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Found 11 papers, 3 papers with code
Frozen Transformers in Language Models Are Effective Visual Encoder Layers
This paper reveals that large language models (LLMs), despite being trained solely on textual data, are surprisingly strong encoders for purely visual tasks in the absence of language.
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DualCross: Cross-Modality Cross-Domain Adaptation for Monocular BEV Perception
In this work, we propose DualCross, a cross-modality cross-domain adaptation framework to facilitate the learning of a more robust monocular bird's-eye-view (BEV) perception model, which transfers the point cloud knowledge from a LiDAR sensor in one domain during the training phase to the camera-only testing scenario in a different domain.
BEV-Guided Multi-Modality Fusion for Driving Perception
We design a BEV-guided multi-sensor attention block to take queries from BEV embeddings and learn the BEV representation from sensor-specific features.
Fast Graph Neural Tangent Kernel via Kronecker Sketching
Given a kernel matrix of $n$ graphs, using sketching in solving kernel regression can reduce the running time to $o(n^3)$.
Multi-Echo LiDAR for 3D Object Detection
LiDAR sensors can be used to obtain a wide range of measurement signals other than a simple 3D point cloud, and those signals can be leveraged to improve perception tasks like 3D object detection.
Multi-Modality Task Cascade for 3D Object Detection
To provide a more integrated approach, we propose a novel Multi-Modality Task Cascade network (MTC-RCNN) that leverages 3D box proposals to improve 2D segmentation predictions, which are then used to further refine the 3D boxes.
Graph Neural Network Acceleration via Matrix Dimension Reduction
Theoretically, we present two techniques to speed up GNTK training while preserving the generalization error: (1) We use a novel matrix decoupling method to reduce matrix dimensions during the kernel solving.
Graph Neural Networks for 3D Multi-Object Tracking
3D Multi-object tracking (MOT) is crucial to autonomous systems.
GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning
As a result, the feature of one object is informed of the features of other objects so that the object feature can lean towards the object with similar feature (i. e., object probably with a same ID) and deviate from objects with dissimilar features (i. e., object probably with different IDs), leading to a more discriminative feature for each object; (2) instead of obtaining the feature from either 2D or 3D space in prior work, we propose a novel joint feature extractor to learn appearance and motion features from 2D and 3D space simultaneously.
Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Net
Segmentation of pancreas is important for medical image analysis, yet it faces great challenges of class imbalance, background distractions and non-rigid geometrical features.
GroundNet: Monocular Ground Plane Normal Estimation with Geometric Consistency
We focus on estimating the 3D orientation of the ground plane from a single image.
