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Found 11 papers, 4 papers with code
PrevPredMap: Exploring Temporal Modeling with Previous Predictions for Online Vectorized HD Map Construction
Specifically, the previous-predictions-based query generator is designed to separately encode different types of information from previous predictions, which are then effectively utilized by the dynamic-position-query decoder to generate current predictions.
CrossDehaze: Scaling Up Image Dehazing with Cross-Data Vision Alignment and Augmentation
Previously, many methods based on priors and deep learning have been proposed to address the task of image dehazing.
NeRF2Points: Large-Scale Point Cloud Generation From Street Views' Radiance Field Optimization
Neural Radiance Fields (NeRF) have emerged as a paradigm-shifting methodology for the photorealistic rendering of objects and environments, enabling the synthesis of novel viewpoints with remarkable fidelity.
ReGeneration Learning of Diffusion Models with Rich Prompts for Zero-Shot Image Translation
To ensure consistent preservation of the shape during image editing, we propose cross-attention guidance based on regeneration learning.
Conformal Loss-Controlling Prediction
Conformal prediction is a learning framework controlling prediction coverage of prediction sets, which can be built on any learning algorithm for point prediction.
BEKG: A Built Environment Knowledge Graph
In this paper, more than 80, 000 paper abstracts in the built environment field were obtained to build a knowledge graph, a knowledge base storing entities and their connective relations in a graph-structured data model.
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results
This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results.
DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-Resolution
Previous image SR methods fail to exhibit similar performance on Real-SR as the image data is not aligned inherently.
Land Cover Classification from Multi-temporal, Multi-spectral Remotely Sensed Imagery using Patch-Based Recurrent Neural Networks
Even with the increased number of satellite systems and sensors acquiring data with improved spectral, spatial, radiometric and temporal characteristics and the new data distribution policy, most existing land cover datasets were derived from a pixel-based single-date multi-spectral remotely sensed image with low accuracy.
