Search Results for author:
Found 130 papers, 49 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.
USP-Gaussian: Unifying Spike-based Image Reconstruction, Pose Correction and Gaussian Splatting
Leveraging the multi-view consistency afforded by 3DGS and the motion capture capability of the spike camera, our framework enables a joint iterative optimization that seamlessly integrates information between the spike-to-image network and 3DGS.
Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?
We are committed to expanding this benchmark to encourage more innovation of AI algorithms for the medical domain.
Comprehensive Online Training and Deployment for Spiking Neural Networks
Spiking Neural Networks (SNNs) are considered to have enormous potential in the future development of Artificial Intelligence (AI) due to their brain-inspired and energy-efficient properties.
CopyLens: Dynamically Flagging Copyrighted Sub-Dataset Contributions to LLM Outputs
However, the gap between them exists, where direct assessments of how dataset contributions impact LLM outputs are missing.
Emu3: Next-Token Prediction is All You Need
While next-token prediction is considered a promising path towards artificial general intelligence, it has struggled to excel in multimodal tasks, which are still dominated by diffusion models (e. g., Stable Diffusion) and compositional approaches (e. g., CLIP combined with LLMs).
Ranked #104 on
Visual Question Answering
on MM-Vet
Learning from Pattern Completion: Self-supervised Controllable Generation
Inspired by the neural mechanisms that may contribute to the brain's associative power, specifically the cortical modularization and hippocampal pattern completion, here we propose a self-supervised controllable generation (SCG) framework.
Video-XL: Extra-Long Vision Language Model for Hour-Scale Video Understanding
Although current Multi-modal Large Language Models (MLLMs) demonstrate promising results in video understanding, processing extremely long videos remains an ongoing challenge.
Towards Low-latency Event-based Visual Recognition with Hybrid Step-wise Distillation Spiking Neural Networks
In this paper, we propose Hybrid Step-wise Distillation (HSD) method, tailored for neuromorphic datasets, to mitigate the notable decline in performance at lower time steps.
OmniGen: Unified Image Generation
In this work, we introduce OmniGen, a new diffusion model for unified image generation.
ShapeMamba-EM: Fine-Tuning Foundation Model with Local Shape Descriptors and Mamba Blocks for 3D EM Image Segmentation
Electron microscopy (EM) imaging offers unparalleled resolution for analyzing neural tissues, crucial for uncovering the intricacies of synaptic connections and neural processes fundamental to understanding behavioral mechanisms.
Discovering Long-Term Effects on Parameter Efficient Fine-tuning
Pre-trained Artificial Neural Networks (ANNs) exhibit robust pattern recognition capabilities and share extensive similarities with the human brain, specifically Biological Neural Networks (BNNs).
PRTGS: Precomputed Radiance Transfer of Gaussian Splats for Real-Time High-Quality Relighting
We proposed Precomputed RadianceTransfer of GaussianSplats (PRTGS), a real-time high-quality relighting method for Gaussian splats in low-frequency lighting environments that captures soft shadows and interreflections by precomputing 3D Gaussian splats' radiance transfer.
Multimodal Large Language Models for Bioimage Analysis
Rapid advancements in imaging techniques and analytical methods over the past decade have revolutionized our ability to comprehensively probe the biological world at multiple scales, pinpointing the type, quantity, location, and even temporal dynamics of biomolecules.
SpikeGS: 3D Gaussian Splatting from Spike Streams with High-Speed Camera Motion
To address this issue, we make the first attempt to introduce the 3D Gaussian Splatting (3DGS) into spike cameras in high-speed capture, providing 3DGS as dense and continuous clues of views, then constructing SpikeGS.
Retrospective for the Dynamic Sensorium Competition for predicting large-scale mouse primary visual cortex activity from videos
To address this gap, we established the Sensorium 2023 Benchmark Competition with dynamic input, featuring a new large-scale dataset from the primary visual cortex of ten mice.
SpikeGS: Reconstruct 3D scene via fast-moving bio-inspired sensors
3D Gaussian Splatting (3DGS) demonstrates unparalleled superior performance in 3D scene reconstruction.
52B to 1T: Lessons Learned via Tele-FLM Series
Large Language Models (LLMs) represent a significant stride toward Artificial General Intelligence.
