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Found 26 papers, 14 papers with code
Let All be Whitened: Multi-teacher Distillation for Efficient Visual Retrieval
Instead of crafting a new method pursuing further improvement on accuracy, in this paper we propose a multi-teacher distillation framework Whiten-MTD, which is able to transfer knowledge from off-the-shelf pre-trained retrieval models to a lightweight student model for efficient visual retrieval.
Ternary Spike: Learning Ternary Spikes for Spiking Neural Networks
To handle the problem, we propose a ternary spike neuron to transmit information.
Robust Visual Imitation Learning with Inverse Dynamics Representations
Imitation learning (IL) has achieved considerable success in solving complex sequential decision-making problems.
Video Infringement Detection via Feature Disentanglement and Mutual Information Maximization
We theoretically analyzed the mutual information between the label and the disentangled features, arriving at a loss that maximizes the extraction of task-relevant information from the original feature.
Membrane Potential Batch Normalization for Spiking Neural Networks
All these BNs are suggested to be used after the convolution layer as usually doing in CNNs.
RMP-Loss: Regularizing Membrane Potential Distribution for Spiking Neural Networks
Spiking Neural Networks (SNNs) as one of the biology-inspired models have received much attention recently.
InfLoR-SNN: Reducing Information Loss for Spiking Neural Networks
The Spiking Neural Network (SNN) has attracted more and more attention recently.
NeuroCLIP: Neuromorphic Data Understanding by CLIP and SNN
However, the neuromorphic data consists of asynchronous event spikes, which makes it difficult to construct a big benchmark to train a power general neural network model, thus limiting the neuromorphic data understanding for ``unseen" objects by deep learning.
Direct Learning-Based Deep Spiking Neural Networks: A Review
The spiking neural network (SNN), as a promising brain-inspired computational model with binary spike information transmission mechanism, rich spatially-temporal dynamics, and event-driven characteristics, has received extensive attention.
From Region to Patch: Attribute-Aware Foreground-Background Contrastive Learning for Fine-Grained Fashion Retrieval
As the attribute-specific similarity typically corresponds to the specific subtle regions of images, we propose a Region-to-Patch Framework (RPF) that consists of a region-aware branch and a patch-aware branch to extract fine-grained attribute-related visual features for precise retrieval in a coarse-to-fine manner.
Joint A-SNN: Joint Training of Artificial and Spiking Neural Networks via Self-Distillation and Weight Factorization
In this paper, we propose a joint training framework of ANN and SNN, in which the ANN can guide the SNN's optimization.
Model-Driven Deep Learning for Non-Coherent Massive Machine-Type Communications
Trainable parameters are introduced in the DL-mAMPnet to approximate the correlated sparsity pattern and the large-scale fading coefficient.
PeakConv: Learning Peak Receptive Field for Radar Semantic Segmentation
However, neither the regular convolution operation nor the modified ones are specific to interpret radar signals.
Deep Dive Into Gradients: Better Optimization for 3D Object Detection With Gradient-Corrected IoU Supervision
In this paper, we propose a Gradient-Corrected IoU (GCIoU) loss to achieve fast and accurate 3D bounding box regression.
Alleviating Catastrophic Forgetting of Incremental Object Detection via Within-Class and Between-Class Knowledge Distillation
However, previous works on feature distillation heavily rely on low-level feature information, while under-exploring the importance of high-level semantic information.
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.
Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Report
In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based bokeh effect rendering approach that can run on modern smartphone GPUs using TensorFlow Lite.
IM-Loss: Information Maximization Loss for Spiking Neural Networks
To deal with this problem, the Information maximization loss (IM-Loss) that aims at maximizing the information flow in the SNN is proposed in the paper.
Ranked #6 on
Event data classification
on CIFAR10-DVS
Real Spike: Learning Real-valued Spikes for Spiking Neural Networks
Motivated by this assumption, a training-inference decoupling method for SNNs named as Real Spike is proposed, which not only enjoys both unshared convolution kernels and binary spikes in inference-time but also maintains both shared convolution kernels and Real-valued Spikes during training.
NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results
The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).
Efficient Progressive High Dynamic Range Image Restoration via Attention and Alignment Network
In this paper, we propose a lightweight neural network called Efficient Attention-and-alignment-guided Progressive Network (EAPNet) for the challenge NTIRE 2022 HDR Track 1 and Track 2.
RecDis-SNN: Rectifying Membrane Potential Distribution for Directly Training Spiking Neural Networks
Unfortunately, with the propagation of binary spikes, the distribution of membrane potential will shift, leading to degeneration, saturation, and gradient mismatch problems, which would be disadvantageous to the network optimization and convergence.
ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learning
Specifically, we first employ the comparison module to explore the pairwise sample relations to learn rich sample representations in the instance-level graph.
Fine-Grained Fashion Similarity Prediction by Attribute-Specific Embedding Learning
In this similarity paradigm, one should pay more attention to the similarity in terms of a specific design/attribute between fashion items.
Hierarchical Similarity Learning for Language-based Product Image Retrieval
In this paper, we focus on the cross-modal similarity measurement, and propose a novel Hierarchical Similarity Learning (HSL) network.
Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network
This paper strives to learn fine-grained fashion similarity.
