175 citations · 367 across the 10 of their papers we have counts for
10 papers
InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition
Pan Zhang, Xiaoyi Dong, Bin Wang +18
We propose InternLM-XComposer, a vision-language large model that enables advanced image-text comprehension and composition. The innovative nature of our model is highlighted by th…
OCBEV: Object-Centric BEV Transformer for Multi-View 3D Object Detection
Zhangyang Qi, Jiaqi Wang, Xiaoyang Wu +1
Multi-view 3D object detection is becoming popular in autonomous driving due to its high effectiveness and low cost. Most of the current state-of-the-art detectors follow the query…
Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant
Ying Jin, Jiaqi Wang, Dahua Lin
Semi-Supervised Semantic Segmentation aims at training the segmentation model with limited labeled data and a large amount of unlabeled data. To effectively leverage the unlabeled…
What Are Expected Queries in End-to-End Object Detection?
Shilong Zhang, Xinjiang Wang, Jiaqi Wang +2
End-to-end object detection is rapidly progressed after the emergence of DETR. DETRs use a set of sparse queries that replace the dense candidate boxes in most traditional detector…
MINI: Mining Implicit Novel Instances for Few-Shot Object Detection
Yuhang Cao, Jiaqi Wang, Yiqi Lin +1
Learning from a few training samples is a desirable ability of an object detector, inspiring the explorations of Few-Shot Object Detection (FSOD). Most existing approaches employ a…
LAVT: Language-Aware Vision Transformer for Referring Image Segmentation
Zhao Yang, Jiaqi Wang, Yansong Tang +3
Referring image segmentation is a fundamental vision-language task that aims to segment out an object referred to by a natural language expression from an image. One of the key cha…
CARAFE++: Unified Content-Aware ReAssembly of FEatures
Jiaqi Wang, Kai Chen, Rui Xu +3
Feature reassembly, i.e. feature downsampling and upsampling, is a key operation in a number of modern convolutional network architectures, e.g., residual networks and feature pyra…
Texture Memory-Augmented Deep Patch-Based Image Inpainting
Rui Xu, Minghao Guo, Jiaqi Wang +3
Patch-based methods and deep networks have been employed to tackle image inpainting problem, with their own strengths and weaknesses. Patch-based methods are capable of restoring a…
CARAFE: Content-Aware ReAssembly of FEatures
Jiaqi Wang, Kai Chen, Rui Xu +3
Feature upsampling is a key operation in a number of modern convolutional network architectures, e.g. feature pyramids. Its design is critical for dense prediction tasks such as ob…
Hybrid Task Cascade for Instance Segmentation
Kai Chen, Jiangmiao Pang, Jiaqi Wang +9
Cascade is a classic yet powerful architecture that has boosted performance on various tasks. However, how to introduce cascade to instance segmentation remains an open question. A…