Trending Research
SVTR: Scene Text Recognition with a Single Visual Model
Dominant scene text recognition models commonly contain two building blocks, a visual model for feature extraction and a sequence model for text transcription.
READ: Large-Scale Neural Scene Rendering for Autonomous Driving
In this paper, a large-scale neural rendering method is proposed to synthesize the autonomous driving scene~(READ), which makes it possible to synthesize large-scale driving scenarios on a PC through a variety of sampling schemes.
Ranked #1 on
Novel View Synthesis
on KITTI
unilm
microsoft/unilm
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Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
Neural 3D Scene Reconstruction with the Manhattan-world Assumption
Based on the Manhattan-world assumption, planar constraints are employed to regularize the geometry in floor and wall regions predicted by a 2D semantic segmentation network.
HeadNeRF: A Real-time NeRF-based Parametric Head Model
Different from existing related parametric models, we use the neural radiance fields as a novel 3D proxy instead of the traditional 3D textured mesh, which makes that HeadNeRF is able to generate high fidelity images.
View Synthesis with Sculpted Neural Points
princeton-vl/snp
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We address the task of view synthesis, which can be posed as recovering a rendering function that renders new views from a set of existing images.
Flamingo: a Visual Language Model for Few-Shot Learning
lucidrains/flamingo-pytorch
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DeepMind 2022
Building models that can be rapidly adapted to numerous tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research.
Ranked #1 on
Zero-Shot Learning
on iVQA
DeepDPM: Deep Clustering With an Unknown Number of Clusters
Using a split/merge framework, a dynamic architecture that adapts to the changing K, and a novel loss, our proposed method outperforms existing nonparametric methods (both classical and deep ones).
Ranked #6 on
Unsupervised Image Classification
on ImageNet
VGAER: Graph Neural Network Reconstruction based Community Detection
qcydm/vgaer
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Community detection is a fundamental and important issue in network science, but there are only a few community detection algorithms based on graph neural networks, among which unsupervised algorithms are almost blank.
RITA: a Study on Scaling Up Generative Protein Sequence Models
In this work we introduce RITA: a suite of autoregressive generative models for protein sequences, with up to 1. 2 billion parameters, trained on over 280 million protein sequences belonging to the UniRef-100 database.
