108 citations · 201 across the 14 of their papers we have counts for
7 papers · 1 filter
UniRef++: Segment Every Reference Object in Spatial and Temporal Spaces
Jiannan Wu, Yi Jiang, Bin Yan +3
The reference-based object segmentation tasks, namely referring image segmentation (RIS), few-shot image segmentation (FSS), referring video object segmentation (RVOS), and video o…
CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection
Chuofan Ma, Yi Jiang, Xin Wen +2
Deriving reliable region-word alignment from image-text pairs is critical to learn object-level vision-language representations for open-vocabulary object detection. Existing metho…
Exploring Transformers for Open-world Instance Segmentation
Jiannan Wu, Yi Jiang, Bin Yan +3
Open-world instance segmentation is a rising task, which aims to segment all objects in the image by learning from a limited number of base-category objects. This task is challengi…
EGC: Image Generation and Classification via a Diffusion Energy-Based Model
Qiushan Guo, Chuofan Ma, Yi Jiang +3
Learning image classification and image generation using the same set of network parameters is a challenging problem. Recent advanced approaches perform well in one task often exhi…
Multi-Level Contrastive Learning for Dense Prediction Task
Qiushan Guo, Yizhou Yu, Yi Jiang +3
In this work, we present Multi-Level Contrastive Learning for Dense Prediction Task (MCL), an efficient self-supervised method for learning region-level feature representation for…
Universal Instance Perception as Object Discovery and Retrieval
Bin Yan, Yi Jiang, Jiannan Wu +4
All instance perception tasks aim at finding certain objects specified by some queries such as category names, language expressions, and target annotations, but this complete field…
Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling
Keyu Tian, Yi Jiang, Qishuai Diao +3
We identify and overcome two key obstacles in extending the success of BERT-style pre-training, or the masked image modeling, to convolutional networks (convnets): (i) convolution…