Search Results for author:
Found 35 papers, 31 papers with code
Data-Driven Neuron Allocation for Scale Aggregation Networks
The most informative output neurons in each block are preserved while others are discarded, and thus neurons for multiple scales are competitively and adaptively allocated.
Ranked #701 on
Image Classification
on ImageNet
Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid
To address this issue, we propose a novel Graph Reasoning Network (GRNet) on a Similarity Pyramid, which learns similarities between a query and a gallery cloth by using both global and local representations in multiple scales.
Ranked #4 on
Image Retrieval
on DeepFashion - Consumer-to-shop
(Rank-1 metric)
Geometry Normalization Networks for Accurate Scene Text Detection
Large geometry (e. g., orientation) variances are the key challenges in the scene text detection.
Ranked #10 on
Scene Text Detection
on ICDAR 2017 MLT
Recovery of Future Data via Convolution Nuclear Norm Minimization
This paper studies the problem of time series forecasting (TSF) from the perspective of compressed sensing.
Gradual Network for Single Image De-raining
Taking the residual result (the coarse de-rained result) between the rainy image sample (i. e. the input data) and the output of coarse stage (i. e. the learnt rain mask) as input, the fine stage continues to de-rain by removing the fine-grained rain streaks (e. g. light rain streaks and water mist) to get a rain-free and well-reconstructed output image via a unified contextual merging sub-network with dense blocks and a merging block.
Object Instance Mining for Weakly Supervised Object Detection
Weakly supervised object detection (WSOD) using only image-level annotations has attracted growing attention over the past few years.
Ranked #10 on
Weakly Supervised Object Detection
on PASCAL VOC 2007
Scale-Equalizing Pyramid Convolution for Object Detection
Inspired by this, a convolution across the pyramid level is proposed in this study, which is termed pyramid convolution and is a modified 3-D convolution.
Ranked #76 on
Object Detection
on COCO test-dev
Maximum-and-Concatenation Networks
While successful in many fields, deep neural networks (DNNs) still suffer from some open problems such as bad local minima and unsatisfactory generalization performance.
RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition
Theoretically, our proposed method, dubbed \emph{RobustScanner}, decodes individual characters with dynamic ratio between context and positional clues, and utilizes more positional ones when the decoding sequences with scarce context, and thus is robust and practical.
Context-Aware RCNN: A Baseline for Action Detection in Videos
In this work, we first empirically find the recognition accuracy is highly correlated with the bounding box size of an actor, and thus higher resolution of actors contributes to better performance.
Webly Supervised Image Classification with Self-Contained Confidence
Therefore, a simple yet effective WSL framework is proposed.
Ranked #7 on
Image Classification
on WebVision-1000
Webly Supervised Image Classification with Metadata: Automatic Noisy Label Correction via Visual-Semantic Graph
VSGraph-LC starts from anchor selection referring to the semantic similarity between metadata and correct label concepts, and then propagates correct labels from anchors on a visual graph using graph neural network (GNN).
Ranked #9 on
Image Classification
on WebVision-1000
Towards Impartial Multi-task Learning
Multi-task learning (MTL) has been widely used in representation learning.
Spatial Dual-Modality Graph Reasoning for Key Information Extraction
In order to roundly evaluate our proposed method as well as boost the future research, we release a new dataset named WildReceipt, which is collected and annotated tailored for the evaluation of key information extraction from document images of unseen templates in the wild.
Fourier Contour Embedding for Arbitrary-Shaped Text Detection
One of the main challenges for arbitrary-shaped text detection is to design a good text instance representation that allows networks to learn diverse text geometry variances.
WSSOD: A New Pipeline for Weakly- and Semi-Supervised Object Detection
The performance of object detection, to a great extent, depends on the availability of large annotated datasets.
Group Fisher Pruning for Practical Network Compression
Our method can be used to prune any structures including those with coupled channels.
Ranked #4 on
Network Pruning
on ImageNet
Vision Transformer with Progressive Sampling
As a typical example, the Vision Transformer (ViT) directly applies a pure transformer architecture on image classification, by simply splitting images into tokens with a fixed length, and employing transformers to learn relations between these tokens.
Progressive Representative Labeling for Deep Semi-Supervised Learning
In this paper, we propose to label only the most representative samples to expand the labeled set.
MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding
We present MMOCR-an open-source toolbox which provides a comprehensive pipeline for text detection and recognition, as well as their downstream tasks such as named entity recognition and key information extraction.
Semantically Coherent Out-of-Distribution Detection
The proposed UDG can not only enrich the semantic knowledge of the model by exploiting unlabeled data in an unsupervised manner, but also distinguish ID/OOD samples to enhance ID classification and OOD detection tasks simultaneously.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Pseudo-mask Matters in Weakly-supervised Semantic Segmentation
For these matters, we propose the following designs to push the performance to new state-of-art: (i) Coefficient of Variation Smoothing to smooth the CAMs adaptively; (ii) Proportional Pseudo-mask Generation to project the expanded CAMs to pseudo-mask based on a new metric indicating the importance of each class on each location, instead of the scores trained from binary classifiers.
Ranked #27 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation
So we try to improve WSSS in the aspect of noise mitigation.
Ranked #23 on
Weakly-Supervised Semantic Segmentation
on COCO 2014 val
ViM: Out-Of-Distribution with Virtual-logit Matching
Most of the existing Out-Of-Distribution (OOD) detection algorithms depend on single input source: the feature, the logit, or the softmax probability.
Panoptic Scene Graph Generation
Existing research addresses scene graph generation (SGG) -- a critical technology for scene understanding in images -- from a detection perspective, i. e., objects are detected using bounding boxes followed by prediction of their pairwise relationships.
Ranked #5 on
Panoptic Scene Graph Generation
on PSG Dataset
Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation
In this work, we revisit the weak-to-strong consistency framework, popularized by FixMatch from semi-supervised classification, where the prediction of a weakly perturbed image serves as supervision for its strongly perturbed version.
Semi-supervised Change Detection
Semi-supervised Medical Image Segmentation
+1
Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detection
In this study, we dive deep into the inconsistency of pseudo targets in semi-supervised object detection (SSOD).
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
Out-of-distribution (OOD) detection is vital to safety-critical machine learning applications and has thus been extensively studied, with a plethora of methods developed in the literature.
BiFormer: Vision Transformer with Bi-Level Routing Attention
As the core building block of vision transformers, attention is a powerful tool to capture long-range dependency.
Ranked #9 on
Object Detection
on COCO 2017
(mAP metric)
OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection
Out-of-Distribution (OOD) detection is critical for the reliable operation of open-world intelligent systems.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Get the Best of Both Worlds: Improving Accuracy and Transferability by Grassmann Class Representation
We generalize the class vectors found in neural networks to linear subspaces (i. e.~points in the Grassmann manifold) and show that the Grassmann Class Representation (GCR) enables the simultaneous improvement in accuracy and feature transferability.
Diverse Cotraining Makes Strong Semi-Supervised Segmentor
Deep co-training has been introduced to semi-supervised segmentation and achieves impressive results, yet few studies have explored the working mechanism behind it.
SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation
Unsupervised semantic segmentation is a challenging task that segments images into semantic groups without manual annotation.
Panoptic Video Scene Graph Generation
PVSG relates to the existing video scene graph generation (VidSGG) problem, which focuses on temporal interactions between humans and objects grounded with bounding boxes in videos.
RelayAttention for Efficient Large Language Model Serving with Long System Prompts
Practical large language model (LLM) services may involve a long system prompt, which specifies the instructions, examples, and knowledge documents of the task and is reused across numerous requests.
