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Found 8 papers, 4 papers with code
Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables
To address these challenges, we propose Pan-LUT, a novel learnable look-up table (LUT) framework for pan-sharpening that strikes a balance between performance and computational efficiency for high-resolution remote sensing images.
ModelGrow: Continual Text-to-Video Pre-training with Model Expansion and Language Understanding Enhancement
There is a lack of extensive study of the continual pre-training techniques in T2V generation.
Large Motion Video Autoencoding with Cross-modal Video VAE
Directly applying image VAEs to individual frames in isolation can result in temporal inconsistencies and suboptimal compression rates due to a lack of temporal compression.
High-fidelity 3D GAN Inversion by Pseudo-multi-view Optimization
We present a high-fidelity 3D generative adversarial network (GAN) inversion framework that can synthesize photo-realistic novel views while preserving specific details of the input image.
Shape from Polarization for Complex Scenes in the Wild
We present a new data-driven approach with physics-based priors to scene-level normal estimation from a single polarization image.
Dual-Camera Super-Resolution with Aligned Attention Modules
We present a novel approach to reference-based super-resolution (RefSR) with the focus on dual-camera super-resolution (DCSR), which utilizes reference images for high-quality and high-fidelity results.
Depth Sensing Beyond LiDAR Range
Depth sensing is a critical component of autonomous driving technologies, but today's LiDAR- or stereo camera-based solutions have limited range.
Video Depth Estimation by Fusing Flow-to-Depth Proposals
Our flow-to-depth layer is differentiable, and thus we can refine camera poses by maximizing the aggregated confidence in the camera pose refinement module.
