Search Results for author:
Found 34 papers, 5 papers with code
An Attention-driven Two-stage Clustering Method for Unsupervised Person Re-Identification
By iteratively learning with the two strategies, the attentive regions are gradually shifted from the background to the foreground and the features become more discriminative.
Learning Sequence Attractors in Recurrent Networks with Hidden Neurons
We show that to store arbitrary pattern sequences, it is necessary for the network to include hidden neurons even though their role in displaying sequence memories is indirect.
AttriHuman-3D: Editable 3D Human Avatar Generation with Attribute Decomposition and Indexing
We propose AttriHuman-3D, an editable 3D human generation model, which address the aforementioned problems with attribute decomposition and indexing.
A differentiable brain simulator bridging brain simulation and brain-inspired computing
Brain simulation builds dynamical models to mimic the structure and functions of the brain, while brain-inspired computing (BIC) develops intelligent systems by learning from the structure and functions of the brain.
Composition and Deformance: Measuring Imageability with a Text-to-Image Model
Although psycholinguists and psychologists have long studied the tendency of linguistic strings to evoke mental images in hearers or readers, most computational studies have applied this concept of imageability only to isolated words.
AI of Brain and Cognitive Sciences: From the Perspective of First Principles
Nowadays, we have witnessed the great success of AI in various applications, including image classification, game playing, protein structure analysis, language translation, and content generation.
Text-Guided Unsupervised Latent Transformation for Multi-Attribute Image Manipulation
To address this limitation, we propose a Text-guided Unsupervised StyleGAN Latent Transformation (TUSLT) model, which adaptively infers a single transformation step in the latent space of StyleGAN to simultaneously manipulate multiple attributes on a given input image.
Exploring Intra-Class Variation Factors With Learnable Cluster Prompts for Semi-Supervised Image Synthesis
Semi-supervised class-conditional image synthesis is typically performed by inferring and injecting class labels into a conditional Generative Adversarial Network (GAN).
Semi-Supervised Stereo-Based 3D Object Detection via Cross-View Consensus
Stereo-based 3D object detection, which aims at detecting 3D objects with stereo cameras, shows great potential in low-cost deployment compared to LiDAR-based methods and excellent performance compared to monocular-based algorithms.
Blemish-Aware and Progressive Face Retouching With Limited Paired Data
It is worth noting that we reduce the dependence of BPFRe on paired training samples by imposing effective regularization on unpaired ones.
Quadratic Programming for Continuous Control of Safety-Critical Multi-Agent Systems Under Uncertainty
Also, the interaction between the controlled integrator and the uncertain actuation dynamics may lead to significant robustness issues.
1000x Faster Camera and Machine Vision with Ordinary Devices
By treating vidar as spike trains in biological vision, we have further developed a spiking neural network-based machine vision system that combines the speed of the machine and the mechanism of biological vision, achieving high-speed object detection and tracking 1, 000x faster than human vision.
SphericGAN: Semi-Supervised Hyper-Spherical Generative Adversarial Networks for Fine-Grained Image Synthesis
To reduce the dependence of generative models on labeled data, we propose a semi-supervised hyper-spherical GAN for class-conditional fine-grained image generation, and our model is referred to as SphericGAN.
Digital Editions as Distant Supervision for Layout Analysis of Printed Books
Using markup schemes such as those of the Text Encoding Initiative and EpiDoc, these digital editions often record documents' semantic regions (such as notes and figures) and physical features (such as page and line breaks) as well as transcribing their textual content.
Noisy Adaptation Generates Lévy Flights in Attractor Neural Networks
To elucidate the underlying mechanism clearly, we first study continuous attractor neural networks (CANNs), and find that noisy neural adaptation, exemplified by spike frequency adaptation (SFA) in this work, can generate Lévy flights representing transitions of the network state in the attractor space.
Scalable Font Reconstruction with Dual Latent Manifolds
We propose a deep generative model that performs typography analysis and font reconstruction by learning disentangled manifolds of both font style and character shape.
Mask-Embedded Discriminator With Region-Based Semantic Regularization for Semi-Supervised Class-Conditional Image Synthesis
Semi-supervised generative learning (SSGL) makes use of unlabeled data to achieve a trade-off between the data collection/annotation effort and generation performance, when adequate labeled data are not available.
Semi-Supervised Single-Stage Controllable GANs for Conditional Fine-Grained Image Generation
To ensure disentanglement among the variables, we maximize mutual information between the class-independent variable and synthesized images, map real images to the latent space of a generator to perform consistency regularization of cross-class attributes, and incorporate class semantic-based regularization into a discriminator's feature space.
Vision at A Glance: Interplay between Fine and Coarse Information Processing Pathways
In this study, we build a computational model to elucidate the computational advantages associated with the interactions between two pathways.
Unsupervised Few-shot Learning via Self-supervised Training
The proposed model consists of two alternate processes, progressive clustering and episodic training.
Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently
Experimental data has revealed that in addition to feedforward connections, there exist abundant feedback connections in a neural pathway.
A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits
This study provides a normative theory for how Bayesian causal inference can be implemented in neural circuits.
Semi-Supervised Pedestrian Instance Synthesis and Detection With Mutual Reinforcement
We propose a GAN-based scene-specific instance synthesis and classification model for semi-supervised pedestrian detection.
Unsupervised Few Shot Learning via Self-supervised Training
Using the benchmark dataset Omniglot, we show that our model outperforms other unsupervised few-shot learning methods to a large extend and approaches to the performances of supervised methods.
Spatiotemporal Information Processing with a Reservoir Decision-making Network
Spatiotemporal information processing is fundamental to brain functions.
Enhancing TripleGAN for Semi-Supervised Conditional Instance Synthesis and Classification
We follow the adversarial training scheme of the original TripleGAN, but completely re-design the training targets of the generator and classifier.
Mutual Learning of Complementary Networks via Residual Correction for Improving Semi-Supervised Classification
Our experimental results demonstrate that the proposed approach clearly improves mutual learning between essential networks, and achieves state-of-the-art results on multiple semi-supervised classification benchmarks.
“Congruent” and “Opposite” Neurons: Sisters for Multisensory Integration and Segregation
Mimicking the experimental protocol, our model reproduces the characteristics of congruent and opposite neurons, and demonstrates that in each module, the sisters of congruent and opposite neurons can jointly achieve optimal multisensory information integration and segregation.
A Synaptical Story of Persistent Activity with Graded Lifetime in a Neural System
Here, in contrast to the view of attractor, we consider that the stimulus information is encoded in a marginally unstable state of the network which decays very slowly and exhibits persistent firing for a prolonged duration.
Spike Frequency Adaptation Implements Anticipative Tracking in Continuous Attractor Neural Networks
To extract motion information, the brain needs to compensate for time delays that are ubiquitous in neural signal transmission and processing.
Reciprocally Coupled Local Estimators Implement Bayesian Information Integration Distributively
Psychophysical experiments have demonstrated that the brain integrates information from multiple sensory cues in a near Bayesian optimal manner.
Delay Compensation with Dynamical Synapses
To achieve real-time tracking, it is critical to compensate the transmission and processing delays in a neural system.
Attractor Dynamics with Synaptic Depression
Neuronal connection weights exhibit short-term depression (STD).
Tracking Changing Stimuli in Continuous Attractor Neural Networks
Continuous attractor neural networks (CANNs) are emerging as promising models for describing the encoding of continuous stimuli in neural systems.
