine-grained Inpainting [89.17316318927621]
  We present a one-stage model that utilizes dense combinations of dilated convolutions to obtain larger and more effective receptive fields. To better train this efficient generator, except for frequently-used VGG feature matching loss, we design a novel self-guided regression loss. We also employ a discriminator with local and global branches to ensure local-global contents consistency.
  arXiv  Detail & Related papers  (2020-02-07T03:45:25Z)

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.