Search Results for author:
Found 9 papers, 2 papers with code
DiverseDream: Diverse Text-to-3D Synthesis with Augmented Text Embedding
Text-to-3D synthesis has recently emerged as a new approach to sampling 3D models by adopting pretrained text-to-image models as guiding visual priors.
EmbryosFormer: Deformable Transformer and Collaborative Encoding-Decoding for Embryos Stage Development Classification
The transformer expanding path models the temporal coherency between embryo images to ensure monotonic non-decreasing constraint and is optimized by a segmentation head.
SP-GPT2: Semantics Improvement in Vietnamese Poetry Generation
Both automatic and human evaluation demonstrated that our approach can generate poems that have better cohesion without losing the quality due to additional loss.
Lightweight Monocular Depth with a Novel Neural Architecture Search Method
This paper presents a novel neural architecture search method, called LiDNAS, for generating lightweight monocular depth estimation models.
Monocular Depth Estimation Primed by Salient Point Detection and Normalized Hessian Loss
In addition, we introduce a normalized Hessian loss term invariant to scaling and shear along the depth direction, which is shown to substantially improve the accuracy.
Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion
In this paper, we propose enhancing monocular depth estimation by adding 3D points as depth guidance.
RGBD-Net: Predicting color and depth images for novel views synthesis
We propose a new cascaded architecture for novel view synthesis, called RGBD-Net, which consists of two core components: a hierarchical depth regression network and a depth-aware generator network.
A Multi-Objective Learning to re-Rank Approach to Optimize Online Marketplaces for Multiple Stakeholders
However these systems are most often designed to address the objective of one single stakeholder, typically, in online commerce, the consumers whose input and purchasing decisions ultimately determine the success of the recommendation systems.
Factorizing LambdaMART for cold start recommendations
Motivated by the fact that very often the users' and items' descriptions as well as the preference behavior can be well summarized by a small number of hidden factors, we propose a novel algorithm, LambdaMART Matrix Factorization (LambdaMART-MF), that learns a low rank latent representation of users and items using gradient boosted trees.
