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Found 17 papers, 6 papers with code
Hierarchical Feature Alignment Network for Unsupervised Video Object Segmentation
Optical flow is an easily conceived and precious cue for advancing unsupervised video object segmentation (UVOS).
Rapid Person Re-Identification via Sub-space Consistency Regularization
To strike a balance between the model accuracy and efficiency, we propose a novel Sub-space Consistency Regularization (SCR) algorithm that can speed up the ReID procedure by $0. 25$ times than real-value features under the same dimensions whilst maintaining a competitive accuracy, especially under short codes.
Non-Salient Region Object Mining for Weakly Supervised Semantic Segmentation
To further mine the non-salient region objects, we propose to exert the segmentation network's self-correction ability.
Jo-SRC: A Contrastive Approach for Combating Noisy Labels
Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels usually results in inferior model performance.
Semantically Meaningful Class Prototype Learning for One-Shot Image Semantic Segmentation
Then we utilize the fused prototype to guide the final segmentation of the query image.
Exploiting Web Images for Fine-Grained Visual Recognition by Eliminating Noisy Samples and Utilizing Hard Ones
Labeling objects at a subordinate level typically requires expert knowledge, which is not always available when using random annotators.
Data-driven Meta-set Based Fine-Grained Visual Classification
To this end, we propose a data-driven meta-set based approach to deal with noisy web images for fine-grained recognition.
Extracting Visual Knowledge from the Internet: Making Sense of Image Data
Recent successes in visual recognition can be primarily attributed to feature representation, learning algorithms, and the ever-increasing size of labeled training data.
Towards better Validity: Dispersion based Clustering for Unsupervised Person Re-identification
With this insight, we design a novel Dispersion-based Clustering (DBC) approach which can discover the underlying patterns in data.
Ranked #19 on
Unsupervised Person Re-Identification
on Market-1501
Road Segmentation with Image-LiDAR Data Fusion
Robust road segmentation is a key challenge in self-driving research.
Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification
Our approach measures the affinity of unlabeled samples with the underlying clusters of labeled data samples using the intermediate feature representations from deep networks.
Let Features Decide for Themselves: Feature Mask Network for Person Re-identification
Person re-identification aims at establishing the identity of a pedestrian from a gallery that contains images of multiple people obtained from a multi-camera system.
Refining Image Categorization by Exploiting Web Images and General Corpus
To tackle these problems, in this work, we exploit general corpus information to automatically select and subsequently classify web images into semantic rich (sub-)categories.
Exploiting Web Images for Dataset Construction: A Domain Robust Approach
To reduce the cost of manual labelling, there has been increased research interest in automatically constructing image datasets by exploiting web images.
Hashing on Nonlinear Manifolds
In addition, a supervised inductive manifold hashing framework is developed by incorporating the label information, which is shown to greatly advance the semantic retrieval performance.
Fast Approximate L_infty Minimization: Speeding Up Robust Regression
Minimization of the $L_\infty$ norm, which can be viewed as approximately solving the non-convex least median estimation problem, is a powerful method for outlier removal and hence robust regression.