MLVU: A Comprehensive Benchmark for Multi-Task Long Video Understanding
To address the above problems, we propose a new benchmark, called MLVU (Multi-task Long Video Understanding Benchmark), for the comprehensive and in-depth evaluation of LVU.
SpikeMM: Flexi-Magnification of High-Speed Micro-Motions
This innovative approach comprehensively records temporal and spatial visual information, rendering it particularly suitable for magnifying high-speed micro-motions. This paper introduces SpikeMM, a pioneering spike-based algorithm tailored specifically for high-speed motion magnification.
Robust Stable Spiking Neural Networks
We present that membrane potential perturbation dynamics can reliably convey the intensity of perturbation.
Enhancing Adversarial Robustness in SNNs with Sparse Gradients
In this paper, we propose a novel approach to enhance the robustness of SNNs through gradient sparsity regularization.
Implicit Neural Image Field for Biological Microscopy Image Compression
The rapid pace of innovation in biological microscopy imaging has led to large images, putting pressure on data storage and impeding efficient sharing, management, and visualization.
SCSim: A Realistic Spike Cameras Simulator
Large-scale synthetic datasets have significantly accelerated the development of these cameras, particularly in reconstruction and optical flow.
Implementing feature binding through dendritic networks of a single neuron
A single neuron receives an extensive array of synaptic inputs through its dendrites, raising the fundamental question of how these inputs undergo integration and summation, culminating in the initiation of spikes in the soma.
Tele-FLM Technical Report
Large language models (LLMs) have showcased profound capabilities in language understanding and generation, facilitating a wide array of applications.
SpikeNVS: Enhancing Novel View Synthesis from Blurry Images via Spike Camera
Since the spike camera relies on temporal integration instead of temporal differentiation used by event cameras, our proposed TfS loss maintains manageable training costs.
M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models
Additionally, we propose M3D-LaMed, a versatile multi-modal large language model for 3D medical image analysis.
Spike-NeRF: Neural Radiance Field Based On Spike Camera
As a neuromorphic sensor with high temporal resolution, spike cameras offer notable advantages over traditional cameras in high-speed vision applications such as high-speed optical estimation, depth estimation, and object tracking.
SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike Streams
Our approach begins with the formulation of a spike-guided deblurring model that explores the theoretical relationships among spike streams, blurry images, and their corresponding sharp sequences.
Noisy Spiking Actor Network for Exploration
As a general method for exploration in deep reinforcement learning (RL), NoisyNet can produce problem-specific exploration strategies.
Pushing Auto-regressive Models for 3D Shape Generation at Capacity and Scalability
In this paper, we extend auto-regressive models to 3D domains, and seek a stronger ability of 3D shape generation by improving auto-regressive models at capacity and scalability simultaneously.
Efficient Multimodal Learning from Data-centric Perspective
Multimodal Large Language Models (MLLMs) have demonstrated notable capabilities in general visual understanding and reasoning tasks.
LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through Learnable Multi-hierarchical Threshold Model
Extensive experimental results have demonstrated that our model can outperform previous state-of-the-art works on various types of datasets, which promote SNNs to achieve a brand-new level of performance comparable to quantized ANNs.
Learning to Robustly Reconstruct Low-light Dynamic Scenes from Spike Streams
In this paper, we propose a bidirectional recurrent-based reconstruction framework, including a Light-Robust Representation (LR-Rep) and a fusion module, to better handle such extreme conditions.
Fully Spiking Actor Network with Intra-layer Connections for Reinforcement Learning
Recently, the surrogate gradient method has been utilized for training multi-layer SNNs, which allows SNNs to achieve comparable performance with the corresponding deep networks in this task.
Super-Resolution Reconstruction from Bayer-Pattern Spike Streams
Experimental results demonstrate that the proposed scheme can reconstruct satisfactory color images with both high temporal and spatial resolution from low-resolution Bayer-pattern spike streams.
Spike-guided Motion Deblurring with Unknown Modal Spatiotemporal Alignment
It can supplement the temporal information lost in traditional cameras and guide motion deblurring.
Boosting Spike Camera Image Reconstruction from a Perspective of Dealing with Spike Fluctuations
Different from traditional cameras each pixel in spike cameras records the arrival of photons continuously by firing binary spikes at an ultra-fine temporal granularity.
Intensity-Robust Autofocus for Spike Camera
Nevertheless due to disparities in data modality and information characteristics compared to frame stream and event stream the current lack of efficient AC methods has made it challenging for spike cameras to adapt to intricate real-world conditions.
