This repository contains implementations of 5 classical zero-shot algorithms (SAE, ALE, SJE, ESZSL, and DeViSE) in the usual as well as the Generalized zero-shot learning (GZSL) settings using the
Proposed Split
and evaluation protocols (eg. Class-Averaged Top-1 Accuracy) outlined in
Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly (ZSLGBU) by Yongqin Xian, Christoph H. Lampert, Bernt Schiele, Zeynep Akata (TPAMI 2018).
The original papers corresponding to the 5 algorithms are:
[1] SAE (Semantic Autoencoder) - Semantic Autoencoder for Zero-Shot Learning. Elyor Kodirov, Tao Xiang, Shaogang Gong. CVPR, 2017.
[2] ALE (Attribute Label Embedding) - Label-Embedding for Image Classification. Zeynep Akata, Florent Perronnin, Zaid Harchaoui, Cordelia Schmid. TPAMI, 2016.
[3] SJE (Structured Joint Embedding) - Evaluation of Output Embeddings for Fine-Grained Image Classification. Zeynep Akata, Scott Reed, Daniel Walter, Honglak Lee, Bernt Schiele. CVPR, 2015.
[4] ESZSL - An embarrassingly simple approach to zero-shot learning. Bernardino Romera-Paredes, Philip H. S. Torr. ICML, 2015.
[5] DeViSE - DeViSE: A Deep Visual-Semantic Embedding Model. Andrea Frome*, Greg S. Corrado*, Jonathon Shlens*, Samy Bengio, Jeffrey Dean, Marc’Aurelio Ranzato, Tomas Mikolov. NIPS, 2013.
Dataset | Total Images | Attributes | Class Split (Tr+Val+Ts) | ZSL | GZSL | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
tr | val | ts | tr | val | tr+val | ts seen | ts unseen | ||||
SUN | 14340 | 102 | 580+65+72 | 11600 | 1300 | 1440 | 9280 | 1040 | 10320 | 2580 | 1440 |
CUB | 11788 | 312 | 100+50+50 | 5875 | 2946 | 2967 | 4702 | 2355 | 7057 | 1764 | 2967 |
AWA1 | 30475 | 85 | 27+13+10 | 16864 | 7926 | 5685 | 13460 | 6372 | 19832 | 4958 | 5685 |
AWA2 | 37322 | 85 | 27+13+10 | 20218 | 9191 | 7913 | 16187 | 7340 | 23527 | 5882 | 7913 |
aPY | 15339 | 64 | 15+5+12 | 6086 | 1329 | 7924 | 4906 | 1026 | 5932 | 1483 | 7924 |
Each folder above has its own README
with running instructions, results and their comparisons with those reported in ZSLGBU.
To be updated soon...
If you find any errors, kindly raise an issue and I will get back to you ASAP.