ProgRoCC: A Progressive Approach to Rough Crowd Counting
As the number of individuals in a crowd grows, enumeration-based techniques become increasingly infeasible and their estimates increasingly unreliable. We propose instead an estimation-based version of the problem: we label Rough Crowd Counting that delivers better accuracy on the basis of training data that is easier to acquire. Rough crowd counting requires only rough annotations of the number of targets in an image, instead of the more traditional, and far more expensive, per-target annotations. We propose an approach to the rough crowd counting problem based on CLIP, termed ProgRoCC. Specifically, we introduce a progressive estimation learning strategy that determines the object count through a coarse-to-fine approach. This approach delivers answers quickly, outperforms the state-of-the-art in semi- and weakly-supervised crowd counting. In addition, we design a vision-language matching adapter that optimizes key-value pairs by mining effective matches of two modalities to refine the visual features, thereby improving the final performance. Extensive experimental results on three widely adopted crowd counting datasets demonstrate the effectiveness of our method.
Code (0)
등록된 구현이 없습니다.
Tasks
Crowd CountingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method
Due to its variety of applications in the real-world, the task of single image-based crowd counting has received a lot of interest in the recent years. Recently, several approaches have been proposed to address various p…
Crowd CountingPushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method
In this work, we propose a novel crowd counting network that progressively generates crowd density maps via residual error estimation. The proposed method uses VGG16 as the backbone network and employs density map genera…
Crowd CountingHybrid attention network based on progressive embedding scale-context for crowd counting
The existing crowd counting methods usually adopted attention mechanism to tackle background noise, or applied multi-level features or multi-scales context fusion to tackle scale variation. However, these approaches deal…
Crowd CountingCrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model
Supervised crowd counting relies heavily on costly manual labeling, which is difficult and expensive, especially in dense scenes. To alleviate the problem, we propose a novel unsupervised framework for crowd counting, na…
Cross-Part Crowd CountingCrowd CountingLanguage ModelingLanguage Modelling+1Recurrent Distillation based Crowd Counting
In recent years, with the progress of deep learning technologies, crowd counting has been rapidly developed. In this work, we propose a simple yet effective crowd counting framework that is able to achieve the state-of-t…
Crowd Counting