AudioSet) has an even larger negative impact.

IMP-MoE-L focusing on video tasks that achieves new state-of-the-art in zero-shot video classification: 77.0% on Kinetics-400, 76.8% on Kinetics-600, and 68.3% on

The learnt models in the latter domain can then be used for a diverse set of tasks in a zero-shot way, similar to "Contrastive Language-Image

In this paper, we develop MAE as a unified, modality-agnostic SSL framework.

Audio-visual learning has been a major pillar of multi-modal machine learning, where the community mostly focused on its modality-aligned setting, i.e ., the

Limitations One limitation of our model is its potential for data bias. This could limit the applications of the model. MLLMs could be used to create fake news articles or social media posts. Hyperparameters Number of layers 24 Hidden size 2,048 FFN inner hidden size 8,192 Attention heads 32 Dropout 0.1 Attention dropout 0.1 Activation function GeLU [1] V ocabulary size 64,007 Soft tokens V size 64 Max length 2,048 Relative position embedding xPos [2] Initialization Magneto [3] Table 1: Hyperparameters of causal language model of K The detailed instruction tuning hyperparameters are listed in Table 3. The models are trained on web-scale multimodal corpora.

A big convergence of language, multimodal perception, action, and world modeling is a key step toward artificial general intelligence.

On one hand, it operates as a general data augmentation strategy.