The structure of protein-protein complexes is critical for understanding binding dynamics, biological mechanisms, and intervention strategies.

Finally, Section E illustrates qualitative results. We present the encoder-decoder variant of HAMT in fine-tuning on the right of Figure 1. Compared to the original cross-modal transformer on the left, the variant removes text-tovision cross-modal attention. The encoder encodes the texts to obtain textual embeddings. Theoriginal target location is viewed as a middle stop point.

Section A provides additional details for the method. The scene encoder is to extract the environment information. Following [19], we sample the frames at 2.5 HZ and predict future For ETH and UCY datasets, we adopt the standard metrics ( i . Due to the limitations discussed in Section 4.1, we introudce curve smoothing (CS) into current We conduct experiments on P A V using the traditional ADE/FDE metrics. In particular, our method improves the FDE by 13.6% on PETS.

Accurate sleep stage classification is significant for sleep health assessment. In recent years, several machine-learning based sleep staging algorithms have been developed , and in particular, deep-learning based algorithms have achieved performance on par with human annotation. Despite improved performance, a limitation of most deep-learning based algorithms is their black-box behavior, which have limited their use in clinical settings. Here, we propose a cross-modal transformer, which is a transformer-based method for sleep stage classification. The proposed cross-modal transformer consists of a novel cross-modal transformer encoder architecture along with a multi-scale one-dimensional convolutional neural network for automatic representation learning. Our method outperforms the state-of-the-art methods and eliminates the black-box behavior of deep-learning models by utilizing the interpretability aspect of the attention modules. Furthermore, our method provides considerable reductions in the number of parameters and training time compared to the state-of-the-art methods. Our code is available at https://github.com/Jathurshan0330/Cross-Modal-Transformer. A demo of our work can be found at https://bit.ly/Cross_modal_transformer_demo.

Existing audio-language task-specific predictive approaches focus on building complicated late-fusion mechanisms. However, these models are facing challenges of overfitting with limited labels and low model generalization abilities. In this paper, we present a Cross-modal Transformer for Audio-and-Language, i.e., CTAL, which aims to learn the intra-modality and inter-modality connections between audio and language through two proxy tasks on a large amount of audio-and-language pairs: masked language modeling and masked cross-modal acoustic modeling. After fine-tuning our pre-trained model on multiple downstream audio-and-language tasks, we observe significant improvements across various tasks, such as, emotion classification, sentiment analysis, and speaker verification. On this basis, we further propose a specially-designed fusion mechanism that can be used in fine-tuning phase, which allows our pre-trained model to achieve better performance. Lastly, we demonstrate detailed ablation studies to prove that both our novel cross-modality fusion component and audio-language pre-training methods significantly contribute to the promising results.