This article introduces DiffSpeaker, a new model for speech-driven 3D facial animation. Traditional models use either Diffusion models or Transformer architectures, but these have limitations due to the shortage of…
Continue readingThe article presents a novel method for time series forecasting, the Sparse Vector Quantized FFN-Free Transformer (Sparse-VQ). This approach leverages sparse vector quantization and Reverse Instance Normalization to reduce noise…
Continue readingThe study focuses on improving the performance of large language models (LLMs) in tasks that involve repetitive sub-tasks or deceptive contents. Existing prompting strategies often fall short in these areas…
Continue readingThe study focuses on Reinforcement Learning from Human Feedback (RLHF) and how it can be optimized. Traditionally, RLHF involves generating completions from a language model in response to a query…
Continue readingODIN (Omni-Dimensional INstance segmentation) is a model proposed to challenge the belief that 2D and 3D perception require distinct model architectures. ODIN can segment and label both 2D RGB images…
Continue readingThe article presents a training approach for Neural Machine Translation (NMT) based on Layer-wise Relevance Propagation (LRP). The approach is applied to Unsupervised and Supervised model training, for translation of…
Continue readingThe paper examines in-context learning (ICL) in large language models based on the transformer architecture. It explores how transformers learn in complex scenarios, specifically studying learning with representations. The authors…
Continue readingThe research paper by Zhong, Bhattad, Wang, and Forsyth from the University of Illinois Urbana-Champaign investigates the lack of equivariance in state-of-the-art depth and normal predictors. Despite strong performances, these…
Continue readingLooks like you're using an ad blocker. Please support us by disabling these ad blocker.
