Exemplar Free Counting On Fsc147

Métriques

MAE(test)

MAE(val)

RMSE(test)

RMSE(val)

Résultats

Résultats de performance de divers modèles sur ce benchmark

					Paper Title
FamNet	32.27	32.15	131.46	98.75	Learning To Count Everything
RepRPN-Counter	26.66	29.24	129.11	98.11	Exemplar-free Class Incremental Learning via Discriminative and Comparable One-class Classifiers
RCC	17.12	17.49	104.53	58.81	Learning to Count Anything: Reference-less Class-agnostic Counting with Weak Supervision
LOCA	16.2	17.43	103.96	54.96	A Low-Shot Object Counting Network With Iterative Prototype Adaptation
DAVE	15.14	15.54	103.49	52.67	DAVE -- A Detect-and-Verify Paradigm for Low-Shot Counting
CounTR	14.71	18.07	106.87	71.84	CounTR: Transformer-based Generalised Visual Counting
GCA-SUN	14.00	16.06	92.19	53.04	GCA-SUNet: A Gated Context-Aware Swin-UNet for Exemplar-Free Counting
GeCo	13.30	14.81	108.72	64.95	A Novel Unified Architecture for Low-Shot Counting by Detection and Segmentation
SAVE	8.92	8.89	80.39	35.83	SAVE: Self-Attention on Visual Embedding for Zero-Shot Generic Object Counting

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Exemplar Free Counting On Fsc147 | SOTA | HyperAI