1adrianb / 1adrianb/binary-human-pose-estimation

how to extract multi-scale feature by using Fig.4e block?

Ouverte
#16 0 commentaires 0 réactions 0 personnes assignées Voir sur GitHub
Langage dominant
Lua
Étoiles
214
Forks
67
Métriques de merge des PR
Aucune PR mergée en 30 j

Description

hi,
I have a question about the final block.
In your paper, you mentioned multi-scale should be used in Fig.4e which is your final block. However, Fig.4e only contains 3x3 conv. It doesn't show any pooling or conv with stride operation. I can't understand how to extract multi-scale feature by using it. Could you explain more details about it? Thanks a lot.
![image](https://user-images.githubusercontent.com/47654539/60490432-1a9f6200-9cd9-11e9-9e31-1cd2f26829e4.png)

Guide de contribution

Aucun guide de contribution indexé pour ce dépôt

Piste de recherche

The issue references Figure 4e from the paper and asks about multi-scale feature extraction. The repository contains Lua/Torch7 code for human pose estimation. To understand the implementation, examine the model architecture files (likely in the root or a models/ directory) for the final block. Look for convolutional layers, pooling, or stride operations. Compare the code to the paper's description. Determine if the figure is accurately implemented or if there's a discrepancy. Running the demo might help visualize the feature maps.

Rédigé par le modèle d'indexation à partir du texte de l'issue.

Évaluation

Stack technique
computer-vision, lua
Domaine
computer-vision, machine-learning
Type d'issue
Documentation
Difficulté
4/5
Temps estimé
3-5 jours
Activité
À l'abandon
Clarté
À clarifier
Accessibilité débutants
35/100

Recevez les nouvelles issues par e-mail

Un résumé court des issues GitHub adaptées aux débutants.