Search Results for author:
Found 13 papers, 8 papers with code
Progressive Fine-to-Coarse Reconstruction for Accurate Low-Bit Post-Training Quantization in Vision Transformers
To this end, in this paper, we propose a Progressive Fine-to-Coarse Reconstruction (PFCR) method for accurate PTQ, which significantly improves the performance of low-bit quantized vision transformers.
STOP: Spatiotemporal Orthogonal Propagation for Weight-Threshold-Leakage Synergistic Training of Deep Spiking Neural Networks
The prevailing of artificial intelligence-of-things calls for higher energy-efficient edge computing paradigms, such as neuromorphic agents leveraging brain-inspired spiking neural network (SNN) models based on spatiotemporally sparse binary spikes.
EigenSR: Eigenimage-Bridged Pre-Trained RGB Learners for Single Hyperspectral Image Super-Resolution
In recent years, research on RGB SR has shown that models pre-trained on large-scale benchmark datasets can greatly improve performance on unseen data, which may stand as a remedy for HSI.
A survey of Transformer applications for histopathological image analysis: New developments and future directions
Transformers have been widely used in many computer vision challenges and have shown the capability of producing better results than convolutional neural networks (CNNs).
BiGSeT: Binary Mask-Guided Separation Training for DNN-based Hyperspectral Anomaly Detection
Our method introduces a separation training loss based on a latent binary mask to separately constrain the background and anomalies in the estimated image.
Strong but Simple Baseline with Dual-Granularity Triplet Loss for Visible-Thermal Person Re-Identification
In this letter, we propose a conceptually simple and effective dual-granularity triplet loss for visible-thermal person re-identification (VT-ReID).
Ranked #2 on
Cross-Modal Person Re-Identification
on RegDB
Parameter Sharing Exploration and Hetero-Center based Triplet Loss for Visible-Thermal Person Re-Identification
By well splitting the ResNet50 model to construct the modality-specific feature extracting network and modality-sharing feature embedding network, we experimentally demonstrate the effect of parameters sharing of two-stream network for VT Re-ID.
Ranked #1 on
Cross-Modal Person Re-Identification
on RegDB
Cross-Modality Person Re-identification
Cross-Modal Person Re-Identification
+1
MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time
Monocular multi-object detection and localization in 3D space has been proven to be a challenging task.
Neural Network Activation Quantization with Bitwise Information Bottlenecks
Recent researches on information bottleneck shed new light on the continuous attempts to open the black box of neural signal encoding.
Probability Weighted Compact Feature for Domain Adaptive Retrieval
Most of the existing image retrieval methods only focus on single-domain retrieval, which assumes that the distributions of retrieval databases and queries are similar.
Two-Phase Object-Based Deep Learning for Multi-temporal SAR Image Change Detection
The prediction of this Phase is the set of changed and unchanged superpixels.
When Single Event Upset Meets Deep Neural Networks: Observations, Explorations, and Remedies
We are then able to analytically explore the weakness of a network and summarize the key findings for the impact of SIPP on different types of bits in a floating point parameter, layer-wise robustness within the same network and impact of network depth.
Deep Adaptive Network: An Efficient Deep Neural Network with Sparse Binary Connections
Deep neural networks are state-of-the-art models for understanding the content of images, video and raw input data.
