Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification
Authors: Muhammad Ferjad Naeem, Yongqin Xian, Luc V Gool, Federico Tombari
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Quantitatively, we demonstrate that our I2DFormer significantly outperforms previous unsupervised semantic embeddings under both zero-shot and generalized zero-shot learning settings on three public datasets.We conduct extensive experiments on Animals with Attributes2 (AWA2) [57], Caltech-UCSD Birds (CUB) [51] and Oxford Flowers (FLO) [32], which are widely used datasets in ZSL. |
| Researcher Affiliation | Collaboration | Muhammad Ferjad Naeem1 Yongqin Xian1 Luc Van Gool1 Federico Tombari2,3 1 ETH Zürich 2 TUM 3Google |
| Pseudocode | No | The paper presents architectural diagrams and describes methods in text, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code available at https://github.com/ferjad/I2DFormer. |
| Open Datasets | Yes | We conduct extensive experiments on Animals with Attributes2 (AWA2) [57], Caltech-UCSD Birds (CUB) [51] and Oxford Flowers (FLO) [32], which are widely used datasets in ZSL. |
| Dataset Splits | Yes | We follow the evaluation protocol and data splits proposed by Xian et al. [57]. |
| Hardware Specification | Yes | We implement our model in Py Torch and train on an Nvidia A100 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | Yes | The model is trained with Adam optimizer with a learning rate of 1e 3 and takes 24 hours to converge. LCLS and Llocal relative weights are chosen by ablation. More details are available in the supplementary. |