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Found 15 papers, 8 papers with code
Vector Quantization Prompting for Continual Learning
In this way, VQ-Prompt can optimize the prompt selection process with task loss and meanwhile achieve effective abstraction of task knowledge for continual learning.
Poly Kernel Inception Network for Remote Sensing Detection
Object detection in remote sensing images (RSIs) often suffers from several increasing challenges, including the large variation in object scales and the diverse-ranging context.
Evaluating Text-to-Image Generative Models: An Empirical Study on Human Image Synthesis
In this paper, we present an empirical study introducing a nuanced evaluation framework for text-to-image (T2I) generative models, applied to human image synthesis.
BIFRNet: A Brain-Inspired Feature Restoration DNN for Partially Occluded Image Recognition
Several studies in neuroscience reveal that feature restoration which fills in the occluded information and is called amodal completion is essential for human brains to recognize partially occluded images.
DeepSAT: An EDA-Driven Learning Framework for SAT
Based on this observation, we approximate the SAT solving procedure with a conditional generative model, leveraging a novel directed acyclic graph neural network (DAGNN) with two polarity prototypes for conditional SAT modeling.
What You See is Not What the Network Infers: Detecting Adversarial Examples Based on Semantic Contradiction
For legitimate inputs that are correctly inferred, the synthetic output tries to reconstruct the input.
T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis
In this work, we propose a novel tree-structured wavelet neural network for time series signal analysis, namely T-WaveNet, by taking advantage of an inherent property of various types of signals, known as the dominant frequency range.
Information Bottleneck Approach to Spatial Attention Learning
Extensive experiments show that the proposed IB-inspired spatial attention mechanism can yield attention maps that neatly highlight the regions of interest while suppressing backgrounds, and bootstrap standard DNN structures for visual recognition tasks (e. g., image classification, fine-grained recognition, cross-domain classification).
SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction
One unique property of time series is that the temporal relations are largely preserved after downsampling into two sub-sequences.
Ranked #1 on
Time Series Forecasting
on ETTh1 (24) Multivariate
(using extra training data)
TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks
To be specific, we first build a similarity graph on test instances and training samples, and we conduct graph-based semi-supervised learning to extract contextual features.
MixDefense: A Defense-in-Depth Framework for Adversarial Example Detection Based on Statistical and Semantic Analysis
Consequently, we propose a novel learning-based solution to model such contradictions for AE detection.
T-WaveNet: Tree-Structured Wavelet Neural Network for Sensor-Based Time Series Analysis
Motivated by the fact that usually a small subset of the frequency components carries the primary information for sensor data, we propose a novel tree-structured wavelet neural network for sensor data analysis, namely \emph{T-WaveNet}.
DeepFuse: An IMU-Aware Network for Real-Time 3D Human Pose Estimation from Multi-View Image
In this paper, we propose a two-stage fully 3D network, namely \textbf{DeepFuse}, to estimate human pose in 3D space by fusing body-worn Inertial Measurement Unit (IMU) data and multi-view images deeply.
Ranked #5 on
3D Human Pose Estimation
on Total Capture
Understanding More about Human and Machine Attention in Deep Neural Networks
With three example computer vision tasks, diverse representative backbones, and famous architectures, corresponding real human gaze data, and systematically conducted large-scale quantitative studies, we quantify the consistency between artificial attention and human visual attention and offer novel insights into existing artificial attention mechanisms by giving preliminary answers to several key questions related to human and artificial attention mechanisms.
