Search Results for author:
Found 23 papers, 11 papers with code
Distribution-Aware Calibration for Object Detection with Noisy Bounding Boxes
Large-scale well-annotated datasets are of great importance for training an effective object detector.
Clustered-patch Element Connection for Few-shot Learning
According to this, we propose a novel Clustered-patch Element Connection (CEC) layer to correct the mismatch problem.
Ranked #47 on
Few-Shot Semantic Segmentation
on COCO-20i (5-shot)
SpatialFormer: Semantic and Target Aware Attentions for Few-Shot Learning
Then we derive two specific attention modules, named SpatialFormer Semantic Attention (SFSA) and SpatialFormer Target Attention (SFTA), to enhance the target object regions while reduce the background distraction.
HDNet: A Hierarchically Decoupled Network for Crowd Counting
Recently, density map regression-based methods have dominated in crowd counting owing to their excellent fitting ability on density distribution.
Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision
This paper builds a unified framework to perform effective noisy-proposal suppression and to interact between global and local features for robust feature learning.
Rethinking the Metric in Few-shot Learning: From an Adaptive Multi-Distance Perspective
In this paper, for the first time, we investigate the contributions of different distance metrics, and propose an adaptive fusion scheme, bringing significant improvements in few-shot classification.
tSF: Transformer-based Semantic Filter for Few-Shot Learning
Few-Shot Learning (FSL) alleviates the data shortage challenge via embedding discriminative target-aware features among plenty seen (base) and few unseen (novel) labeled samples.
Towards Continual Adaptation in Industrial Anomaly Detection
We believe that the proposed task and benchmark will be beneficial to the field of AD.
Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation
This paper focus on few-shot object detection~(FSOD) and instance segmentation~(FSIS), which requires a model to quickly adapt to novel classes with a few labeled instances.
Ranked #2 on
Few-Shot Object Detection
on MS-COCO (1-shot)
How to Reduce Change Detection to Semantic Segmentation
And most segmentation networks can be adapted to solve the CD problems with our MTF module.
Ranked #1 on
Change Detection
on PCD
APANet: Adaptive Prototypes Alignment Network for Few-Shot Semantic Segmentation
In practice, it can adaptively generate multiple class-agnostic prototypes for query images and learn feature alignment in a self-contrastive manner.
A Coarse-to-Fine Instance Segmentation Network with Learning Boundary Representation
Experiments also show that our proposed method achieves competitive performance compared to existing boundary-based methods with a lightweight design and a simple pipeline.
SCNet: Enhancing Few-Shot Semantic Segmentation by Self-Contrastive Background Prototypes
To this end, we generate self-contrastive background prototypes directly from the query image, with which we enable the construction of complete sample pairs and thus a complementary and auxiliary segmentation task to achieve the training of a better segmentation model.
Learning to Discover Multi-Class Attentional Regions for Multi-Label Image Recognition
To bridge the gap between global and local streams, we propose a multi-class attentional region module which aims to make the number of attentional regions as small as possible and keep the diversity of these regions as high as possible.
Ranked #2 on
Multi-Label Classification
on PASCAL VOC 2012
Learning Expectation of Label Distribution for Facial Age and Attractiveness Estimation
The effectiveness of our approach has been demonstrated on both facial age and attractiveness estimation tasks.
Ranked #1 on
Age Estimation
on ChaLearn 2016
Age Estimation Using Expectation of Label Distribution Learning
Age estimation performance has been greatly improved by using convolutional neural network.
A Fast and Robust TSVM for Pattern Classification
In this paper, we propose a Fast and Robust TSVM~(FR-TSVM) to deal with the above issues.
Sunrise or Sunset: Selective Comparison Learning for Subtle Attribute Recognition
The difficulty of image recognition has gradually increased from general category recognition to fine-grained recognition and to the recognition of some subtle attributes such as temperature and geolocation.
Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors
In this paper, we propose Adaptive Feeding (AF) to combine a fast (but less accurate) detector and an accurate (but slow) detector, by adaptively determining whether an image is easy or hard and choosing an appropriate detector for it.
Deep Label Distribution Learning with Label Ambiguity
However, it is difficult to collect sufficient training images with precise labels in some domains such as apparent age estimation, head pose estimation, multi-label classification and semantic segmentation.
Ranked #1 on
Head Pose Estimation
on BJUT-3D
Exploit Bounding Box Annotations for Multi-label Object Recognition
With strong labels, our framework is able to achieve state-of-the-art results in both datasets.
Ranked #16 on
Multi-Label Classification
on PASCAL VOC 2007
Deep Spatial Pyramid: The Devil is Once Again in the Details
In this paper we show that by carefully making good choices for various detailed but important factors in a visual recognition framework using deep learning features, one can achieve a simple, efficient, yet highly accurate image classification system.
Visual Recognition Using Directional Distribution Distance
In computer vision, an entity such as an image or video is often represented as a set of instance vectors, which can be SIFT, motion, or deep learning feature vectors extracted from different parts of that entity.
