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TKG-DM: Training-free Chroma Key Content Generation Diffusion Model
To address this limitation, we present a novel Training-Free Chroma Key Content Generation Diffusion Model (TKG-DM), which optimizes the initial random noise to produce images with foreground objects on a specifiable color background.
Block based Adaptive Compressive Sensing with Sampling Rate Control
To further reduce the sampled data while keeping the video quality, this paper explores the temporal redundancy in video CS and proposes a block based adaptive compressive sensing framework with a sampling rate (SR) control strategy.
Visual question answering based evaluation metrics for text-to-image generation
After that, we use Visual Question Answering(VQA) to measure the relevance of the generated images to the input text, which allows for a more detailed evaluation of the alignment compared to existing methods.
Edge-based Denoising Image Compression
In recent years, deep learning-based image compression, particularly through generative models, has emerged as a pivotal area of research.
BATINet: Background-Aware Text to Image Synthesis and Manipulation Network
We proposed a Background-Aware Text to Image synthesis and manipulation Network (BATINet), which contains two key components: Position Detect Network (PDN) and Harmonize Network (HN).
Interactive Image Manipulation with Complex Text Instructions
To solve these problems, we propose a novel image manipulation method that interactively edits an image using complex text instructions.
