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Enhanced Blockwise Classification

EBC

4 papers tagged archive 2025-07-28

Introduced by Yiming Ma et al. in CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Traditional methods are based on block-wise regression. This framework, Enhanced Blockwise Classification (EBC), however, is based on the idea that aims to classify the count value within each block into several pre-defined bins. The enhancement comes from 3 aspects: discretization policy, label correction and loss function.

Notice that the original block-wise classification concept was introduced by Liu et al. in Counting Objects by Blockwise Classification.

PaperSource

Papers archive 2025-07-28

4 shown of 4, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

11 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Crowd Counting2
Density Estimation2
Classification1
Edge-computing1
Event Detection1
Event-based vision1
Object1
Object Detection1
Quantization1
image-classification1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with EBC: 2024 to 2025, peak 2 2 0 2024: 2 papers 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (4 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Counting Methods

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