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Found 7 papers, 4 papers with code
DIFFVSGG: Diffusion-Driven Online Video Scene Graph Generation
This embedding then serves as the input to task-specific heads for object classification, scene graph generation, etc.
TGP: Two-modal occupancy prediction with 3D Gaussian and sparse points for 3D Environment Awareness
To address this issue, we propose a dual-modal prediction method based on 3D Gaussian sets and sparse points, which balances both spatial location and volumetric structural information, achieving higher accuracy in semantic occupancy prediction.
UAHOI: Uncertainty-aware Robust Interaction Learning for HOI Detection
Through extensive experiments, we demonstrate that \textsc{UAHOI} achieves significant improvements over existing state-of-the-art methods, enhancing both the accuracy and robustness of HOI detection.
PiPa++: Towards Unification of Domain Adaptive Semantic Segmentation via Self-supervised Learning
Unsupervised domain adaptive segmentation aims to improve the segmentation accuracy of models on target domains without relying on labeled data from those domains.
General and Task-Oriented Video Segmentation
We present GvSeg, a general video segmentation framework for addressing four different video segmentation tasks (i. e., instance, semantic, panoptic, and exemplar-guided) while maintaining an identical architectural design.
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation
Based on such observation, we propose a depth-aware framework to explicitly leverage depth estimation to mix the categories and facilitate the two complementary tasks, i. e., segmentation and depth learning in an end-to-end manner.
PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation
In an attempt to fill this gap, we propose a unified pixel- and patch-wise self-supervised learning framework, called PiPa, for domain adaptive semantic segmentation that facilitates intra-image pixel-wise correlations and patch-wise semantic consistency against different contexts.
Ranked #1 on
Semantic Segmentation
on SYNTHIA-to-Cityscapes
