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20162026
most citedMVSplat: Efficient 3D Gaussian Splatting from Sparse Multi-View Images

167 citations · 471 across the 83 of their papers we have counts for

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Showing 2023 · cs.CVShow all

8 papers · 2 filters

cs.CV2023★ 4 cited

Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction

Zeshuai Deng, Zhuokun Chen, Shuaicheng Niu +3

Image super-resolution (SR) aims to learn a mapping from low-resolution (LR) to high-resolution (HR) using paired HR-LR training images. Conventional SR methods typically gather th…

cs.CV2023★ 4 cited

Mask Propagation for Efficient Video Semantic Segmentation

Yuetian Weng, Mingfei Han, Haoyu He +4

Video Semantic Segmentation (VSS) involves assigning a semantic label to each pixel in a video sequence. Prior work in this field has demonstrated promising results by extending im…

cs.CV2023★ 3 cited

Object-aware Inversion and Reassembly for Image Editing

Zhen Yang, Ganggui Ding, Wen Wang +3

By comparing the original and target prompts, we can obtain numerous editing pairs, each comprising an object and its corresponding editing target. To allow editability while maint…

cs.CV2023★ 4 cited

EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models

Yefei He, Jing Liu, Weijia Wu +2

Diffusion models have demonstrated remarkable capabilities in image synthesis and related generative tasks. Nevertheless, their practicality for real-world applications is constrai…

cs.CV2023★ 3 cited

SwitchGPT: Adapting Large Language Models for Non-Text Outputs

Xinyu Wang, Bohan Zhuang, Qi Wu

Large Language Models (LLMs), primarily trained on text-based datasets, exhibit exceptional proficiencies in understanding and executing complex linguistic instructions via text ou…

cs.CV2023

Stitched ViTs are Flexible Vision Backbones

Zizheng Pan, Jing Liu, Haoyu He +2

Large pretrained plain vision Transformers (ViTs) have been the workhorse for many downstream tasks. However, existing works utilizing off-the-shelf ViTs are inefficient in terms o…