512 citations · 1.4k across the 23 of their papers we have counts for
24 papers · 1 filter
V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detection
Yichao Shen, Zigang Geng, Yuhui Yuan +6
We introduce a highly performant 3D object detector for point clouds using the DETR framework. The prior attempts all end up with suboptimal results because they fail to learn accu…
Mask Frozen-DETR: High Quality Instance Segmentation with One GPU
Zhanhao Liang, Yuhui Yuan
In this paper, we aim to study how to build a strong instance segmenter with minimal training time and GPUs, as opposed to the majority of current approaches that pursue more accur…
DETR Doesn't Need Multi-Scale or Locality Design
Yutong Lin, Yuhui Yuan, Zheng Zhang +3
This paper presents an improved DETR detector that maintains a "plain" nature: using a single-scale feature map and global cross-attention calculations without specific locality co…
Exploring Predicate Visual Context in Detecting Human-Object Interactions
Frederic Z. Zhang, Yuhui Yuan, Dylan Campbell +2
Recently, the DETR framework has emerged as the dominant approach for human--object interaction (HOI) research. In particular, two-stage transformer-based HOI detectors are amongst…
Revisiting DETR Pre-training for Object Detection
Yan Ma, Weicong Liang, Bohan Chen +5
Motivated by the remarkable achievements of DETR-based approaches on COCO object detection and segmentation benchmarks, recent endeavors have been directed towards elevating their…
LISA: Reasoning Segmentation via Large Language Model
Xin Lai, Zhuotao Tian, Yukang Chen +4
Although perception systems have made remarkable advancements in recent years, they still rely on explicit human instruction or pre-defined categories to identify the target object…
Space Engage: Collaborative Space Supervision for Contrastive-based Semi-Supervised Semantic Segmentation
Changqi Wang, Haoyu Xie, Yuhui Yuan +2
Semi-Supervised Semantic Segmentation (S4) aims to train a segmentation model with limited labeled images and a substantial volume of unlabeled images. To improve the robustness of…
detrex: Benchmarking Detection Transformers
Tianhe Ren, Shilong Liu, Feng Li +13
The DEtection TRansformer (DETR) algorithm has received considerable attention in the research community and is gradually emerging as a mainstream approach for object detection and…
GlyphControl: Glyph Conditional Control for Visual Text Generation
Yukang Yang, Dongnan Gui, Yuhui Yuan +4
Recently, there has been an increasing interest in developing diffusion-based text-to-image generative models capable of generating coherent and well-formed visual text. In this pa…
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…
Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning
Weicong Liang, Yuhui Yuan, Henghui Ding +6
Vision transformers have recently achieved competitive results across various vision tasks but still suffer from heavy computation costs when processing a large number of tokens. M…
DETRs with Hybrid Matching
Ding Jia, Yuhui Yuan, Haodi He +6
One-to-one set matching is a key design for DETR to establish its end-to-end capability, so that object detection does not require a hand-crafted NMS (non-maximum suppression) to r…
Region Rebalance for Long-Tailed Semantic Segmentation
Jiequan Cui, Yuhui Yuan, Zhisheng Zhong +4
In this paper, we study the problem of class imbalance in semantic segmentation. We first investigate and identify the main challenges of addressing this issue through pixel rebala…
RankSeg: Adaptive Pixel Classification with Image Category Ranking for Segmentation
Haodi He, Yuhui Yuan, Xiangyu Yue +1
The segmentation task has traditionally been formulated as a complete-label pixel classification task to predict a class for each pixel from a fixed number of predefined semantic c…
HRFormer: High-Resolution Transformer for Dense Prediction
Yuhui Yuan, Rao Fu, Lang Huang +4
We present a High-Resolution Transformer (HRFormer) that learns high-resolution representations for dense prediction tasks, in contrast to the original Vision Transformer that prod…
Conditional DETR for Fast Training Convergence
Depu Meng, Xiaokang Chen, Zejia Fan +5
The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the c…
Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision
Xiaokang Chen, Yuhui Yuan, Gang Zeng +1
In this paper, we study the semi-supervised semantic segmentation problem via exploring both labeled data and extra unlabeled data. We propose a novel consistency regularization ap…
SegFix: Model-Agnostic Boundary Refinement for Segmentation
Yuhui Yuan, Jingyi Xie, Xilin Chen +1
We present a model-agnostic post-processing scheme to improve the boundary quality for the segmentation result that is generated by any existing segmentation model. Motivated by th…
Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification
Jianyuan Guo, Yuhui Yuan, Lang Huang +3
Person re-identification is a challenging task due to various complex factors. Recent studies have attempted to integrate human parsing results or externally defined attributes to…
Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation
Yuhui Yuan, Xiaokang Chen, Xilin Chen +1
In this paper, we address the semantic segmentation problem with a focus on the context aggregation strategy. Our motivation is that the label of a pixel is the category of the obj…
Interlaced Sparse Self-Attention for Semantic Segmentation
Lang Huang, Yuhui Yuan, Jianyuan Guo +3
In this paper, we present a so-called interlaced sparse self-attention approach to improve the efficiency of the \emph{self-attention} mechanism for semantic segmentation. The main…
OCNet: Object Context Network for Scene Parsing
Yuhui Yuan, Lang Huang, Jianyuan Guo +3
In this paper, we address the semantic segmentation task with a new context aggregation scheme named \emph{object context}, which focuses on enhancing the role of object informatio…
Feature Incay for Representation Regularization
Yuhui Yuan, Kuiyuan Yang, Chao Zhang
Softmax loss is widely used in deep neural networks for multi-class classification, where each class is represented by a weight vector, a sample is represented as a feature vector,…
Hard-Aware Deeply Cascaded Embedding
Yuhui Yuan, Kuiyuan Yang, Chao Zhang
Riding on the waves of deep neural networks, deep metric learning has also achieved promising results in various tasks using triplet network or Siamese network. Though the basic go…