8 accepted papers
Monocular dense SLAM faces significant challenges in low-texture environments and under rapid camera motions. The recent development of 3D Gaussian Splatting (3DGS) offers a promising approach for real-time dense 3D reconstruction. However, existing 3DGS-based SLAM systems employ end-to-end optimiza…
While Vision-Language Models (VLMs) excel at semantic understanding, they struggle to comprehend 3D spatial relationships from limited views. Their reliance on implicit geometric encoding often leads to severe hallucinations and inconsistencies in spatial reasoning tasks. To address this, we introdu
In Multimodal Sentiment Analysis (MSA), data noise arising from various sources can lead to uncertainty in Aleatoric Uncertainty (AU), significantly impacting model performance. Current efforts to address AU have insufficiently explored its sources. They primarily focus on modeling noise rather than
Monocular dense SLAM faces significant challenges in low-texture environments and under rapid camera motions. The recent development of 3D Gaussian Splatting (3DGS) offers a promising approach for real-time dense 3D reconstruction. However, existing 3DGS-based SLAM systems employ end-to-end optimiza
Recently, the mutual combination of Neural Radiance Fields (NeRF) and Segment Anything Model (SAM) in 3D semantic segmentation has achieved impressive results. However, they face the challenge of accurately and consistently segmenting objects in complex scenarios. To address this issue, we introduce…
Unsupervised Domain Adaptation for image classification aims to adapt models trained on a labeled source domain to an unlabeled target domain, improving target domain classification performance. However, previous UDA classification researches tend to assume the two domain distributions after domain…
Unsupervised domain adaptation (UDA) aims to transfer knowledge from the labeled source domain to the fully-unlabeled target domain, thus improving the classification performance of the target domain. Recently, self-training has shown its effectiveness on UDA. However, the feature space for generati…
Unsupervised domain adaptation aims to transfer the knowledge learned from the labeled source domain to the unlabeled target domain, thereby improving the classification performance of the target domain. Recent methods use contrastive learning to optimize this task, however, these methods only focus…