829 citations · 4.3k across the 90 of their papers we have counts for
11 papers · 2 filters
Clicking Matters:Towards Interactive Human Parsing
Yutong Gao, Liqian Liang, Congyan Lang +3
In this work, we focus on Interactive Human Parsing (IHP), which aims to segment a human image into multiple human body parts with guidance from users' interactions. This new task…
M5Product: Self-harmonized Contrastive Learning for E-commercial Multi-modal Pretraining
Xiao Dong, Xunlin Zhan, Yangxin Wu +6
Despite the potential of multi-modal pre-training to learn highly discriminative feature representations from complementary data modalities, current progress is being slowed by the…
Product1M: Towards Weakly Supervised Instance-Level Product Retrieval via Cross-modal Pretraining
Xunlin Zhan, Yangxin Wu, Xiao Dong +5
Nowadays, customer's demands for E-commerce are more diversified, which introduces more complications to the product retrieval industry. Previous methods are either subject to sing…
Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentation
Bingfeng Zhang, Jimin Xiao, Jianbo Jiao +2
Weakly supervised semantic segmentation is receiving great attention due to its low human annotation cost. In this paper, we aim to tackle bounding box supervised semantic segmenta…
ReGO: Reference-Guided Outpainting for Scenery Image
Yaxiong Wang, Yunchao Wei, Xueming Qian +2
We aim to tackle the challenging yet practical scenery image outpainting task in this work. Recently, generative adversarial learning has significantly advanced the image outpainti…
Associating Objects with Transformers for Video Object Segmentation
Zongxin Yang, Yunchao Wei, Yi Yang
This paper investigates how to realize better and more efficient embedding learning to tackle the semi-supervised video object segmentation under challenging multi-object scenarios…
Few-Shot Segmentation via Cycle-Consistent Transformer
Gengwei Zhang, Guoliang Kang, Yi Yang +1
Few-shot segmentation aims to train a segmentation model that can fast adapt to novel classes with few exemplars. The conventional training paradigm is to learn to make predictions…
Rethinking Cross-modal Interaction from a Top-down Perspective for Referring Video Object Segmentation
Chen Liang, Yu Wu, Tianfei Zhou +4
Referring video object segmentation (RVOS) aims to segment video objects with the guidance of natural language reference. Previous methods typically tackle RVOS through directly gr…
Cross-Modal Progressive Comprehension for Referring Segmentation
Si Liu, Tianrui Hui, Shaofei Huang +3
Given a natural language expression and an image/video, the goal of referring segmentation is to produce the pixel-level masks of the entities described by the subject of the expre…
Decoupled Spatial Temporal Graphs for Generic Visual Grounding
Qianyu Feng, Yunchao Wei, Mingming Cheng +1
Visual grounding is a long-lasting problem in vision-language understanding due to its diversity and complexity. Current practices concentrate mostly on performing visual grounding…
AINet+: Advancing Superpixel Segmentation via Cascaded Association Implantation
Yaxiong Wang, Yunchao Wei, Yujiao Wu +3
Superpixel segmentation has seen significant progress benefiting from the deep convolutional networks. The typical approach entails initial division of the image into grids, follow…