26 citations · 75 across the 13 of their papers we have counts for
13 papers
Unified Frequency-Assisted Transformer Framework for Detecting and Grounding Multi-Modal Manipulation
Huan Liu, Zichang Tan, Qiang Chen +3
Detecting and grounding multi-modal media manipulation (DGM^4) has become increasingly crucial due to the widespread dissemination of face forgery and text misinformation. In this…
Enhancing Your Trained DETRs with Box Refinement
Yiqun Chen, Qiang Chen, Peize Sun +3
We present a conceptually simple, efficient, and general framework for localization problems in DETR-like models. We add plugins to well-trained models instead of inefficiently des…
LSAS: Lightweight Sub-attention Strategy for Alleviating Attention Bias Problem
Shanshan Zhong, Wushao Wen, Jinghui Qin +2
In computer vision, the performance of deep neural networks (DNNs) is highly related to the feature extraction ability, i.e., the ability to recognize and focus on key pixel region…
DATE: Dual Assignment for End-to-End Fully Convolutional Object Detection
Yiqun Chen, Qiang Chen, Qinghao Hu +1
Fully convolutional detectors discard the one-to-many assignment and adopt a one-to-one assigning strategy to achieve end-to-end detection but suffer from the slow convergence issu…
SelfMix: Robust Learning Against Textual Label Noise with Self-Mixup Training
Dan Qiao, Chenchen Dai, Yuyang Ding +4
The conventional success of textual classification relies on annotated data, and the new paradigm of pre-trained language models (PLMs) still requires a few labeled data for downst…
Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning
Yanpeng Sun, Qiang Chen, Xiangyu He +7
Freezing the pre-trained backbone has become a standard paradigm to avoid overfitting in few-shot segmentation. In this paper, we rethink the paradigm and explore a new regime: {\e…
MixFormer: Mixing Features across Windows and Dimensions
Qiang Chen, Qiman Wu, Jian Wang +5
While local-window self-attention performs notably in vision tasks, it suffers from limited receptive field and weak modeling capability issues. This is mainly because it performs…
Improving Transferability for Domain Adaptive Detection Transformers
Kaixiong Gong, Shuang Li, Shugang Li +3
DETR-style detectors stand out amongst in-domain scenarios, but their properties in domain shift settings are under-explored. This paper aims to build a simple but effective baseli…
HIH: Towards More Accurate Face Alignment via Heatmap in Heatmap
Xing Lan, Qinghao Hu, Qiang Chen +2
Heatmap-based regression overcomes the lack of spatial and contextual information of direct coordinate regression, and has revolutionized the task of face alignment. Yet it suffers…
You Only Look One-level Feature
Qiang Chen, Yingming Wang, Tong Yang +3
This paper revisits feature pyramids networks (FPN) for one-stage detectors and points out that the success of FPN is due to its divide-and-conquer solution to the optimization pro…
Location-aware Upsampling for Semantic Segmentation
Xiangyu He, Zitao Mo, Qiang Chen +3
Many successful learning targets such as minimizing dice loss and cross-entropy loss have enabled unprecedented breakthroughs in segmentation tasks. Beyond these semantic metrics,…
SpatialFlow: Bridging All Tasks for Panoptic Segmentation
Qiang Chen, Anda Cheng, Xiangyu He +2
Object location is fundamental to panoptic segmentation as it is related to all things and stuff in the image scene. Knowing the locations of objects in the image provides clues fo…
Compact Global Descriptor for Neural Networks
Xiangyu He, Ke Cheng, Qiang Chen +3
Long-range dependencies modeling, widely used in capturing spatiotemporal correlation, has shown to be effective in CNN dominated computer vision tasks. Yet neither stacks of convo…