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Found 7 papers, 3 papers with code
Symphonize 3D Semantic Scene Completion with Contextual Instance Queries
`3D Semantic Scene Completion (SSC) has emerged as a nascent and pivotal undertaking in autonomous driving, aiming to predict voxel occupancy within volumetric scenes.
Ranked #1 on
3D Semantic Scene Completion
on KITTI-360
3D Semantic Scene Completion from a single RGB image
Autonomous Driving
InsPro: Propagating Instance Query and Proposal for Online Video Instance Segmentation
When using such a pair to predict an object instance on the current frame, not only the generated instance is automatically associated with its precursors on previous frames, but the model gets a good prior for predicting the same object.
Learning Disentangled Label Representations for Multi-label Classification
The specificity of the framework lies in a feature disentangle module, which contains learnable semantic queries and a Semantic Spatial Cross-Attention (SSCA) module.
QueryProp: Object Query Propagation for High-Performance Video Object Detection
The proposed QueryProp contains two propagation strategies: 1) query propagation is performed from sparse key frames to dense non-key frames to reduce the redundant computation on non-key frames; 2) query propagation is performed from previous key frames to the current key frame to improve feature representation by temporal context modeling.
PanopticDepth: A Unified Framework for Depth-aware Panoptic Segmentation
To overcome these limitations, we propose a unified framework for the DPS task by applying a dynamic convolution technique to both the PS and depth prediction tasks.
Learning Category- and Instance-Aware Pixel Embedding for Fast Panoptic Segmentation
Panoptic segmentation (PS) is a complex scene understanding task that requires providing high-quality segmentation for both thing objects and stuff regions.
SSAP: Single-Shot Instance Segmentation With Affinity Pyramid
Moreover, incorporating with the learned affinity pyramid, a novel cascaded graph partition module is presented to sequentially generate instances from coarse to fine.
