Search Results for author:
Found 13 papers, 6 papers with code
View-Dependent Octree-based Mesh Extraction in Unbounded Scenes for Procedural Synthetic Data
Procedural synthetic data generation has received increasing attention in computer vision.
Infinite Photorealistic Worlds using Procedural Generation
We introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural world.
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification
Although graph neural networks (GNNs) have achieved impressive achievements in graph classification, they often need abundant task-specific labels, which could be extensively costly to acquire.
Inverse Compositional Learning for Weakly-supervised Relation Grounding
In this study, we introduce a novel approach called inverse compositional learning (ICL) for weakly-supervised video relation grounding.
Semantic Enhanced Knowledge Graph for Large-Scale Zero-Shot Learning
In this paper, we provide a new semantic enhanced knowledge graph that contains both expert knowledge and categories semantic correlation.
Learning Modal-Invariant and Temporal-Memory for Video-based Visible-Infrared Person Re-Identification
Based on our constructed dataset, we prove that with the increase of frames in a tracklet, the performance does meet more enhancement, demonstrating the significance of video-to-video matching in RGB-IR person Re-ID.
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
Multiview Stereo with Cascaded Epipolar RAFT
CER-MVS is significantly different from prior work in multiview stereo.
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation
Recent progress in generative language models has enabled machines to generate astonishingly realistic texts.
ARGO: Modeling Heterogeneity in E-commerce Recommendation
Nowadays, E-commerce is increasingly integrated into our daily lives.
Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation
Accurate automatic liver and tumor segmentation plays a vital role in treatment planning and disease monitoring.
Automatic Liver And Tumor Segmentation
Image Segmentation
+3
Deep Unsupervised Hashing by Distilled Smooth Guidance
Hashing has been widely used in approximate nearest neighbor search for its storage and computational efficiency.
CIMON: Towards High-quality Hash Codes
However, due to the inefficient representation ability of the pre-trained model, many false positives and negatives in local semantic similarity will be introduced and lead to error propagation during the hash code learning.
