Search Results for author:
Found 26 papers, 4 papers with code
Human-Object Interaction Detection via Disentangled Transformer
To associate the predictions of disentangled decoders, we first generate a unified representation for HOI triplets with a base decoder, and then utilize it as input feature of each disentangled decoder.
GaTector: A Unified Framework for Gaze Object Prediction
In this paper, we build a novel framework named GaTector to tackle the gaze object prediction problem in a unified way.
EgoRenderer: Rendering Human Avatars from Egocentric Camera Images
We next combine the target pose image and the textures into a combined feature image, which is transformed into the output color image using a neural image translation network.
CRD-CGAN: Category-Consistent and Relativistic Constraints for Diverse Text-to-Image Generation
Based on those constraints, a category-consistent and relativistic diverse conditional GAN (CRD-CGAN) is proposed to synthesize $K$ photo-realistic images simultaneously.
Self-Supervised 3D Mesh Reconstruction From Single Images
Recent single-view 3D reconstruction methods reconstruct object's shape and texture from a single image with only 2D image-level annotation.
Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distribution using healthcare worker infection data
Therefore, in this paper, a novel framework was proposed to explore the impact of under-reporting on COVID-19 spatiotemporal distributions, and empirical analysis was carried out using infection data of healthcare workers in Wuhan and Hubei (excluding Wuhan).
Hierarchical Modes Exploring in Generative Adversarial Networks
In conditional Generative Adversarial Networks (cGANs), when two different initial noises are concatenated with the same conditional information, the distance between their outputs is relatively smaller, which makes minor modes likely to collapse into large modes.
Multi-object Tracking via End-to-end Tracklet Searching and Ranking
Recent works in multiple object tracking use sequence model to calculate the similarity score between the detections and the previous tracklets.
Learning to Generate Dense Point Clouds with Textures on Multiple Categories
In this paper, we propose a novel approach for reconstructing point clouds from RGB images.
3D Shape Completion with Multi-view Consistent Inference
We formulate the regularization term as a consistency loss that encourages geometric consistency among multiple views, while the data term guarantees that the optimized views do not drift away too much from a learned shape descriptor.
SILCO: Show a Few Images, Localize the Common Object
To that end, we introduce a spatial similarity module that searches the spatial commonality among the given images.
A Radio Signal Modulation Recognition Algorithm Based on Residual Networks and Attention Mechanisms
To solve the problem of inaccurate recognition of types of communication signal modulation, a RNN neural network recognition algorithm combining residual block network with attention mechanism is proposed.
Real Time Visual Tracking using Spatial-Aware Temporal Aggregation Network
Our tracker achieves leading performance in OTB2013, OTB2015, VOT2015, VOT2016 and LaSOT, and operates at a real-time speed of 26 FPS, which indicates our method is effective and practical.
VITAL: A Visual Interpretation on Text with Adversarial Learning for Image Labeling
In this paper, we propose a novel way to interpret text information by extracting visual feature presentation from multiple high-resolution and photo-realistic synthetic images generated by Text-to-image Generative Adversarial Network (GAN) to improve the performance of image labeling.
Attention-Based Multi-Context Guiding for Few-Shot Semantic Segmentation
Few-shot learning is a nascent research topic, motivated by the fact that traditional deep learning methods require tremen- dous amounts of data.
Ranked #1 on
Few-Shot Semantic Segmentation
on Pascal5i
Render4Completion: Synthesizing Multi-View Depth Maps for 3D Shape Completion
Different from image-to-image translation network that completes each view separately, our novel network, multi-view completion net (MVCN), leverages information from all views of a 3D shape to help the completion of each single view.
See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification
Specifically, for each training image, we first generate attention maps to represent the object's discriminative parts by weakly supervised learning.
Ranked #9 on
Fine-Grained Image Classification
on CUB-200-2011
Weakly Supervised Bilinear Attention Network for Fine-Grained Visual Classification
Besides, we propose attention regularization and attention dropout to weakly supervise the generating process of attention maps.
Facial Landmarks Detection by Self-Iterative Regression based Landmarks-Attention Network
Only one self-iterative regressor is trained to learn the descent directions for samples from coarse stages to fine stages, and parameters are iteratively updated by the same regressor.
Ranked #13 on
Face Alignment
on 300W
(NME_inter-pupil (%, Common) metric)
A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data
Such networks learn the principal subspace, in the sense of principal component analysis (PCA), by adjusting synaptic weights according to activity-dependent learning rules.
A Hebbian/Anti-Hebbian Network for Online Sparse Dictionary Learning Derived from Symmetric Matrix Factorization
Here, to overcome this problem, we derive sparse dictionary learning from a novel cost-function - a regularized error of the symmetric factorization of the input's similarity matrix.
Task-group Relatedness and Generalization Bounds for Regularized Multi-task Learning
We are mainly concerned with two theoretical questions: 1) under what conditions does RMTL perform better with a smaller task sample size than STL?
A Neuron as a Signal Processing Device
Here we propose to view a neuron as a signal processing device that represents the incoming streaming data matrix as a sparse vector of synaptic weights scaled by an outgoing sparse activity vector.
A mechanistic model of early sensory processing based on subtracting sparse representations
However, feedback inhibitory circuits are common in early sensory circuits and furthermore their dynamics may be nonlinear.
Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME)
One of the central problems in neuroscience is reconstructing synaptic connectivity in neural circuits.
