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Found 15 papers, 2 papers with code
PPAD: Iterative Interactions of Prediction and Planning for End-to-end Autonomous Driving
Unlike existing end-to-end autonomous driving frameworks, PPAD models the interactions among ego, agents, and the dynamic environment in an autoregressive manner by interleaving the Prediction and Planning processes at every timestep, instead of a single sequential process of prediction followed by planning.
SVQNet: Sparse Voxel-Adjacent Query Network for 4D Spatio-Temporal LiDAR Semantic Segmentation
To take full advantage of the historical frames high-efficiently, we shunt the historical points into two groups with reference to the current points.
From One to Many: Dynamic Cross Attention Networks for LiDAR and Camera Fusion
The whole fusion architecture named Dynamic Cross Attention Network (DCAN) exploits multi-level image features and adapts to multiple representations of point clouds, which allows DCA to serve as a plug-in fusion module.
Sparse Cross-scale Attention Network for Efficient LiDAR Panoptic Segmentation
Two major challenges of 3D LiDAR Panoptic Segmentation (PS) are that point clouds of an object are surface-aggregated and thus hard to model the long-range dependency especially for large instances, and that objects are too close to separate each other.
DRINet++: Efficient Voxel-as-point Point Cloud Segmentation
The Sparse Feature Encoder extracts the local context information for each point, and the Sparse Geometry Feature Enhancement enhances the geometric properties of a sparse point cloud via multi-scale sparse projection and attentive multi-scale fusion.
DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation
By utilizing these two modules iteratively, features can be propagated between two different representations.
Coarse to Fine: Domain Adaptive Crowd Counting via Adversarial Scoring Network
In specific, at the coarse-grained stage, we design a dual-discriminator strategy to adapt source domain to be close to the targets from the perspectives of both global and local feature space via adversarial learning.
Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation
To capture temporal information from previous frames, we use a memory network to refine the mask of current frame by retrieving historic masks in a temporal graph.
Crowd Counting via Hierarchical Scale Recalibration Network
Extensive experiments on crowd counting datasets (ShanghaiTech, MALL, WorldEXPO'10, and UCSD) show that our HSRNet can deliver superior results over all state-of-the-art approaches.
HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection
We present a Hybrid Voxel network that solves this problem by fusing voxel feature encoder (VFE) of different scales at point-wise level and project into multiple pseudo-image feature maps.
Dynamic Graph Correlation Learning for Disease Diagnosis with Incomplete Labels
In this work, we propose a Disease Diagnosis Graph Convolutional Network (DD-GCN) that presents a novel view of investigating the inter-dependency among different diseases by using a dynamic learnable adjacency matrix in graph structure to improve the diagnosis accuracy.
Tell-the-difference: Fine-grained Visual Descriptor via a Discriminating Referee
In this paper, we investigate a novel problem of telling the difference between image pairs in natural language.
MHP-VOS: Multiple Hypotheses Propagation for Video Object Segmentation
Extensive experiments on challenging datasets demonstrate the effectiveness of the proposed method, especially in the case of object missing.
Spatial-Temporal Synergic Residual Learning for Video Person Re-Identification
We present a novel method - Spatial-Temporal Synergic Residual Network (STSRN) for this problem.
Jointly Attentive Spatial-Temporal Pooling Networks for Video-based Person Re-Identification
Person Re-Identification (person re-id) is a crucial task as its applications in visual surveillance and human-computer interaction.
