Bipolar Disorder, Digital Phenotyping, Multimodal Learning, Face/Voice/Phone, Mood Classification, Relapse Prediction, T-SNE, Ablation Share and Cite: de Filippis, R. and Al Foysal, A. (2025) ...

According to DeepLearning.AI, Amazon has introduced the Nova 2 family, which includes Pro, Omni, Lite, and Sonic models, delivering highly competitive multimodal reasoning and generation capabilities.

T5Gemma 2 follows the same adaptation idea introduced in T5Gemma, initialize an encoder-decoder model from a decoder-only checkpoint, then adapt with UL2. In the above figure the research team show ...

SAM Audio uses separate encoders for each conditioning signal, an audio encoder for the mixture, a text encoder for the natural language description, a span encoder for time anchors, and a visual ...

Despite notable progress in deep learning, change detection (CD) in remote sensing images continues to pose significant challenges, especially for multimodal datasets due to intrinsic differences [1].

With the great success of large language models, self-supervised pre-training technologies have shown the great promise in the field of drug discovery. In particular, multimodal pre-training models ...

CLIP is one of the most important multimodal foundational models today, aligning visual and textual signals into a shared feature space using a simple contrastive learning loss on large-scale ...

Abstract: Knowledge distillation (KD) is the de facto standard for compressing large-scale multimodal models into smaller ones. Prior works have explored ever more complex KD strategies involving ...