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..
Masked Vision and Language Modeling for Multi-modal Representation Learning
Authors: Gukyeong Kwon, Zhaowei Cai, Avinash Ravichandran, Erhan Bas, Rahul Bhotika, Stefano Soatto
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on various V+L tasks show that the proposed method, along with common V+L alignment losses, achieves state-of-the-art performance in the regime of millions of pre-training data. |
| Researcher Affiliation | Industry | Gukyeong Kwon, Zhaowei Cai, Avinash Ravichandran, Erhan Bas, Rahul Bhotika, Stefano Soatto AWS AI Labs EMAIL |
| Pseudocode | No | No explicit pseudocode or algorithm blocks are provided in the paper. |
| Open Source Code | No | No explicit statement about releasing source code for the described methodology or a direct link to a code repository is found in the paper. |
| Open Datasets | Yes | We use the union of four datasets for pre-training... These datasets are Conceptual Captions (CC) (Sharma et al., 2018), SBU Captions (Ordonez et al., 2011), Visual Genome (VG) (Krishna et al., 2017), and COCO Captions (Lin et al., 2014). |
| Dataset Splits | Yes | To be specific, we follow data splits proposed in (Karpathy & Fei-Fei, 2015) and an average recall over image and text retrieval is used to find the best model in the validation set. |
| Hardware Specification | Yes | A batch size of 512 is used with 16 NVIDIA Tesla V100 GPUs. |
| Software Dependencies | No | We used the Image Net pretrained Vi T (vit base patch16 224) from (Wightman, 2019) and the pre-trained Ro BERTa (roberta-base) from Hugging Face (Wolf et al., 2020). |
| Experiment Setup | Yes | We pre-train the model for 50 epochs when the 4M dataset is used and 30 epochs for all other experiments. A batch size of 512 is used with 16 NVIDIA Tesla V100 GPUs. All parameters are optimized using Adam W (Loshchilov & Hutter, 2017) with a weight decay of 0.05. Following (Xie et al., 2021), we use the image masking ratio of 60%. While 15% masking ratio is used for text in language models (Devlin et al., 2018; Liu et al., 2019), we use 30% since the paired image can provide additional information for text reconstruction. During pre-training, the learning rate is warmed up to 3 × 10−4 in the first 5 epochs and decayed to 3 × 10−5 using a cosine scheduler. The learning rates for the image encoder and the text encoder are set to 10−5, which is less than that of the cross-modality encoders. An image size of 224 × 224 and Rand Augment (Cubuk et al., 2020) are used. |