Search Results for author:
Found 30 papers, 13 papers with code
ScrollTimes: Tracing the Provenance of Paintings as a Window into History
We collaborated with art historians to examine handscrolls, a form of traditional Chinese painting which offers a wealth of data for historical analysis and provides a unique opportunity for understanding history through artwork.
Improving Knowledge Distillation via Regularizing Feature Norm and Direction
Finally, we propose a rather simple loss term (dubbed ND loss) to simultaneously (1) encourage student to produce large-\emph{norm} features, and (2) align the \emph{direction} of student features and teacher class-means.
Ranked #1 on
Knowledge Distillation
on ImageNet
Masked Collaborative Contrast for Weakly Supervised Semantic Segmentation
This study introduces an efficacious approach, Masked Collaborative Contrast (MCC), to highlight semantic regions in weakly supervised semantic segmentation.
Contrastive Learning
Weakly supervised Semantic Segmentation
+1
Mitigating Undisciplined Over-Smoothing in Transformer for Weakly Supervised Semantic Segmentation
A surge of interest has emerged in weakly supervised semantic segmentation due to its remarkable efficiency in recent years.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Life Regression based Patch Slimming for Vision Transformers
In this study, we tackle the patch slimming problem from a different perspective by proposing a life regression module that determines the lifespan of each image patch in one go.
ViT-Calibrator: Decision Stream Calibration for Vision Transformer
In this work, we propose a new paradigm dubbed Decision Stream Calibration that boosts the performance of general Vision Transformers.
Propheter: Prophetic Teacher Guided Long-Tailed Distribution Learning
This is specifically achieved by devising an elaborated ``prophetic'' teacher, termed as ``Propheter'', that aims to learn the potential class distributions.
Generalization Matters: Loss Minima Flattening via Parameter Hybridization for Efficient Online Knowledge Distillation
Most existing online knowledge distillation(OKD) techniques typically require sophisticated modules to produce diverse knowledge for improving students' generalization ability.
Model Doctor for Diagnosing and Treating Segmentation Error
Despite the remarkable progress in semantic segmentation tasks with the advancement of deep neural networks, existing U-shaped hierarchical typical segmentation networks still suffer from local misclassification of categories and inaccurate target boundaries.
Team DETR: Guide Queries as a Professional Team in Detection Transformers
Recent proposed DETR variants have made tremendous progress in various scenarios due to their streamlined processes and remarkable performance.
Revisiting Long-tailed Image Classification: Survey and Benchmarks with New Evaluation Metrics
We set up novel evaluation benchmarks based on a series of testing sets with evolving distributions.
Ranked #59 on
Long-tail Learning
on CIFAR-100-LT (ρ=100)
Boosting Low-Data Instance Segmentation by Unsupervised Pre-training with Saliency Prompt
Inspired by the recent success of the Prompting technique, we introduce a new pre-training method that boosts QEIS models by giving Saliency Prompt for queries/kernels.
Text-Guided Mask-free Local Image Retouching
In the realm of multi-modality, text-guided image retouching techniques emerged with the advent of deep learning.
SASFormer: Transformers for Sparsely Annotated Semantic Segmentation
In this work, we propose a simple yet effective sparse annotated semantic segmentation framework based on segformer, dubbed SASFormer, that achieves remarkable performance.
Semantic Segmentation
Weakly supervised Semantic Segmentation
Compound Batch Normalization for Long-tailed Image Classification
It can model the feature space more comprehensively and reduce the dominance of head classes.
Transferability Estimation Based On Principal Gradient Expectation
Transfer learning aims to improve the performance of target tasks by transferring knowledge acquired in source tasks.
A Survey of Neural Trees
This survey aims to present a comprehensive review of NTs and attempts to identify how they enhance the model interpretability.
Combating Noisy Labels in Long-Tailed Image Classification
Most existing methods that cope with noisy labels usually assume that the class distributions are well balanced, which has insufficient capacity to deal with the practical scenarios where training samples have imbalanced distributions.
ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognition
The global prototypes are adopted to provide the global view of objects to guide local prototypes to concentrate on the foreground while eliminating the influence of the background.
Dual Domain-Adversarial Learning for Audio-Visual Saliency Prediction
The domain discrepancy induces to performance degradation on target testing data for CNN models.
Re-Attention Transformer for Weakly Supervised Object Localization
Weakly supervised object localization is a challenging task which aims to localize objects with coarse annotations such as image categories.
Long-term Leap Attention, Short-term Periodic Shift for Video Classification
By replacing a vanilla 2D attention with the LAPS, we could adapt a static transformer into a video one, with zero extra parameters and neglectable computation overhead ($\sim$2. 6\%).
KE-RCNN: Unifying Knowledge based Reasoning into Part-level Attribute Parsing
In this article, we propose a Knowledge Embedded RCNN (KE-RCNN) to identify attributes by leveraging rich knowledges, including implicit knowledge (e. g., the attribute ``above-the-hip'' for a shirt requires visual/geometry relations of shirt-hip) and explicit knowledge (e. g., the part of ``shorts'' cannot have the attribute of ``hoodie'' or ``lining'').
Cross-Modality High-Frequency Transformer for MR Image Super-Resolution
Improving the resolution of magnetic resonance (MR) image data is critical to computer-aided diagnosis and brain function analysis.
CNN LEGO: Disassembling and Assembling Convolutional Neural Network
the feature maps are adopted to locate the critical features in each layer.
Weakly Supervised Semantic Segmentation via Alternative Self-Dual Teaching
Current weakly supervised semantic segmentation (WSSS) frameworks usually contain the separated mask-refinement model and the main semantic region mining model.
Knowledge Distillation
Weakly supervised Semantic Segmentation
+1
Edge-competing Pathological Liver Vessel Segmentation with Limited Labels
The diagnosis of MVI needs discovering the vessels that contain hepatocellular carcinoma cells and counting their number in each vessel, which depends heavily on experiences of the doctor, is largely subjective and time-consuming.
Visual Boundary Knowledge Translation for Foreground Segmentation
To this end, we propose a Translation Segmentation Network (Trans-Net), which comprises a segmentation network and two boundary discriminators.
Boundary Knowledge Translation based Reference Semantic Segmentation
In this paper, we introduce a novel Reference semantic segmentation Network (Ref-Net) to conduct visual boundary knowledge translation.
Intrinsic Image Transformation via Scale Space Decomposition
We introduce a new network structure for decomposing an image into its intrinsic albedo and shading.