Towards HDR and HFR Video from Rolling-Mixed-Bit Spikings
However reconstructing HDR videos in high-speed conditions using single-bit spikings presents challenges due to the limited bit depth.
Generative Multimodal Models are In-Context Learners
The human ability to easily solve multimodal tasks in context (i. e., with only a few demonstrations or simple instructions), is what current multimodal systems have largely struggled to imitate.
Ranked #3 on
Personalized Image Generation
on DreamBooth
GeoDream: Disentangling 2D and Geometric Priors for High-Fidelity and Consistent 3D Generation
We justify that the refined 3D geometric priors aid in the 3D-aware capability of 2D diffusion priors, which in turn provides superior guidance for the refinement of 3D geometric priors.
SegVol: Universal and Interactive Volumetric Medical Image Segmentation
Precise image segmentation provides clinical study with instructive information.
Uni3D: Exploring Unified 3D Representation at Scale
Scaling up representations for images or text has been extensively investigated in the past few years and has led to revolutions in learning vision and language.
Ranked #1 on
Zero-shot 3D classification
on Objaverse LVIS
(using extra training data)
GIN: Generative INvariant Shape Prior for Amodal Instance Segmentation
In human amodal perception, shape-prior knowledge is helpful for AIS.
Emu: Generative Pretraining in Multimodality
We present Emu, a Transformer-based multimodal foundation model, which can seamlessly generate images and texts in multimodal context.
Ranked #1 on
Visual Question Answering
on VizWiz
SVIT: Scaling up Visual Instruction Tuning
Thanks to the emerging of foundation models, the large language and vision models are integrated to acquire the multimodal ability of visual captioning, question answering, etc.
Unveiling the Potential of Spike Streams for Foreground Occlusion Removal from Densely Continuous Views
To process the spikes, we build a novel model \textbf{SpkOccNet}, in which we integrate information of spikes from continuous viewpoints within multi-windows, and propose a novel cross-view mutual attention mechanism for effective fusion and refinement.
Mitigating Communication Costs in Neural Networks: The Role of Dendritic Nonlinearity
Our comprehension of biological neuronal networks has profoundly influenced the evolution of artificial neural networks (ANNs).
Pushing the Limits of 3D Shape Generation at Scale
Through the adaptation of the Auto-Regressive model and the utilization of large language models, we have developed a remarkable model with an astounding 3. 6 billion trainable parameters, establishing it as the largest 3D shape generation model to date, named Argus-3D.
Spike timing reshapes robustness against attacks in spiking neural networks
The success of deep learning in the past decade is partially shrouded in the shadow of adversarial attacks.
Large-scale Dataset Pruning with Dynamic Uncertainty
The state of the art of many learning tasks, e. g., image classification, is advanced by collecting larger datasets and then training larger models on them.
OAFormer: Learning Occlusion Distinguishable Feature for Amodal Instance Segmentation
Hence we believe it is vital for the method to be distinguishable about the degree of occlusion for each instance.
HUB: Guiding Learned Optimizers with Continuous Prompt Tuning
By incorporating hand-designed optimizers as the second component in our hybrid approach, we are able to retain the benefits of learned optimizers while stabilizing the training process and, more importantly, improving testing performance.
Spike Stream Denoising via Spike Camera Simulation
Based on our noise model, the first benchmark for spike stream denoising is proposed which includes clear (noisy) spike stream.
SegGPT: Segmenting Everything In Context
We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data by transforming them into the same format of images.
Ranked #1 on
Few-Shot Semantic Segmentation
on PASCAL-5i (5-Shot)
(using extra training data)
Exploring Efficient Asymmetric Blind-Spots for Self-Supervised Denoising in Real-World Scenarios
Based on this, we propose Asymmetric Tunable Blind-Spot Network (AT-BSN), where the blind-spot size can be freely adjusted, thus better balancing noise correlation suppression and image local spatial destruction during training and inference.
SpikeCV: Open a Continuous Computer Vision Era
SpikeCV focuses on encapsulation for spike data, standardization for dataset interfaces, modularization for vision tasks, and real-time applications for challenging scenes.
EVA-02: A Visual Representation for Neon Genesis
We launch EVA-02, a next-generation Transformer-based visual representation pre-trained to reconstruct strong and robust language-aligned vision features via masked image modeling.
Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks
In this paper, we theoretically analyze ANN-SNN conversion error and derive the estimated activation function of SNNs.
Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes
The experimental results show that our proposed method achieves state-of-the-art performance on CIFAR-10, CIFAR-100, and ImageNet datasets.
Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation
The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models.
Reducing ANN-SNN Conversion Error through Residual Membrane Potential
Spiking Neural Networks (SNNs) have received extensive academic attention due to the unique properties of low power consumption and high-speed computing on neuromorphic chips.
SegGPT: Towards Segmenting Everything in Context
We unify various segmentation tasks into a generalist in-context learning framework that accommodates different kinds of segmentation data by transforming them into the same format of images.
1000 FPS HDR Video With a Spike-RGB Hybrid Camera
Capturing high frame rate and high dynamic range (HFR&HDR) color videos in high-speed scenes with conventional frame-based cameras is very challenging.
MUVA: A New Large-Scale Benchmark for Multi-View Amodal Instance Segmentation in the Shopping Scenario
To bridge this gap, we propose a new task called Multi-view Amodal Instance Segmentation (MAIS) and introduce the MUVA dataset, the first MUlti-View AIS dataset that takes the shopping scenario as instantiation.
Images Speak in Images: A Generalist Painter for In-Context Visual Learning
In this work, we present Painter, a generalist model which addresses these obstacles with an "image"-centric solution, that is, to redefine the output of core vision tasks as images, and specify task prompts as also images.
Ranked #6 on
Personalized Segmentation
on PerSeg
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
We launch EVA, a vision-centric foundation model to explore the limits of visual representation at scale using only publicly accessible data.
Ranked #1 on
Object Detection
on COCO-O
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning
We investigate the use of natural language to drive the generalization of policies in multi-agent settings.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Uncertainty Guided Depth Fusion for Spike Camera
In this paper, we propose a novel Uncertainty-Guided Depth Fusion (UGDF) framework to fuse the predictions of monocular and stereo depth estimation networks for spike camera.
Unsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge Transfer
Neuromorphic spike data, an upcoming modality with high temporal resolution, has shown promising potential in autonomous driving by mitigating the challenges posed by high-velocity motion blur.
2D Amodal Instance Segmentation Guided by 3D Shape Prior
However, masks in 2D space are only some observations and samples from the 3D model in different viewpoints and thus can not represent the real complete physical shape of the instances.
Annotation Efficient Person Re-Identification with Diverse Cluster-Based Pair Selection
To solve this problem, we propose the Annotation Efficient Person Re-Identification method to select image pairs from an alternative pair set according to the fallibility and diversity of pairs, and train the Re-ID model based on the annotation.
Optimized Potential Initialization for Low-latency Spiking Neural Networks
We evaluate our algorithm on the CIFAR-10, CIFAR-100 and ImageNet datasets and achieve state-of-the-art accuracy, using fewer time-steps.
Event-based Video Reconstruction via Potential-assisted Spiking Neural Network
We propose a novel Event-based Video reconstruction framework based on a fully Spiking Neural Network (EVSNN), which utilizes Leaky-Integrate-and-Fire (LIF) neuron and Membrane Potential (MP) neuron.
Computational Efficiency
Event-Based Video Reconstruction
+2
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.
Deep Reinforcement Learning with Spiking Q-learning
With the help of special neuromorphic hardware, spiking neural networks (SNNs) are expected to realize artificial intelligence (AI) with less energy consumption.
A Robust Visual Sampling Model Inspired by Receptive Field
More importantly, due to mimicking receptive field mechanism to collect regional information, RVSM can filter high intensity noise effectively and improves the problem that Spike camera is sensitive to noise largely.
Sample Prior Guided Robust Model Learning to Suppress Noisy Labels
Imperfect labels are ubiquitous in real-world datasets and seriously harm the model performance.
Ranked #1 on
Learning with noisy labels
on CIFAR-100N
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.
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling
Inspired by BERT, we devise a Masked Point Modeling (MPM) task to pre-train point cloud Transformers.
Ranked #15 on
Few-Shot 3D Point Cloud Classification
on ModelNet40 5-way (10-shot)
(using extra training data)
3D Point Cloud Linear Classification
Few-Shot 3D Point Cloud Classification
+2
Optical Flow Estimation for Spiking Camera
Further, for training SCFlow, we synthesize two sets of optical flow data for the spiking camera, SPIkingly Flying Things and Photo-realistic High-speed Motion, denoted as SPIFT and PHM respectively, corresponding to random high-speed and well-designed scenes.
