32 citations · 95 across the 11 of their papers we have counts for
11 papers
RegionBLIP: A Unified Multi-modal Pre-training Framework for Holistic and Regional Comprehension
Qiang Zhou, Chaohui Yu, Shaofeng Zhang +3
In this work, we investigate extending the comprehension of Multi-modal Large Language Models (MLLMs) to regional objects. To this end, we propose to extract features corresponding…
AxWin Transformer: A Context-Aware Vision Transformer Backbone with Axial Windows
Fangjian Lin, Yizhe Ma, Sitong Wu +2
Recently Transformer has shown good performance in several vision tasks due to its powerful modeling capabilities. To reduce the quadratic complexity caused by the attention, some…
PRSeg: A Lightweight Patch Rotate MLP Decoder for Semantic Segmentation
Yizhe Ma, Fangjian Lin, Sitong Wu +2
The lightweight MLP-based decoder has become increasingly promising for semantic segmentation. However, the channel-wise MLP cannot expand the receptive fields, lacking the context…
UniNeXt: Exploring A Unified Architecture for Vision Recognition
Fangjian Lin, Jianlong Yuan, Sitong Wu +2
Vision Transformers have shown great potential in computer vision tasks. Most recent works have focused on elaborating the spatial token mixer for performance gains. However, we ob…
Semantic Diffusion Network for Semantic Segmentation
Haoru Tan, Sitong Wu, Jimin Pi
Precise and accurate predictions over boundary areas are essential for semantic segmentation. However, the commonly-used convolutional operators tend to smooth and blur local detai…
Demystify Transformers & Convolutions in Modern Image Deep Networks
Xiaowei Hu, Min Shi, Weiyun Wang +9
Vision transformers have gained popularity recently, leading to the development of new vision backbones with improved features and consistent performance gains. However, these adva…
CATrans: Context and Affinity Transformer for Few-Shot Segmentation
Shan Zhang, Tianyi Wu, Sitong Wu +1
Few-shot segmentation (FSS) aims to segment novel categories given scarce annotated support images. The crux of FSS is how to aggregate dense correlations between support and query…
Feature Selective Transformer for Semantic Image Segmentation
Fangjian Lin, Tianyi Wu, Sitong Wu +2
Recently, it has attracted more and more attentions to fuse multi-scale features for semantic image segmentation. Various works were proposed to employ progressive local or global…
StructToken : Rethinking Semantic Segmentation with Structural Prior
Fangjian Lin, Zhanhao Liang, Sitong Wu +3
In previous deep-learning-based methods, semantic segmentation has been regarded as a static or dynamic per-pixel classification task, \textit{i.e.,} classify each pixel representa…
Pale Transformer: A General Vision Transformer Backbone with Pale-Shaped Attention
Sitong Wu, Tianyi Wu, Haoru Tan +1
Recently, Transformers have shown promising performance in various vision tasks. To reduce the quadratic computation complexity caused by the global self-attention, various methods…
Fully Transformer Networks for Semantic Image Segmentation
Sitong Wu, Tianyi Wu, Fangjian Lin +2
Transformers have shown impressive performance in various natural language processing and computer vision tasks, due to the capability of modeling long-range dependencies. Recent p…