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Found 13 papers, 2 papers with code
XR-VIO: High-precision Visual Inertial Odometry with Fast Initialization for XR Applications
In terms of feature matching, we introduce a hybrid method that combines optical flow and descriptor-based matching.
StarGen: A Spatiotemporal Autoregression Framework with Video Diffusion Model for Scalable and Controllable Scene Generation
Recent advances in large reconstruction and generative models have significantly improved scene reconstruction and novel view generation.
WeKnow-RAG: An Adaptive Approach for Retrieval-Augmented Generation Integrating Web Search and Knowledge Graphs
First, the accuracy and reliability of LLM responses are improved by combining the structured representation of Knowledge Graphs with the flexibility of dense vector retrieval.
PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction
Experiments on indoor and outdoor scenes show that our method achieves fast training and rendering while maintaining high-fidelity rendering and geometric reconstruction, outperforming 3DGS-based and NeRF-based methods.
Depth Completion with Multiple Balanced Bases and Confidence for Dense Monocular SLAM
In this paper, we propose a novel method that integrates a light-weight depth completion network into a sparse SLAM system using a multi-basis depth representation, so that dense mapping can be performed online even on a mobile phone.
VIP-SLAM: An Efficient Tightly-Coupled RGB-D Visual Inertial Planar SLAM
Even if the plane parameters are involved in the optimization, we effectively simplify the back-end map by using planar structures.
Procedural Text Understanding via Scene-Wise Evolution
In this paper, we propose a new \textbf{scene-wise} paradigm for procedural text understanding, which jointly tracks states of all entities in a scene-by-scene manner.
From Discourse to Narrative: Knowledge Projection for Event Relation Extraction
Current event-centric knowledge graphs highly rely on explicit connectives to mine relations between events.
CLUE: A Chinese Language Understanding Evaluation Benchmark
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks.
Multi-task Sentence Encoding Model for Semantic Retrieval in Question Answering Systems
Sentence matching is an essential task in the QA systems and is usually reformulated as a Paraphrase Identification (PI) problem.
Ranked #13 on
Paraphrase Identification
on Quora Question Pairs
(Accuracy metric)