Sequential Communication in Multi-Agent Reinforcement Learning
Agents determine the priority of decision-making by comparing the value of intention.
Accelerating Training of Deep Spiking Neural Networks with Parameter Initialization
Despite that spiking neural networks (SNNs) show strong advantages in information encoding, power consuming, and computational capability, the underdevelopment of supervised learning algorithms is still a hindrance for training SNN.
Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation
Recently, many deep learning methods have shown great success in providing promising solutions to many event-based problems, such as optical flow estimation.
High-Speed Image Reconstruction Through Short-Term Plasticity for Spiking Cameras
Mimicking the sampling mechanism of the fovea, a retina-inspired camera, named spiking camera, is developed to record the external information with a sampling rate of 40, 000 Hz, and outputs asynchronous binary spike streams.
Spk2ImgNet: Learning To Reconstruct Dynamic Scene From Continuous Spike Stream
Different from the conventional digital cameras that compact the photoelectric information within the exposure interval into a single snapshot, the spike camera produces a continuous spike stream to record the dynamic light intensity variation process.
Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks
We show that the inference time can be reduced by optimizing the upper bound of the fit curve in the revised ANN to achieve fast inference.
Pruning of Deep Spiking Neural Networks through Gradient Rewiring
Our key innovation is to redefine the gradient to a new synaptic parameter, allowing better exploration of network structures by taking full advantage of the competition between pruning and regrowth of connections.
Deep Residual Learning in Spiking Neural Networks
Previous Spiking ResNet mimics the standard residual block in ANNs and simply replaces ReLU activation layers with spiking neurons, which suffers the degradation problem and can hardly implement residual learning.
Super Resolve Dynamic Scene From Continuous Spike Streams
In this paper, we properly exploit the relative motion and derive the relationship between light intensity and each spike, so as to recover the external scene with both high temporal and high spatial resolution.
NeuSpike-Net: High Speed Video Reconstruction via Bio-Inspired Neuromorphic Cameras
In this paper, we propose a NeuSpike-Net to learn both the high dynamic range and high motion sensitivity of DVS and the full texture sampling of spike camera to achieve high-speed and high dynamic image reconstruction.
UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging
A conventional camera often suffers from over- or under-exposure when recording a real-world scene with a very high dynamic range (HDR).
Learning Open Set Network with Discriminative Reciprocal Points
In this process, one of the key challenges is to reduce the risk of generalizing the inherent characteristics of numerous unknown samples learned from a small amount of known data.
Human Perception-based Evaluation Criterion for Ultra-high Resolution Cell Membrane Segmentation
Computer vision technology is widely used in biological and medical data analysis and understanding.
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.
Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks
In this paper, we take inspiration from the observation that membrane-related parameters are different across brain regions, and propose a training algorithm that is capable of learning not only the synaptic weights but also the membrane time constants of SNNs.
Learning Individually Inferred Communication for Multi-Agent Cooperation
Empirically, we show that I2C can not only reduce communication overhead but also improve the performance in a variety of multi-agent cooperative scenarios, comparing to existing methods.
Kernel Quantization for Efficient Network Compression
Thus, KQ can represent the weight tensor in the convolution layer with low-bit indexes and a kernel codebook with limited size, which enables KQ to achieve significant compression ratio.
Video Coding for Machines: A Paradigm of Collaborative Compression and Intelligent Analytics
Meanwhile, we systematically review state-of-the-art techniques in video compression and feature compression from the unique perspective of MPEG standardization, which provides the academic and industrial evidence to realize the collaborative compression of video and feature streams in a broad range of AI applications.
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.
Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning
By solving the problem with its closed-form solution on the fly with the setup of transduction, our approach efficiently tailors an episodic-wise metric for each task to adapt all features from a shared task-agnostic embedding space into a more discriminative task-specific metric space.
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.
Knowledge Transfer via Student-Teacher Collaboration
One way to compress these heavy models is knowledge transfer (KT), in which a light student network is trained through absorbing the knowledge from a powerful teacher network.
Spatiotemporal Information Processing with a Reservoir Decision-making Network
Spatiotemporal information processing is fundamental to brain functions.
