OCCAM: Class-Agnostic, Training-Free, Prior-Free and Multi-Class Object Counting

• URL: http://arxiv.org/abs/2601.13871v1
• Date: Tue, 20 Jan 2026 11:36:38 GMT
• Title: OCCAM: Class-Agnostic, Training-Free, Prior-Free and Multi-Class Object Counting
• Authors: Michail Spanakis, Iason Oikonomidis, Antonis Argyros,
• Abstract summary: Class-Agnostic object Counting (CAC) involves counting instances of objects from arbitrary classes within an image.<n>We present OCCAM, the first training-free approach to CAC that operates without the need of any supplementary information.
• Score: 1.2196508752999795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Class-Agnostic object Counting (CAC) involves counting instances of objects from arbitrary classes within an image. Due to its practical importance, CAC has received increasing attention in recent years. Most existing methods assume a single object class per image, rely on extensive training of large deep learning models and address the problem by incorporating additional information, such as visual exemplars or text prompts. In this paper, we present OCCAM, the first training-free approach to CAC that operates without the need of any supplementary information. Moreover, our approach addresses the multi-class variant of the problem, as it is capable of counting the object instances in each and every class among arbitrary object classes within an image. We leverage Segment Anything Model 2 (SAM2), a foundation model, and a custom threshold-based variant of the First Integer Neighbor Clustering Hierarchy (FINCH) algorithm to achieve competitive performance on widely used benchmark datasets, FSC-147 and CARPK. We propose a synthetic multi-class dataset and F1 score as a more suitable evaluation metric. The code for our method and the proposed synthetic dataset will be made publicly available at https://mikespanak.github.io/OCCAM_counter.

Related papers

• Bootstrapping MLLM for Weakly-Supervised Class-Agnostic Object Counting [59.37613121962146]
  We propose WS-COC, the first MLLM-driven weakly-supervised framework for class-agnostic object counting.<n> WS-COC matches or even surpasses many state-of-art fully-supervised methods while significantly reducing annotation costs.
  arXiv  Detail & Related papers  (2026-02-13T09:58:35Z)
• Improving Multi-label Recognition using Class Co-Occurrence Probabilities [7.062238472483738]
  Multi-label Recognition (MLR) involves the identification of multiple objects within an image. Recent works have leveraged information from vision-language models (VLMs) trained on large text-images datasets for the task. We propose a framework to extend the independent classifiers by incorporating the co-occurrence information for object pairs.
  arXiv  Detail & Related papers  (2024-04-24T20:33:25Z)
• Point, Segment and Count: A Generalized Framework for Object Counting [40.192374437785155]
  Class-agnostic object counting aims to count all objects in an image with respect to example boxes or class names. We propose a generalized framework for both few-shot and zero-shot object counting based on detection. PseCo achieves state-of-the-art performance in both few-shot/zero-shot object counting/detection.
  arXiv  Detail & Related papers  (2023-11-21T06:55:21Z)
• 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)
• Learning from Pseudo-labeled Segmentation for Multi-Class Object Counting [35.652092907690694]
  Class-agnostic counting (CAC) has numerous potential applications across various domains. The goal is to count objects of an arbitrary category during testing, based on only a few annotated exemplars. We show that the segmentation model trained on these pseudo-labeled masks can effectively localize objects of interest for an arbitrary multi-class image.
  arXiv  Detail & Related papers  (2023-07-15T01:33:19Z)
• Unicom: Universal and Compact Representation Learning for Image Retrieval [65.96296089560421]
  We cluster the large-scale LAION400M into one million pseudo classes based on the joint textual and visual features extracted by the CLIP model. To alleviate such conflict, we randomly select partial inter-class prototypes to construct the margin-based softmax loss. Our method significantly outperforms state-of-the-art unsupervised and supervised image retrieval approaches on multiple benchmarks.
  arXiv  Detail & Related papers  (2023-04-12T14:25:52Z)
• A Unified Object Counting Network with Object Occupation Prior [32.32999623924954]
  Existing object counting tasks are designed for a single object class. It is inevitable to encounter newly coming data with new classes in our real world. We build the first evolving object counting dataset and propose a unified object counting network.
  arXiv  Detail & Related papers  (2022-12-29T06:42:51Z)
• Semantic Representation and Dependency Learning for Multi-Label Image Recognition [76.52120002993728]
  We propose a novel and effective semantic representation and dependency learning (SRDL) framework to learn category-specific semantic representation for each category. Specifically, we design a category-specific attentional regions (CAR) module to generate channel/spatial-wise attention matrices to guide model. We also design an object erasing (OE) module to implicitly learn semantic dependency among categories by erasing semantic-aware regions.
  arXiv  Detail & Related papers  (2022-04-08T00:55:15Z)
• Rank4Class: A Ranking Formulation for Multiclass Classification [26.47229268790206]
  Multiclass classification (MCC) is a fundamental machine learning problem. We show that it is easy to boost MCC performance with a novel formulation through the lens of ranking.
  arXiv  Detail & Related papers  (2021-12-17T19:22:37Z)
• Rectifying the Shortcut Learning of Background: Shared Object Concentration for Few-Shot Image Recognition [101.59989523028264]
  Few-Shot image classification aims to utilize pretrained knowledge learned from a large-scale dataset to tackle a series of downstream classification tasks. We propose COSOC, a novel Few-Shot Learning framework, to automatically figure out foreground objects at both pretraining and evaluation stage.
  arXiv  Detail & Related papers  (2021-07-16T07:46:41Z)
• 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)
• One-Shot Object Detection without Fine-Tuning [62.39210447209698]
  We introduce a two-stage model consisting of a first stage Matching-FCOS network and a second stage Structure-Aware Relation Module. We also propose novel training strategies that effectively improve detection performance. Our method exceeds the state-of-the-art one-shot performance consistently on multiple datasets.
  arXiv  Detail & Related papers  (2020-05-08T01:59:23Z)

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.