Search Results for author:
Found 11 papers, 6 papers with code
Unleashing Text-to-Image Diffusion Prior for Zero-Shot Image Captioning
Additionally, to facilitate the model training with synthetic data, a novel CLIP-weighted cross-entropy loss is devised to prioritize the high-quality image-text pairs over the low-quality counterparts.
Improving Text-guided Object Inpainting with Semantic Pre-inpainting
In this paper, we propose to decompose the typical single-stage object inpainting into two cascaded processes: 1) semantic pre-inpainting that infers the semantic features of desired objects in a multi-modal feature space; 2) high-fieldity object generation in diffusion latent space that pivots on such inpainted semantic features.
Improving Virtual Try-On with Garment-focused Diffusion Models
To address this, we shape a new Diffusion model, namely GarDiff, which triggers the garment-focused diffusion process with amplified guidance of both basic visual appearance and detailed textures (i. e., high-frequency details) derived from the given garment.
ControlStyle: Text-Driven Stylized Image Generation Using Diffusion Priors
To achieve this, we present a new diffusion model (ControlStyle) via upgrading a pre-trained text-to-image model with a trainable modulation network enabling more conditions of text prompts and style images.
X-modaler: A Versatile and High-performance Codebase for Cross-modal Analytics
Nevertheless, there has not been an open-source codebase in support of training and deploying numerous neural network models for cross-modal analytics in a unified and modular fashion.
Scheduled Sampling in Vision-Language Pretraining with Decoupled Encoder-Decoder Network
Despite having impressive vision-language (VL) pretraining with BERT-based encoder for VL understanding, the pretraining of a universal encoder-decoder for both VL understanding and generation remains challenging.
Physical-Virtual Collaboration Modeling for Intra-and Inter-Station Metro Ridership Prediction
To address this problem, we model a metro system as graphs with various topologies and propose a unified Physical-Virtual Collaboration Graph Network (PVCGN), which can effectively learn the complex ridership patterns from the tailor-designed graphs.
Image Blind Denoising With Generative Adversarial Network Based Noise Modeling
In this paper, we consider a typical image blind denoising problem, which is to remove unknown noise from noisy images.
Deep Recurrent Convolutional Neural Network: Improving Performance For Speech Recognition
We further show that applying deep residual learning can boost the convergence speed of our novel deep recurret convolutional networks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Composing Music with Grammar Argumented Neural Networks and Note-Level Encoding
Creating aesthetically pleasing pieces of art, including music, has been a long-term goal for artificial intelligence research.