A Retina-inspired Sampling Method for Visual Texture Reconstruction
Conventional frame-based camera is not able to meet the demand of rapid reaction for real-time applications, while the emerging dynamic vision sensor (DVS) can realize high speed capturing for moving objects.
P-ODN: Prototype based Open Deep Network for Open Set Recognition
Predictors for new categories are added to the classification layer to "open" the deep neural networks to incorporate new categories dynamically.
Reconstruction of Natural Visual Scenes from Neural Spikes with Deep Neural Networks
The SID is an end-to-end decoder with one end as neural spikes and the other end as images, which can be trained directly such that visual scenes are reconstructed from spikes in a highly accurate fashion.
Bi-Directional Cascade Network for Perceptual Edge Detection
Exploiting multi-scale representations is critical to improve edge detection for objects at different scales.
Ranked #2 on
Edge Detection
on BRIND
Probabilistic Inference of Binary Markov Random Fields in Spiking Neural Networks through Mean-field Approximation
Recent studies have suggested that the cognitive process of the human brain is realized as probabilistic inference and can be further modeled by probabilistic graphical models like Markov random fields.
Multi-scale 3D Convolution Network for Video Based Person Re-Identification
A temporal stream in this network is constructed by inserting several Multi-scale 3D (M3D) convolution layers into a 2D CNN network.
Revealing Fine Structures of the Retinal Receptive Field by Deep Learning Networks
When a deep CNN with many layers is used for the visual system, it is not easy to compare the structure components of CNNs with possible neuroscience underpinnings due to highly complex circuits from the retina to higher visual cortex.
Graph Convolutional Reinforcement Learning
The key is to understand the mutual interplay between agents.
Neural System Identification with Spike-triggered Non-negative Matrix Factorization
Furthermore, we show that STNMF can separate spikes of a ganglion cell into a few subsets of spikes where each subset is contributed by one presynaptic bipolar cell.
Winner-Take-All as Basic Probabilistic Inference Unit of Neuronal Circuits
Taken together, our results suggest that the WTA circuit could be seen as the minimal inference unit of neuronal circuits.
A simple blind-denoising filter inspired by electrically coupled photoreceptors in the retina
Photoreceptors in the retina are coupled by electrical synapses called "gap junctions".
Depth-Aware Stereo Video Retargeting
As compared with traditional video retargeting, stereo video retargeting poses new challenges because stereo video contains the depth information of salient objects and its time dynamics.
Exploiting Multi-Grain Ranking Constraints for Precisely Searching Visually-Similar Vehicles
In this paper, we model the relationship of vehicle images as multiple grains.
E$^2$BoWs: An End-to-End Bag-of-Words Model via Deep Convolutional Neural Network
To conquer these issues, we propose an End-to-End BoWs (E$^2$BoWs) model based on Deep Convolutional Neural Network (DCNN).
Compact Descriptors for Video Analysis: the Emerging MPEG Standard
This paper provides an overview of the on-going compact descriptors for video analysis standard (CDVA) from the ISO/IEC moving pictures experts group (MPEG).
Incorporating Intra-Class Variance to Fine-Grained Visual Recognition
In this paper, we propose to leverage intra-class variance in metric learning of triplet network to improve the performance of fine-grained recognition.
Improving Object Detection with Region Similarity Learning
Object detection aims to identify instances of semantic objects of a certain class in images or videos.
Learning long-term dependencies for action recognition with a biologically-inspired deep network
Despite a lot of research efforts devoted in recent years, how to efficiently learn long-term dependencies from sequences still remains a pretty challenging task.
Joint Network based Attention for Action Recognition
We also introduce an attention mechanism on the temporal domain to capture the long-term dependence meanwhile finding the salient portions.
Sequential Deep Trajectory Descriptor for Action Recognition with Three-stream CNN
Nevertheless, most of the existing features or descriptors cannot capture motion information effectively, especially for long-term motion.
Unsupervised Cross-Dataset Transfer Learning for Person Re-Identification
Most existing person re-identification (Re-ID) approaches follow a supervised learning framework, in which a large number of labelled matching pairs are required for training.
Deep Relative Distance Learning: Tell the Difference Between Similar Vehicles
To further facilitate the future research on this problem, we also present a carefully-organized large-scale image database "VehicleID", which includes multiple images of the same vehicle captured by different real-world cameras in a city.
