487 citations · 746 across the 15 of their papers we have counts for
10 papers · 1 filter
SLAN: Self-Locator Aided Network for Cross-Modal Understanding
Jiang-Tian Zhai, Qi Zhang, Tong Wu +4
Learning fine-grained interplay between vision and language allows to a more accurate understanding for VisionLanguage tasks. However, it remains challenging to extract key image r…
ClipCrop: Conditioned Cropping Driven by Vision-Language Model
Zhihang Zhong, Mingxi Cheng, Zhirong Wu +7
Image cropping has progressed tremendously under the data-driven paradigm. However, current approaches do not account for the intentions of the user, which is an issue especially w…
Dual Pyramid Generative Adversarial Networks for Semantic Image Synthesis
Shijie Li, Ming-Ming Cheng, Juergen Gall
The goal of semantic image synthesis is to generate photo-realistic images from semantic label maps. It is highly relevant for tasks like content generation and image editing. Curr…
GMA3D: Local-Global Attention Learning to Estimate Occluded Motions of Scene Flow
Zhiyang Lu, Ming Cheng
Scene flow represents the motion information of each point in the 3D point clouds. It is a vital downstream method applied to many tasks, such as motion segmentation and object tra…
SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation
Meng-Hao Guo, Cheng-Ze Lu, Qibin Hou +3
We present SegNeXt, a simple convolutional network architecture for semantic segmentation. Recent transformer-based models have dominated the field of semantic segmentation due to…
Designing An Illumination-Aware Network for Deep Image Relighting
Zuo-Liang Zhu, Zhen Li, Rui-Xun Zhang +2
Lighting is a determining factor in photography that affects the style, expression of emotion, and even quality of images. Creating or finding satisfying lighting conditions, in re…
RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks
Shanghua Gao, Zhong-Yu Li, Qi Han +2
Temporal/spatial receptive fields of models play an important role in sequential/spatial tasks. Large receptive fields facilitate long-term relations, while small receptive fields…
SERE: Exploring Feature Self-relation for Self-supervised Transformer
Zhong-Yu Li, Shanghua Gao, Ming-Ming Cheng
Learning representations with self-supervision for convolutional networks (CNN) has been validated to be effective for vision tasks. As an alternative to CNN, vision transformers (…
Interactive Style Transfer: All is Your Palette
Zheng Lin, Zhao Zhang, Kang-Rui Zhang +2
Neural style transfer (NST) can create impressive artworks by transferring reference style to content image. Current image-to-image NST methods are short of fine-grained controls,…
Visual Attention Network
Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu +2
While originally designed for natural language processing tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of i…