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Found 18 papers, 7 papers with code
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.
Multi-perspective Contrastive Logit Distillation
Recent research on logit distillation has primarily focused on maximizing the information learned from the teacher model's logits to enhance the performance of the student model.
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.
Learn from Balance: Rectifying Knowledge Transfer for Long-Tailed Scenarios
Consequently, the teacher network can provide balanced and accurate knowledge to train a reliable student network.
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.
Global Priors Guided Modulation Network for Joint Super-Resolution and Inverse Tone-Mapping
Joint super-resolution and inverse tone-mapping (SR-ITM) aims to enhance the visual quality of videos that have quality deficiencies in resolution and dynamic range.
Transcoded Video Restoration by Temporal Spatial Auxiliary Network
In most video platforms, such as Youtube, and TikTok, the played videos usually have undergone multiple video encodings such as hardware encoding by recording devices, software encoding by video editing apps, and single/multiple video transcoding by video application servers.
Real-time FPGA Design for OMP Targeting 8K Image Reconstruction
The choice of sensing matrix, the implementation of DP and LSP.
CSMCNet: Scalable Video Compressive Sensing Reconstruction with Interpretable Motion Estimation
Most deep network methods for compressive sensing reconstruction suffer from the black-box characteristic of DNN.
RCLC: ROI-based joint conventional and learning video compression
COVID-19 leads to the high demand for remote interactive systems ever seen.
Deep Photo Scan: Semi-Supervised Learning for dealing with the real-world degradation in Smartphone Photo Scanning
Second, we simulate many different variants of the real-world degradation using low-level image transformation to gain a generalization in smartphone-scanned image properties, then train a degradation network to generalize all styles of degradation and provide pseudo-scanned photos for unscanned images as if they were scanned by a smartphone.
Image Compression with Encoder-Decoder Matched Semantic Segmentation
In recent years, layered image compression is demonstrated to be a promising direction, which encodes a compact representation of the input image and apply an up-sampling network to reconstruct the image.
B-DRRN: A Block Information Constrained Deep Recursive Residual Network for Video Compression Artifacts Reduction
In this paper, we design a neural network to enhance the quality of the compressed frame by leveraging the block information, called B-DRRN (Deep Recursive Residual Network with Block information).
Deep Preset: Blending and Retouching Photos with Color Style Transfer
It is designed to 1) generalize the features representing the color transformation from content with natural colors to retouched reference, then blend it into the contextual features of content, 2) predict hyper-parameters (settings or preset) of the applied low-level color transformation methods, 3) stylize content to have a similar color style as reference.
Semantic-driven Colorization
As a human experience in colorization, our brains first detect and recognize the objects in the photo, then imagine their plausible colors based on many similar objects we have seen in real life, and finally colorize them, as described in the teaser.
