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Learning in audio-visual context: A review, analysis, and new perspective
Sight and hearing are two senses that play a vital role in human communication and scene
understanding. To mimic human perception ability, audio-visual learning, aimed at …
understanding. To mimic human perception ability, audio-visual learning, aimed at …
Onellm: One framework to align all modalities with language
Multimodal large language models (MLLMs) have gained significant attention due to their
strong multimodal understanding capability. However existing works rely heavily on modality …
strong multimodal understanding capability. However existing works rely heavily on modality …
Lavis: A library for language-vision intelligence
We introduce LAVIS, an open-source deep learning library for LAnguage-VISion research
and applications. LAVIS aims to serve as a one-stop comprehensive library that brings …
and applications. LAVIS aims to serve as a one-stop comprehensive library that brings …
Videollm-online: Online video large language model for streaming video
Abstract Large Language Models (LLMs) have been enhanced with vision capabilities
enabling them to comprehend images videos and interleaved vision-language content …
enabling them to comprehend images videos and interleaved vision-language content …
Learning to answer questions in dynamic audio-visual scenarios
In this paper, we focus on the Audio-Visual Question Answering (AVQA) task, which aims to
answer questions regarding different visual objects, sounds, and their associations in …
answer questions regarding different visual objects, sounds, and their associations in …
[HTML][HTML] Learning towards conversational AI: A survey
Recent years have witnessed a surge of interest in the field of open-domain dialogue.
Thanks to the rapid development of social media, large dialogue corpus from the Internet …
Thanks to the rapid development of social media, large dialogue corpus from the Internet …
Modality competition: What makes joint training of multi-modal network fail in deep learning?(provably)
Despite the remarkable success of deep multi-modal learning in practice, it has not been
well-explained in theory. Recently, it has been observed that the best uni-modal network …
well-explained in theory. Recently, it has been observed that the best uni-modal network …
Macaw-llm: Multi-modal language modeling with image, audio, video, and text integration
Although instruction-tuned large language models (LLMs) have exhibited remarkable
capabilities across various NLP tasks, their effectiveness on other data modalities beyond …
capabilities across various NLP tasks, their effectiveness on other data modalities beyond …
What makes training multi-modal classification networks hard?
Consider end-to-end training of a multi-modal vs. a uni-modal network on a task with
multiple input modalities: the multi-modal network receives more information, so it should …
multiple input modalities: the multi-modal network receives more information, so it should …
PLATO: Pre-trained dialogue generation model with discrete latent variable
Pre-training models have been proved effective for a wide range of natural language
processing tasks. Inspired by this, we propose a novel dialogue generation pre-training …
processing tasks. Inspired by this, we propose a novel dialogue generation pre-training …