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Hassan Akbari

7 accepted papers

2024

VideoPoet: A Large Language Model for Zero-Shot Video Generation

ICML 2024oral

We present VideoPoet, a language model capable of synthesizing high-quality video from a large variety of conditioning signals. VideoPoet employs a decoder-only transformer architecture that processes multimodal inputs -- including images, videos, text, and audio. The training protocol follows that…

Cited by 257SourcePDFScholar
2023

Alternating Gradient Descent and Mixture-of-Experts for Integrated Multimodal Perception

NeurIPS 2023poster

We present Integrated Multimodal Perception (IMP), a simple and scalable multimodal multi-task training and modeling approach. IMP integrates multimodal inputs including image, video, text, and audio into a single Transformer encoder with minimal modality-specific components. IMP makes use of a nove…

Cited by 23SourcePDFScholar
2023

PaLI: A Jointly-Scaled Multilingual Language-Image Model

ICLR 2023top-5%

Effective scaling and a flexible task interface enable large language models to excel at many tasks. We present PaLI, a model that extends this approach to the joint modeling of language and vision. PaLI generates text based on visual and textual inputs, and with this interface performs many vision,…

2022

Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization

NeurIPS 2022accept

Self-supervised pre-training recently demonstrates success on large-scale multimodal data, and state-of-the-art contrastive learning methods often enforce the feature consistency from cross-modality inputs, such as video/audio or video/text pairs. Despite its convenience to formulate and leverage in…

Cited by 13SourcePDFScholar
2021

VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text

NeurIPS 2021poster

We present a framework for learning multimodal representations from unlabeled data using convolution-free Transformer architectures. Specifically, our Video-Audio-Text Transformer (VATT) takes raw signals as inputs and extracts multimodal representations that are rich enough to benefit a variety of…

2019

Multi-Level Multimodal Common Semantic Space for Image-Phrase Grounding

CVPR 2019poster

We address the problem of phrase grounding by learning a multi-level common semantic space shared by the textual and visual modalities. We exploit multiple levels of feature maps of a Deep Convolutional Neural Network, as well as contextualized word and sentence embeddings extracted from a character…

Cited by 99PDFcodeScholar
2018

Lip2Audspec: Speech Reconstruction from Silent Lip Movements Video

ICASSP 2018accepted

In this study, we propose a deep neural network for reconstructing intelligible speech from silent lip movement videos. We use auditory spectrogram as spectral representation of speech and its corresponding sound generation method resulting in a more natural sounding reconstructed speech. Our propos…

Cited by 0SourceScholar