Object Counting
60 papers with code • 10 benchmarks • 23 datasets
The goal of Object Counting task is to count the number of object instances in a single image or video sequence. It has many real-world applications such as traffic flow monitoring, crowdedness estimation, and product counting.
Source: Learning to Count Objects with Few Exemplar Annotations
Benchmarks
These leaderboards are used to track progress in Object Counting
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Libraries
Use these libraries to find Object Counting models and implementations
MS COCO
PASCAL VOC
PASCAL VOC 2007
FSOD
CARPK
FSC147
COWC
TallyQA
AGAR
Latest papers
Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and Analysis
The Change-Agent integrates a multi-level change interpretation (MCI) model as the eyes and a large language model (LLM) as the brain.
Few-shot Object Localization
ryh1218/fsol
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This task achieves generalized object localization by leveraging a small number of labeled support samples to query the positional information of objects within corresponding images.
Griffon v2: Advancing Multimodal Perception with High-Resolution Scaling and Visual-Language Co-Referring
Large Vision Language Models have achieved fine-grained object perception, but the limitation of image resolution remains a significant obstacle to surpass the performance of task-specific experts in complex and dense scenarios.
NWPU-MOC: A Benchmark for Fine-grained Multi-category Object Counting in Aerial Images
lyongo/nwpu-moc
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Considering the absence of a dataset for this task, a large-scale Dataset (NWPU-MOC) is collected, consisting of 3, 416 scenes with a resolution of 1024 $\times$ 1024 pixels, and well-annotated using 14 fine-grained object categories.
VLCounter: Text-aware Visual Representation for Zero-Shot Object Counting
Zero-Shot Object Counting (ZSOC) aims to count referred instances of arbitrary classes in a query image without human-annotated exemplars.
Point, Segment and Count: A Generalized Framework for Object Counting
hzzone/pseco
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In this paper, we propose a generalized framework for both few-shot and zero-shot object counting based on detection.
STEERER: Resolving Scale Variations for Counting and Localization via Selective Inheritance Learning
taohan10200/steerer
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ICCV 2023
Scale variation is a deep-rooted problem in object counting, which has not been effectively addressed by existing scale-aware algorithms.
Learning to Count without Annotations
While recent supervised methods for reference-based object counting continue to improve the performance on benchmark datasets, they have to rely on small datasets due to the cost associated with manually annotating dozens of objects in images.
Training-free Object Counting with Prompts
shizenglin/training-free-object-counter
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However, the vanilla mask generation method of SAM lacks class-specific information in the masks, resulting in inferior counting accuracy.
RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
However, these models primarily learn low-level features and require annotated data for fine-tuning.
