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Found 12 papers, 5 papers with code
PlugNet: Degradation Aware Scene Text Recognition Supervised by a Pluggable Super-Resolution Unit
In this paper, we address the problem of recognizing degradation images that are suffering from high blur or low-resolution.
Prompt Decoupling for Text-to-Image Person Re-identification
In the first stage, we freeze the two encoders from CLIP and solely focus on optimizing the prompts to alleviate domain gap between the original training data of CLIP and downstream tasks.
Scientific Preparation for CSST: Classification of Galaxy and Nebula/Star Cluster Based on Deep Learning
Real-time identification of galaxy and nebula/star cluster (abbreviated as NSC) images is of great value during CSST survey.
Key Issues in Wireless Transmission for NTN-Assisted Internet of Things
The long-distance wireless signal propagation in NTNs leads to severe path loss and large latency, where the accurate acquisition of channel state information (CSI) is another challenge, especially for fast-moving non-terrestrial base stations (NTBSs).
Attention Disturbance and Dual-Path Constraint Network for Occluded Person Re-identification
To address this issue, we propose a novel transformer-based Attention Disturbance and Dual-Path Constraint Network (ADP) to enhance the generalization of attention networks.
Spectral Aware Softmax for Visible-Infrared Person Re-Identification
Based on the observation and analysis of SA-Softmax, we modify the SA-Softmax with the Feature Mask and Absolute-Similarity Term to alleviate the ambiguous optimization during model training.
Exploring Invariant Representation for Visible-Infrared Person Re-Identification
Cross-spectral person re-identification, which aims to associate identities to pedestrians across different spectra, faces a main challenge of the modality discrepancy.
Dynamic Prototype Mask for Occluded Person Re-Identification
Although person re-identification has achieved an impressive improvement in recent years, the common occlusion case caused by different obstacles is still an unsettled issue in real application scenarios.
Weighted Neural Tangent Kernel: A Generalized and Improved Network-Induced Kernel
In this paper, we introduce the Weighted Neural Tangent Kernel (WNTK), a generalized and improved tool, which can capture an over-parameterized NN's training dynamics under different optimizers.
Low Dimensional Landscape Hypothesis is True: DNNs can be Trained in Tiny Subspaces
Deep neural networks (DNNs) usually contain massive parameters, but there is redundancy such that it is guessed that the DNNs could be trained in low-dimensional subspaces.
Panoptic Studio: A Massively Multiview System for Social Interaction Capture
The core challenges in capturing social interactions are: (1) occlusion is functional and frequent; (2) subtle motion needs to be measured over a space large enough to host a social group; (3) human appearance and configuration variation is immense; and (4) attaching markers to the body may prime the nature of interactions.
Panoptic Studio: A Massively Multiview System for Social Motion Capture
We present an approach to capture the 3D structure and motion of a group of people engaged in a social interaction.
