Search Results for author:
Found 15 papers, 6 papers with code
Soft Masked Transformer for Point Cloud Processing with Skip Attention-Based Upsampling
This strategy allows various transformer blocks to share the same position information over the same resolution points, thereby reducing network parameters and training time without compromising accuracy. Experimental comparisons with existing methods on multiple datasets demonstrate the efficacy of SMTransformer and skip-attention-based up-sampling for point cloud processing tasks, including semantic segmentation and classification.
OST: Refining Text Knowledge with Optimal Spatio-Temporal Descriptor for General Video Recognition
Due to the resource-intensive nature of training vision-language models on expansive video data, a majority of studies have centered on adapting pre-trained image-language models to the video domain.
Ranked #2 on
Zero-Shot Action Recognition
on Kinetics
First Place Solution to the CVPR'2023 AQTC Challenge: A Function-Interaction Centric Approach with Spatiotemporal Visual-Language Alignment
Affordance-Centric Question-driven Task Completion (AQTC) has been proposed to acquire knowledge from videos to furnish users with comprehensive and systematic instructions.
DANet: Density Adaptive Convolutional Network with Interactive Attention for 3D Point Clouds
Local features and contextual dependencies are crucial for 3D point cloud analysis.
Full Point Encoding for Local Feature Aggregation in 3D Point Clouds
In particular, we achieve state-of-the-art semantic segmentation results of 76% mIoU on S3DIS 6-fold and 72. 2% on S3DIS Area5.
AShapeFormer: Semantics-Guided Object-Level Active Shape Encoding for 3D Object Detection via Transformers
In this work, we propose AShapeFormer, a semantics-guided object-level shape encoding module for 3D object detection.
Domain-invariant Prototypes for Semantic Segmentation
In this paper, we present an easy-to-train framework that learns domain-invariant prototypes for domain adaptive semantic segmentation.
Learning from Pixel-Level Noisy Label : A New Perspective for Light Field Saliency Detection
Saliency detection with light field images is becoming attractive given the abundant cues available, however, this comes at the expense of large-scale pixel level annotated data which is expensive to generate.
Sparse Optical Flow-Based Line Feature Tracking
To achieve this goal, an efficient two endpoint tracking (TET) method is presented: first, describe a given line feature with its two endpoints; next, track the two endpoints based on SOF to obtain two new tracked endpoints by minimizing a pixel-level grayscale residual function; finally, connect the two tracked endpoints to generate a new line feature.
Learning From Pixel-Level Noisy Label: A New Perspective for Light Field Saliency Detection
Saliency detection with light field images is becoming attractive given the abundant cues available, however, this comes at the expense of large-scale pixel level annotated data which is expensive to generate.
Deep Learning Based 3D Segmentation: A Survey
This paper fills the gap and provides a comprehensive survey of the recent progress made in deep learning based 3D segmentation.
Self-supervised Learning with Fully Convolutional Networks
By learning to solve a Jigsaw Puzzle problem with 25 patches and transferring the learned features to semantic segmentation task on Cityscapes dataset, we achieve a 5. 8 percentage point improvement over the baseline model that initialized from random values.
PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line Features
This paper presents PL-VINS, a real-time optimization-based monocular VINS method with point and line features, developed based on the state-of-the-art point-based VINS-Mono \cite{vins}.
Fast ORB-SLAM without Keypoint Descriptors
ORB-SLAM2 \cite{orbslam2} is a benchmark method in this domain, however, it consumes significant time for computing descriptors that never get reused unless a frame is selected as a keyframe.
Robotics Computational Geometry I.4.0; I.4.9
Real time backbone for semantic segmentation
The rapid development of autonomous driving in recent years presents lots of challenges for scene understanding.
