Search Results for author:
Found 8 papers, 5 papers with code
M2-CLIP: A Multimodal, Multi-task Adapting Framework for Video Action Recognition
In this paper, we introduce a novel Multimodal, Multi-task CLIP adapting framework named \name to address these challenges, preserving both high supervised performance and robust transferability.
CR-SFP: Learning Consistent Representation for Soft Filter Pruning
In this paper, we propose to mitigate this gap by learning consistent representation for soft filter pruning, dubbed as CR-SFP.
Camera-based 3D Semantic Scene Completion with Sparse Guidance Network
Semantic scene completion (SSC) aims to predict the semantic occupancy of each voxel in the entire 3D scene from limited observations, which is an emerging and critical task for autonomous driving.
SSC-RS: Elevate LiDAR Semantic Scene Completion with Representation Separation and BEV Fusion
However, how to effectively exploit the relationships between the semantic context in semantic segmentation and geometric structure in scene completion remains under exploration.
PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and Aggregation
Firstly, we propose a non-learning Sparse Instance Proposal (SIP) module with the ``sampling-shifting-grouping" scheme to directly group thing points into instances from the raw point cloud efficiently.
E-NeRV: Expedite Neural Video Representation with Disentangled Spatial-Temporal Context
However, the redundant parameters within the network structure can cause a large model size when scaling up for desirable performance.
Ranked #4 on
Video Reconstruction
on UVG
MaIL: A Unified Mask-Image-Language Trimodal Network for Referring Image Segmentation
Referring image segmentation is a typical multi-modal task, which aims at generating a binary mask for referent described in given language expressions.
Ranked #1 on
Referring Expression Segmentation
on G-Ref test B
TransVOS: Video Object Segmentation with Transformers
Recently, Space-Time Memory Network (STM) based methods have achieved state-of-the-art performance in semi-supervised video object segmentation (VOS).
