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08/14/2023
Knowledge Prompt-tuning for Sequential Recommendation
Pre-trained language models (PLMs) have demonstrated strong performance ...
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08/08/2023
SSTFormer: Bridging Spiking Neural Network and Memory Support Transformer for Frame-Event based Recognition
Event camera-based pattern recognition is a newly arising research topic...
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08/08/2023
SODFormer: Streaming Object Detection with Transformer Using Events and Frames
DAVIS camera, streaming two complementary sensing modalities of asynchro...
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07/14/2023
Learning Sparse Neural Networks with Identity Layers
The sparsity of Deep Neural Networks is well investigated to maximize th...
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07/04/2023
Spike-driven Transformer
Spiking Neural Networks (SNNs) provide an energy-efficient deep learning...
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06/08/2023
Point-Voxel Absorbing Graph Representation Learning for Event Stream based Recognition
Sampled point and voxel methods are usually employed to downsample the d...
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06/08/2023
Population-Based Evolutionary Gaming for Unsupervised Person Re-identification
Unsupervised person re-identification has achieved great success through...
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06/02/2023
Deep recurrent spiking neural networks capture both static and dynamic representations of the visual cortex under movie stimuli
In the real world, visual stimuli received by the biological visual syst...
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06/01/2023
Auto-Spikformer: Spikformer Architecture Search
The integration of self-attention mechanisms into Spiking Neural Network...
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05/23/2023
Temporal Contrastive Learning for Spiking Neural Networks
Biologically inspired spiking neural networks (SNNs) have garnered consi...
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05/22/2023
Album Storytelling with Iterative Story-aware Captioning and Large Language Models
This work studies how to transform an album to vivid and coherent storie...
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05/20/2023
Probabilistic Modeling: Proving the Lottery Ticket Hypothesis in Spiking Neural Network
The Lottery Ticket Hypothesis (LTH) states that a randomly-initialized l...
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05/10/2023
Enhancing the Performance of Transformer-based Spiking Neural Networks by SNN-optimized Downsampling with Precise Gradient Backpropagation
Deep spiking neural networks (SNNs) have drawn much attention in recent ...
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04/25/2023
Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies
Vanilla spiking neurons in Spiking Neural Networks (SNNs) use charge-fir...
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04/24/2023
Spikingformer: Spike-driven Residual Learning for Transformer-based Spiking Neural Network
Spiking neural networks (SNNs) offer a promising energy-efficient altern...
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04/21/2023
Picking Up Quantization Steps for Compressed Image Classification
The sensitivity of deep neural networks to compressed images hinders the...
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04/02/2023
Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) aims at learning to classify...
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03/09/2023
Deep Spiking Neural Networks with High Representation Similarity Model Visual Pathways of Macaque and Mouse
Deep artificial neural networks (ANNs) play a major role in modeling the...
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02/25/2023
A Unified Framework for Soft Threshold Pruning
Soft threshold pruning is among the cutting-edge pruning methods with st...
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02/20/2023
Large-scale Multi-Modal Pre-trained Models: A Comprehensive Survey
With the urgent demand for generalized deep models, many pre-trained big...
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01/27/2023
Training Full Spike Neural Networks via Auxiliary Accumulation Pathway
Due to the binary spike signals making converting the traditional high-p...
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01/18/2023
MADAv2: Advanced Multi-Anchor Based Active Domain Adaptation Segmentation
Unsupervised domain adaption has been widely adopted in tasks with scarc...
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12/28/2022
Parsing Objects at a Finer Granularity: A Survey
Fine-grained visual parsing, including fine-grained part segmentation an...
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12/28/2022
Part-guided Relational Transformers for Fine-grained Visual Recognition
Fine-grained visual recognition is to classify objects with visually sim...
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12/19/2022
Million-scale Object Detection with Large Vision Model
Over the past few years, developing a broad, universal, and general-purp...
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12/06/2022
Event-based Monocular Dense Depth Estimation with Recurrent Transformers
Event cameras, offering high temporal resolutions and high dynamic range...
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11/26/2022
Meta Architecure for Point Cloud Analysis
Recent advances in 3D point cloud analysis bring a diverse set of networ...
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11/20/2022
Revisiting Color-Event based Tracking: A Unified Network, Dataset, and Metric
Combining the Color and Event cameras (also called Dynamic Vision Sensor...
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11/17/2022
HARDVS: Revisiting Human Activity Recognition with Dynamic Vision Sensors
The main streams of human activity recognition (HAR) algorithms are deve...
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11/02/2022
Unsupervised Deraining: Where Asymmetric Contrastive Learning Meets Self-similarity
Most of the existing learning-based deraining methods are supervisedly t...
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09/29/2022
Spikformer: When Spiking Neural Network Meets Transformer
We consider two biologically plausible structures, the Spiking Neural Ne...
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09/28/2022
Attention Spiking Neural Networks
Benefiting from the event-driven and sparse spiking characteristics of t...
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03/30/2022
Fine-Grained Object Classification via Self-Supervised Pose Alignment
Semantic patterns of fine-grained objects are determined by subtle appea...
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03/23/2022
Training-free Transformer Architecture Search
Recently, Vision Transformer (ViT) has achieved remarkable success in se...
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03/10/2022
Annotation Efficient Person Re-Identification with Diverse Cluster-Based Pair Selection
Person Re-identification (Re-ID) has attracted great attention due to it...
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03/04/2022
Transformations in Learned Image Compression from a Modulation Perspective
In this paper, a unified transformation method in learned image compress...
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01/25/2022
PowerGear: Early-Stage Power Estimation in FPGA HLS via Heterogeneous Edge-Centric GNNs
Power estimation is the basis of many hardware optimization strategies. ...
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01/25/2022
Event-based Video Reconstruction via Potential-assisted Spiking Neural Network
Neuromorphic vision sensor is a new bio-inspired imaging paradigm that r...
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01/21/2022
Deep Reinforcement Learning with Spiking Q-learning
With the help of special neuromorphic hardware, spiking neural networks ...
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11/17/2021
IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Shot Network Quantization
Learning to synthesize data has emerged as a promising direction in zero...
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09/16/2021
Heterogeneous Relational Complement for Vehicle Re-identification
The crucial problem in vehicle re-identification is to find the same veh...
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09/12/2021
Prioritized Subnet Sampling for Resource-Adaptive Supernet Training
A resource-adaptive supernet adjusts its subnets for inference to fit th...
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08/19/2021
An Information Theory-inspired Strategy for Automatic Network Pruning
Despite superior performance on many computer vision tasks, deep convolu...
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08/19/2021
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain
Recently, the generalization behavior of Convolutional Neural Networks (...
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07/14/2021
Training Compact CNNs for Image Classification using Dynamic-coded Filter Fusion
The mainstream approach for filter pruning is usually either to force a ...
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05/25/2021
Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks
Spiking Neural Networks (SNNs), as bio-inspired energy-efficient neural ...
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05/11/2021
Pruning of Deep Spiking Neural Networks through Gradient Rewiring
Spiking Neural Networks (SNNs) have been attached great importance due t...
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04/24/2021
Carrying out CNN Channel Pruning in a White Box
Channel Pruning has been long adopted for compressing CNNs, which signif...
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04/18/2021
Lottery Jackpots Exist in Pre-trained Models
Network pruning is an effective approach to reduce network complexity wi...
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04/10/2021