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Found 6 papers, 1 papers with code
MT3DNet: Multi-Task learning Network for 3D Surgical Scene Reconstruction
In image-assisted minimally invasive surgeries (MIS), understanding surgical scenes is vital for real-time feedback to surgeons, skill evaluation, and improving outcomes through collaborative human-robot procedures.
Appearance Matching Adapter for Exemplar-based Semantic Image Synthesis
Recent tuning-free approaches address this limitation by transferring local appearance from the exemplar image to the synthesized image through implicit cross-image matching in the augmented self-attention mechanism of pre-trained diffusion models.
MoDiTalker: Motion-Disentangled Diffusion Model for High-Fidelity Talking Head Generation
AToM excels in capturing subtle lip movements by leveraging an audio attention mechanism.
Expand-and-Quantize: Unsupervised Semantic Segmentation Using High-Dimensional Space and Product Quantization
In this paper, we propose a novel USS framework called Expand-and-Quantize Unsupervised Semantic Segmentation (EQUSS), which combines the benefits of high-dimensional spaces for better clustering and product quantization for effective information compression.
Ranked #4 on
Unsupervised Semantic Segmentation
on Potsdam-3
Depth-Relative Self Attention for Monocular Depth Estimation
Monocular depth estimation is very challenging because clues to the exact depth are incomplete in a single RGB image.
Towards Intelligent Millimeter and Terahertz Communication for 6G: Computer Vision-aided Beamforming
Beamforming technique realized by the multiple-input-multiple-output (MIMO) antenna arrays has been widely used to compensate for the severe path loss in the millimeter wave (mmWave) bands.
