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Found 9 papers, 0 papers with code
OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model
Omnidirectional images (ODIs) are commonly used in real-world visual tasks, and high-resolution ODIs help improve the performance of related visual tasks.
Invertible Diffusion Models for Compressed Sensing
To enable such memory-intensive end-to-end finetuning, we propose a novel two-level invertible design to transform both (1) the multi-step sampling process and (2) the noise estimation U-Net in each step into invertible networks.
360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model
Recently, the emerging text-to-video (T2V) diffusion methods demonstrate notable effectiveness in standard video generation.
EditGuard: Versatile Image Watermarking for Tamper Localization and Copyright Protection
In the era where AI-generated content (AIGC) models can produce stunning and lifelike images, the lingering shadow of unauthorized reproductions and malicious tampering poses imminent threats to copyright integrity and information security.
Long-Tailed Learning as Multi-Objective Optimization
Real-world data is extremely imbalanced and presents a long-tailed distribution, resulting in models that are biased towards classes with sufficient samples and perform poorly on rare classes.
Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution
Multi-stage strategies are frequently employed in image restoration tasks.
OPDN: Omnidirectional Position-aware Deformable Network for Omnidirectional Image Super-Resolution
Model A aims to enhance the feature extraction ability of 360{\deg} image positional information, while Model B further focuses on the high-frequency information of 360{\deg} images.
D3C2-Net: Dual-Domain Deep Convolutional Coding Network for Compressive Sensing
By unfolding the proposed framework into deep neural networks, we further design a novel Dual-Domain Deep Convolutional Coding Network (D3C2-Net) for CS imaging with the capability of transmitting high-throughput feature-level image representation through all the unfolded stages.
Ensemble Knowledge Distillation for CTR Prediction
Recently, deep learning-based models have been widely studied for click-through rate (CTR) prediction and lead to improved prediction accuracy in many industrial applications.
