As such, web-crawling is an essential tool for both computational and non-computational scientists to conduct research.
Deepfake detection faces a critical generalization hurdle, with performance deteriorating when there is a mismatch between the distributions of training and testing data.
Despite achieving nearly perfect accuracy in the vanilla NIAH test, all models exhibit large performance drops as the context length increases.
The dataset comprises 236, 220 pairs of style prompt in natural text descriptions with five style factors and corresponding speech samples.
To help keep pace with the rapid advancements in computer vision, this paper aims to provide a comprehensive review of visual Mamba approaches.
We present InstantMesh, a feed-forward framework for instant 3D mesh generation from a single image, featuring state-of-the-art generation quality and significant training scalability.
We outperform encoder-only models by a large margin on word-level tasks and reach a new unsupervised state-of-the-art performance on the Massive Text Embeddings Benchmark (MTEB).
For the training, we begin with the knowledge distillation from the SAM-ViT-H image encoder to EfficientViT.
In this paper, we are the first to propose a hybrid model, dubbed as Show-1, which marries pixel-based and latent-based VDMs for text-to-video generation.
Ranked #2 on Text-to-Video Generation on EvalCrafter Text-to-Video (ECTV) Dataset (using extra training data)
As for evaluation, we build WorldNet, a multimodal state transition prediction benchmark encompassing varied real-life scenarios.