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Found 22 papers, 4 papers with code
SLPerf: a Unified Framework for Benchmarking Split Learning
Data privacy concerns has made centralized training of data, which is scattered across silos, infeasible, leading to the need for collaborative learning frameworks.
An Iterative Co-Training Transductive Framework for Zero Shot Learning
How to generate pseudo labels for unseen-class samples and how to use such usually noisy pseudo labels are two critical issues in transductive learning.
Semantic-diversity transfer network for generalized zero-shot learning via inner disagreement based OOD detector
Firstly, we propose a Semantic-diversity transfer Network (SetNet) addressing the first two limitations, where 1) a multiple-attention architecture and a diversity regularizer are proposed to learn multiple local visual features that are more consistent with semantic attributes and 2) a projector ensemble that geometrically takes diverse local features as inputs is proposed to model visual-semantic relations from diverse local perspectives.
Pursuing 3D Scene Structures with Optical Satellite Images from Affine Reconstruction to Euclidean Reconstruction
How to use multiple optical satellite images to recover the 3D scene structure is a challenging and important problem in the remote sensing field.
HardBoost: Boosting Zero-Shot Learning with Hard Classes
This work is a systematical analysis on the so-called hard class problem in zero-shot learning (ZSL), that is, some unseen classes disproportionally affect the ZSL performances than others, as well as how to remedy the problem by detecting and exploiting hard classes.
Superpoint-guided Semi-supervised Semantic Segmentation of 3D Point Clouds
The proposed network is iteratively updated with its predicted pseudo labels, where a superpoint generation module is introduced for extracting superpoints from 3D point clouds, and a pseudo-label optimization module is explored for automatically assigning pseudo labels to the unlabeled points under the constraint of the extracted superpoints.
Rotation Transformation Network: Learning View-Invariant Point Cloud for Classification and Segmentation
Many recent works show that a spatial manipulation module could boost the performances of deep neural networks (DNNs) for 3D point cloud analysis.
Language-Level Semantics Conditioned 3D Point Cloud Segmentation
In this work, a language-level Semantics Conditioned framework for 3D Point cloud segmentation, called SeCondPoint, is proposed, where language-level semantics are introduced to condition the modeling of point feature distribution as well as the pseudo-feature generation, and a feature-geometry-based mixup approach is further proposed to facilitate the distribution learning.
Hardness Sampling for Self-Training Based Transductive Zero-Shot Learning
Addressing this problem, we first empirically analyze the roles of unseen-class samples with different degrees of hardness in the training process based on the uneven prediction phenomenon found in many ZSL methods, resulting in three observations.
Optimization-Based Visual-Inertial SLAM Tightly Coupled with Raw GNSS Measurements
Unlike loose coupling approaches and the EKF-based approaches in the literature, we propose an optimization-based visual-inertial SLAM tightly coupled with raw Global Navigation Satellite System (GNSS) measurements, a first attempt of this kind in the literature to our knowledge.
Robotics
Zero-Shot Learning from Adversarial Feature Residual to Compact Visual Feature
In addition, considering that the visual features from categorization CNNs are generally inconsistent with their semantic features, a simple feature selection strategy is introduced for extracting more compact semantic visual features.
Bidirectional Trajectory Computation for Odometer-Aided Visual-Inertial SLAM
the extrinsic parameters before the first turning, which is a complement of the existing results of observability analyses.
Robotics
Face representation by deep learning: a linear encoding in a parameter space?
In this work, we investigate this problem by formulating face images as points in a shape-appearance parameter space, and our results demonstrate that: (i) The encoding and decoding of the neuron responses (representations) to face images in CNNs could be achieved under a linear model in the parameter space, in agreement with the recent discovery in primate IT face neurons, but different from the aforementioned perspective on CNNs' face representation with complex image feature encoding; (ii) The linear model for face encoding and decoding in the parameter space could achieve close or even better performances on face recognition and verification than state-of-the-art CNNs, which might provide new lights on the design strategies for face recognition systems; (iii) The neuron responses to face images in CNNs could not be adequately modelled by the axis model, a model recently proposed on face modelling in primate IT cortex.
Non-uniqueness phenomenon of object representation in modelling IT cortex by deep convolutional neural network (DCNN)
Recently DCNN (Deep Convolutional Neural Network) has been advocated as a general and promising modelling approach for neural object representation in primate inferotemporal cortex.
Complete Scene Reconstruction by Merging Images and Laser Scans
Experimental evaluations on two ancient Chinese architecture datasets demonstrate the effectiveness of our proposed complete scene reconstruction pipeline.
Large Scale Urban Scene Modeling from MVS Meshes
There are two major steps in our framework: segmentation and building modeling.
Learning Depth from Single Images with Deep Neural Network Embedding Focal Length
In order to learn monocular depth by embedding the focal length, we propose a method to generate synthetic varying-focal-length dataset from fixed-focal-length datasets, and a simple and effective method is implemented to fill the holes in the newly generated images.
CSfM: Community-based Structure from Motion
The global manner has the advantage of simultaneously estimating all camera poses, but it is usually sensitive to epipolar geometry outliers.
HSfM: Hybrid Structure-from-Motion
In this work, we propose a new hybrid SfM method to tackle the issues of efficiency, accuracy and robustness in a unified framework.
Statistics of Visual Responses to Object Stimuli from Primate AIT Neurons to DNN Neurons
Lehky et al. (Lehky, 2011) provided a statistical analysis on neural responses to object stimuli in primate AIT cortex.
Modern Physiognomy: An Investigation on Predicting Personality Traits and Intelligence from the Human Face
The classification results show that the personality traits "Rule-consciousness" and "Vigilance" can be predicted reliably, and that the traits of females can be predicted more accurately than those of male.
Dynamic Parallel and Distributed Graph Cuts
Through both the splitting and merging, we further propose a dynamic parallel and distributed graph-cuts algorithm with guaranteed convergence to the globally optimal solutions within a predefined number of iterations.
