It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval, which provides a unified model foundation for real-world IR applications.
In this paper, we explore this objective and propose LaVi-Bridge, a pipeline that enables the integration of diverse pre-trained language models and generative vision models for text-to-image generation.
In this work, we address various segmentation tasks, each traditionally tackled by distinct or partially unified models.
Unfortunately, it is challenging to distinguish MGTs and human-written texts because the distributional discrepancy between them is often very subtle due to the remarkable performance of LLMs.
Animating a still image offers an engaging visual experience.
For improving texture synthesis, we enhance the discriminator of AOT-GAN by training it with a tailored mask-prediction task.
Ranked #9 on Image Inpainting on Places2
Knowledge distillation involves transferring soft labels from a teacher to a student using a shared temperature-based softmax function.
Ranked #1 on Knowledge Distillation on CIFAR-100
Large-scale sequence modeling has sparked rapid advances that now extend into biology and genomics.
In this paper, we present StyleTTS 2, a text-to-speech (TTS) model that leverages style diffusion and adversarial training with large speech language models (SLMs) to achieve human-level TTS synthesis.
Although the map construction is essentially a point set prediction task, MapQR utilizes instance queries rather than point queries.