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Found 24 papers, 3 papers with code
Spiking Neural Network for Ultra-low-latency and High-accurate Object Detection
We also present a SNN-based ultra-low latency and high accurate object detection model (SUHD) that achieves state-of-the-art performance on nontrivial datasets like PASCAL VOC and MS COCO, with about remarkable 750x fewer timesteps and 30% mean average precision (mAP) improvement, compared to the Spiking-YOLO on MS COCO datasets.
Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning
In the first stage, we obtain a robust feature extractor, which could serve for all images with base and novel categories.
Zero-shot object goal visual navigation
Object goal visual navigation is a challenging task that aims to guide a robot to find the target object based on its visual observation, and the target is limited to the classes pre-defined in the training stage.
Weakly Aligned Feature Fusion for Multimodal Object Detection
In this article, we propose a general multimodal detector named aligned region CNN (AR-CNN) to tackle the position shift problem.
Unseen Object Instance Segmentation with Fully Test-time RGB-D Embeddings Adaptation
In this paper, we emphasize the adaptation process across sim2real domains and model it as a learning problem on the BatchNorm parameters of a simulation-trained model.
A Bioinspired Approach-Sensitive Neural Network for Collision Detection in Cluttered and Dynamic Backgrounds
Rapid, accurate and robust detection of looming objects in cluttered moving backgrounds is a significant and challenging problem for robotic visual systems to perform collision detection and avoidance tasks.
A Bioinspired Retinal Neural Network for Accurately Extracting Small-Target Motion Information in Cluttered Backgrounds
Robust and accurate detection of small moving targets in cluttered moving backgrounds is a significant and challenging problem for robotic visual systems to perform search and tracking tasks.
A Minimax Probability Machine for Non-Decomposable Performance Measures
On the other hand, the minimax probability machine is a popular method for binary classification problems and aims at learning a linear classifier by maximizing the accuracy rate, which makes it unsuitable to deal with imbalanced classification tasks.
Learning with Smooth Hinge Losses
Due to the non-smoothness of the Hinge loss in SVM, it is difficult to obtain a faster convergence rate with modern optimization algorithms.
Long-distance tiny face detection based on enhanced YOLOv3 for unmanned system
In this model, we bring in multi-scale features from feature pyramid networks and make the features fu-sion to adjust prediction feature map of the output, which improves the sensitivity of the entire algorithm for tiny target faces.
FSD-10: A Dataset for Competitive Sports Content Analysis
To promote the research on action recognition from competitive sports video clips, we introduce a Figure Skating Dataset (FSD-10) for finegrained sports content analysis.
Hierarchic Neighbors Embedding
Manifold learning now plays a very important role in machine learning and many relevant applications.
Multi-view Laplacian Eigenmaps Based on Bag-of-Neighbors For RGBD Human Emotion Recognition
As RGB view and depth view lie in different spaces, a new distance metric bag of neighbors (BON) used in MvLE can get the similar distributions of the two views.
Perceptual Visual Interactive Learning
Supervised learning methods are widely used in machine learning.
Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders
Traditional image clustering methods take a two-step approach, feature learning and clustering, sequentially.
Ranked #4 on
Image Clustering
on Coil-20
A Fast and Compact Saliency Score Regression Network Based on Fully Convolutional Network
In this paper, we tackle this problem by proposing a fast and compact saliency score regression network which employs fully convolutional network, a special deep convolutional neural network, to estimate the saliency of objects in images.
Effective Deterministic Initialization for $k$-Means-Like Methods via Local Density Peaks Searching
Based on these two components, we search for the local density peaks which are characterized with high local densities and high LDIs to deal with 1) and 2).
Perceptual uniform descriptor and Ranking on manifold: A bridge between image representation and ranking for image retrieval
Incompatibility of image descriptor and ranking is always neglected in image retrieval.
Poisson Noise Reduction with Higher-order Natural Image Prior Model
We start with a direct modeling in the original image domain by taking into account the Poisson noise statistics, which performs generally well for the cases of high SNR.
A New Manifold Distance Measure for Visual Object Categorization
The proposed distance is more robust to rotations and translations of images than the traditional manifold distance and the CW-SSIM index based distance.
A Novel Biologically Mechanism-Based Visual Cognition Model--Automatic Extraction of Semantics, Formation of Integrated Concepts and Re-selection Features for Ambiguity
In general, the robustness and precision of recognition is one of the key problems for object recognition models.
A Weighted Common Subgraph Matching Algorithm
We propose a weighted common subgraph (WCS) matching algorithm to find the most similar subgraphs in two labeled weighted graphs.
A higher-order MRF based variational model for multiplicative noise reduction
The Fields of Experts (FoE) image prior model, a filter-based higher-order Markov Random Fields (MRF) model, has been shown to be effective for many image restoration problems.
GNCGCP - Graduated NonConvexity and Graduated Concavity Procedure
In this paper we propose the Graduated NonConvexity and Graduated Concavity Procedure (GNCGCP) as a general optimization framework to approximately solve the combinatorial optimization problems on the set of partial permutation matrices.
