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20182024
most citedCrowd Counting and Density Estimation by Trellis Encoder-Decoder Network

78 citations · 192 across the 15 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cs.CV2023★ 2 cited

Controllable Mind Visual Diffusion Model

Bohan Zeng, Shanglin Li, Xuhui Liu +6

Brain signal visualization has emerged as an active research area, serving as a critical interface between the human visual system and computer vision models. Although diffusion mo…

cs.CV2023

MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation

Jie Guo, Qimeng Wang, Yan Gao +4

CLIP (Contrastive Language-Image Pretraining) is well-developed for open-vocabulary zero-shot image-level recognition, while its applications in pixel-level tasks are less investig…

cs.CV2023★ 1 cited

PiClick: Picking the desired mask from multiple candidates in click-based interactive segmentation

Cilin Yan, Haochen Wang, Jie Liu +5

Click-based interactive segmentation aims to generate target masks via human clicking, which facilitates efficient pixel-level annotation and image editing. In such a task, target…

cs.CV2023★ 3 cited

Towards Open-Vocabulary Video Instance Segmentation

Haochen Wang, Cilin Yan, Shuai Wang +5

Video Instance Segmentation (VIS) aims at segmenting and categorizing objects in videos from a closed set of training categories, lacking the generalization ability to handle novel…

cs.CV2023★ 64 cited

Continuous Remote Sensing Image Super-Resolution based on Context Interaction in Implicit Function Space

Keyan Chen, Wenyuan Li, Sen Lei +4

Despite its fruitful applications in remote sensing, image super-resolution is troublesome to train and deploy as it handles different resolution magnifications with separate model…

cs.CV2023★ 5 cited

OvarNet: Towards Open-vocabulary Object Attribute Recognition

Keyan Chen, Xiaolong Jiang, Yao Hu +4

In this paper, we consider the problem of simultaneously detecting objects and inferring their visual attributes in an image, even for those with no manual annotations provided at…