**CTAL** is a pre-training framework for strong audio-and-language representations with a [Transformer](https://paperswithcode.com/method/transformer), which aims to learn the intra-modality and inter-modalities 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. The pre-trained model is a Transformer for Audio and Language, i.e., CTAL, which consists of two modules, a language stream encoding module which adapts word as input element, and a text-referred audio stream encoder module which accepts both frame-level Mel-spectrograms and token-level output embeddings from the language stream

**Spiking Neural Networks** (**SNNs**)  are a class of artificial neural networks inspired by the structure and functioning of the brain's neural networks. Unlike traditional artificial neural networks that operate based on continuous firing rates, SNNs simulate the behavior of individual neurons through discrete spikes or action potentials. These spikes are triggered when the neuron's membrane potential reaches a certain threshold, and they propagate through the network, communicating information and triggering subsequent neuron activations. This spike-based communication allows SNNs to capture the temporal dynamics of information processing and exhibit asynchronous, event-driven behavior, making them well-suited for tasks such as temporal pattern recognition, event detection, and real-time processing. SNNs have gained attention due to their potential in efficiently processing and encoding information, offering advantages in energy efficiency, robustness, and compatibility with neuromorphic hardware architectures.

Self-Normalizing Neural Networks

CTAL

CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations

**Animatable Reconstruction of Clothed Humans** is an end-to-end framework for accurate reconstruction of animation-ready 3D clothed humans from a monocular image. ARCH is a learned pose-aware model that produces detailed 3D rigged full-body human avatars from a single unconstrained RGB image. A Semantic Space and a Semantic Deformation Field are created using a parametric 3D body estimator. They allow the transformation of 2D/3D clothed humans into a canonical space, reducing ambiguities in geometry caused by pose variations and occlusions in training data. Detailed surface geometry and appearance are learned using an implicit function representation with spatial local features.

Source	CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com