Exemplar Free Class Agnostic Counting

• URL: http://arxiv.org/abs/2205.14212v1
• Date: Fri, 27 May 2022 19:44:39 GMT
• Title: Exemplar Free Class Agnostic Counting
• Authors: Viresh Ranjan and Minh Hoai
• Abstract summary: Class agnostic counting aims to count objects in a novel object category at test time without access to labeled training data for that category. Our proposed approach first identifies exemplars from repeating objects in an image, and then counts the repeating objects. We evaluate our proposed approach on FSC-147 dataset, and show that it achieves superior performance compared to the existing approaches.
• Score: 28.41525571128706
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We tackle the task of Class Agnostic Counting, which aims to count objects in a novel object category at test time without any access to labeled training data for that category. All previous class agnostic counting methods cannot work in a fully automated setting, and require computationally expensive test time adaptation. To address these challenges, we propose a visual counter which operates in a fully automated setting and does not require any test time adaptation. Our proposed approach first identifies exemplars from repeating objects in an image, and then counts the repeating objects. We propose a novel region proposal network for identifying the exemplars. After identifying the exemplars, we obtain the corresponding count by using a density estimation based Visual Counter. We evaluate our proposed approach on FSC-147 dataset, and show that it achieves superior performance compared to the existing approaches.

Related papers

• Mind the Prompt: A Novel Benchmark for Prompt-based Class-Agnostic Counting [8.000723123087473]
  Class-agnostic counting (CAC) is a recent task in computer vision that aims to estimate the number of instances of arbitrary object classes never seen during model training. We introduce the Prompt-Aware Counting benchmark, which comprises two targeted tests, each accompanied by appropriate evaluation metrics.
  arXiv  Detail & Related papers  (2024-09-24T10:35:42Z)
• A Fixed-Point Approach to Unified Prompt-Based Counting [51.20608895374113]
  This paper aims to establish a comprehensive prompt-based counting framework capable of generating density maps for objects indicated by various prompt types, such as box, point, and text. Our model excels in prominent class-agnostic datasets and exhibits superior performance in cross-dataset adaptation tasks.
  arXiv  Detail & Related papers  (2024-03-15T12:05:44Z)
• Zero-Shot Object Counting with Language-Vision Models [50.1159882903028]
  Class-agnostic object counting aims to count object instances of an arbitrary class at test time. Current methods require human-annotated exemplars as inputs which are often unavailable for novel categories. We propose zero-shot object counting (ZSC), a new setting where only the class name is available during test time.
  arXiv  Detail & Related papers  (2023-09-22T14:48:42Z)
• Label, Verify, Correct: A Simple Few Shot Object Detection Method [93.84801062680786]
  We introduce a simple pseudo-labelling method to source high-quality pseudo-annotations from a training set. We present two novel methods to improve the precision of the pseudo-labelling process. Our method achieves state-of-the-art or second-best performance compared to existing approaches.
  arXiv  Detail & Related papers  (2021-12-10T18:59:06Z)
• Single Image Object Counting and Localizing using Active-Learning [4.56877715768796]
  We present a new method for counting and localizing repeating objects in single-image scenarios. Our method trains a CNN over a small set of labels carefully collected from the input image in few active-learning iterations. Compared with existing user-assisted counting methods, our active-learning iterations achieve state-of-the-art performance in terms of counting and localizing accuracy, number of user mouse clicks, and running-time.
  arXiv  Detail & Related papers  (2021-11-16T11:29:21Z)
• A Few-Shot Sequential Approach for Object Counting [63.82757025821265]
  We introduce a class attention mechanism that sequentially attends to objects in the image and extracts their relevant features. The proposed technique is trained on point-level annotations and uses a novel loss function that disentangles class-dependent and class-agnostic aspects of the model. We present our results on a variety of object-counting/detection datasets, including FSOD and MS COCO.
  arXiv  Detail & Related papers  (2020-07-03T18:23:39Z)
• Rethinking Object Detection in Retail Stores [55.359582952686175]
  We propose a new task, simultaneously object localization and counting, abbreviated as Locount. Locount requires algorithms to localize groups of objects of interest with the number of instances. We collect a large-scale object localization and counting dataset with rich annotations in retail stores.
  arXiv  Detail & Related papers  (2020-03-18T14:01:54Z)
• Incremental Few-Shot Object Detection [96.02543873402813]
  OpeN-ended Centre nEt is a detector for incrementally learning to detect class objects with few examples. ONCE fully respects the incremental learning paradigm, with novel class registration requiring only a single forward pass of few-shot training samples.
  arXiv  Detail & Related papers  (2020-03-10T12:56:59Z)

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.