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Found 21 papers, 12 papers with code
Toward Realistic Camouflaged Object Detection: Benchmarks and Method
Since camouflaged objects are not rare categories, CAFR fully utilizes a clear perception of the current object within the prior knowledge of large models to assist detectors in deeply understanding the distinctions between background and foreground.
Visual-Semantic Graph Matching Net for Zero-Shot Learning
However, methods trained under this paradigm often struggle to learn robust embedding space because they align the two modalities in an isolated manner among classes, which ignore the crucial class relationship during the alignment process.
Bias in Large Language Models: Origin, Evaluation, and Mitigation
This comprehensive review examines the landscape of bias in LLMs, from its origins to current mitigation strategies.
UP-Diff: Latent Diffusion Model for Remote Sensing Urban Prediction
This study introduces a novel Remote Sensing (RS) Urban Prediction (UP) task focused on future urban planning, which aims to forecast urban layouts by utilizing information from existing urban layouts and planned change maps.
Take A Shortcut Back: Mitigating the Gradient Vanishing for Training Spiking Neural Networks
However, training an SNN directly poses a challenge due to the undefined gradient of the firing spike process.
Ternary Spike: Learning Ternary Spikes for Spiking Neural Networks
To handle the problem, we propose a ternary spike neuron to transmit information.
Spiking PointNet: Spiking Neural Networks for Point Clouds
We discover that the two huge obstacles limiting the application of SNNs in point clouds are: the intrinsic optimization obstacle of SNNs that impedes the training of a big spiking model with large time steps, and the expensive memory and computation cost of PointNet that makes training a big spiking point model unrealistic.
Do Large Language Models Know about Facts?
Large language models (LLMs) have recently driven striking performance improvements across a range of natural language processing tasks.
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.
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.
GPT-4 Technical Report
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
Ranked #1 on
Legal Reasoning
on LegalBench (Rule-recall)
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.
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.
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.
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.
Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks
Based on the introduced finite difference gradient, we propose a new family of Differentiable Spike (Dspike) functions that can adaptively evolve during training to find the optimal shape and smoothness for gradient estimation.
Ranked #4 on
Event data classification
on CIFAR10-DVS
