MAP: Modality-Agnostic Uncertainty-Aware Vision-Language Pre-training Model

Multimodal semantic understanding often has to deal with uncertainty, which means the obtained message tends to refer to multiple targets. Such uncertainty is problematic for our interpretation, including intra-modal and inter-modal uncertainty. Little effort studies the modeling of this uncertainty, particularly in pre-training on unlabeled datasets and fine-tuning in task-specific downstream tasks. To address this, we project the representations of all modalities as probabilistic distributions via a Probability Distribution Encoder (PDE) by utilizing rich multimodal semantic information. Furthermore, we integrate uncertainty modeling with popular pre-training frameworks and propose suitable pre-training tasks: Distribution-based Vision-Language Contrastive learning (D-VLC), Distribution-based Masked Language Modeling (D-MLM), and Distribution-based Image-Text Matching (D-ITM). The fine-tuned models are applied to challenging downstream tasks, including image-text retrieval, visual question answering, visual reasoning, and visual entailment, and achieve state-of-the-art results. Code is released at https://github.com/IIGROUP/MAP.

Egocentric Video-Language Pretraining @ EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022

In this report, we propose a video-language pretraining (VLP) based solution \cite{kevin2022egovlp} for the EPIC-KITCHENS-100 Multi-Instance Retrieval (MIR) challenge. Especially, we exploit the recently released Ego4D dataset \cite{grauman2021ego4d} to pioneer Egocentric VLP from pretraining dataset, pretraining objective, and development set. Based on the above three designs, we develop a pretrained video-language model that is able to transfer its egocentric video-text representation to MIR benchmark. Furthermore, we devise an adaptive multi-instance max-margin loss to effectively fine-tune the model and equip the dual-softmax technique for reliable inference. Our best single model obtains strong performance on the challenge test set with 47.39% mAP and 61.44% nDCG. The code is available at https://github.com/showlab/EgoVLP.