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Found 8 papers, 3 papers with code
Beyond MOT: Semantic Multi-Object Tracking
Current multi-object tracking (MOT) aims to predict trajectories of targets (i. e.,"where") in videos.
VastTrack: Vast Category Visual Object Tracking
The rich annotations of VastTrack enables development of both the vision-only and the vision-language tracking.
Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning
Existing approaches often fully fine-tune a dual-branch encoder-decoder framework with a complicated feature fusion strategy for achieving multimodal semantic segmentation, which is training-costly due to the massive parameter updates in feature extraction and fusion.
Ranked #2 on
Semantic Segmentation
on SUN-RGBD
(using extra training data)
CACFNet: Cross-Modal Attention Cascaded Fusion Network for RGB-T Urban Scene Parsing
Color–thermal (RGB-T) urban scene parsing has recently attracted widespread interest.
Ranked #5 on
Thermal Image Segmentation
on PST900
EGFNet: Edge-Aware Guidance Fusion Network for RGB–Thermal Urban Scene Parsing
To address these problems, an edge-aware guidance fusion network (EGFNet) was developed in this study for RGB–thermal urban scene parsing.
Ranked #6 on
Thermal Image Segmentation
on PST900
GEBNet: Graph-Enhancement Branch Network for RGB-T Scene Parsing
RGB-T (red–green–blue and thermal) scene parsing has recently drawn considerable research attention.
Ranked #9 on
Thermal Image Segmentation
on PST900
MTANet: Multitask-Aware Network With Hierarchical Multimodal Fusion for RGB-T Urban Scene Understanding
To improve the fusion of multimodal features and the segmentation accuracy, we propose a multitask-aware network (MTANet) with hierarchical multimodal fusion (multiscale fusion strategy) for RGB-T urban scene understanding.
Ranked #10 on
Thermal Image Segmentation
on PST900
Edge-aware Guidance Fusion Network for RGB Thermal Scene Parsing
Considering the importance of high level semantic information, we propose a global information module and a semantic information module to extract rich semantic information from the high-level features.
Ranked #11 on
Thermal Image Segmentation
on PST900
