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Found 11 papers, 6 papers with code
Understanding Community Bias Amplification in Graph Representation Learning
In this work, we discover a phenomenon of community bias amplification in graph representation learning, which refers to the exacerbation of performance bias between different classes by graph representation learning.
StructComp: Substituting propagation with Structural Compression in Training Graph Contrastive Learning
This allows the encoder not to perform any message passing during the training stage, and significantly reduces the number of sample pairs in the contrastive loss.
Cross-Domain Product Representation Learning for Rich-Content E-Commerce
It is the first dataset to cover product pages, short videos, and live streams simultaneously, providing the basis for establishing a unified product representation across different media domains.
Cross-view Semantic Alignment for Livestreaming Product Recognition
Moreover, a cRoss-vIew semantiC alignmEnt (RICE) model is proposed to learn discriminative instance features from the image and video views of the products.
Speed Up Object Detection on Gigapixel-Level Images With Patch Arrangement
With the appearance of super high-resolution (e. g., gigapixel-level) images, performing efficient object detection on such images becomes an important issue.
Exploring the Semi-supervised Video Object Segmentation Problem from a Cyclic Perspective
We conduct comprehensive comparison and detailed analysis on challenging benchmarks of DAVIS16, DAVIS17 and Youtube-VOS, demonstrating that the cyclic mechanism is helpful to enhance segmentation quality, improve the robustness of VOS systems, and further provide qualitative comparison and interpretation on how different VOS algorithms work.
Unveiling personnel movement in a larger indoor area with a non-overlapping multi-camera system
Surveillance cameras are widely applied for indoor occupancy measurement and human movement perception, which benefit for building energy management and social security.
Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method
Despite various methods are proposed to make progress in pedestrian attribute recognition, a crucial problem on existing datasets is often neglected, namely, a large number of identical pedestrian identities in train and test set, which is not consistent with practical application.
Ranked #3 on
Pedestrian Attribute Recognition
on PETA
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
In this review, we aim to provide novel ideas and prospects in this fast-growing field as much as possible.
Towards Rich Feature Discovery With Class Activation Maps Augmentation for Person Re-Identification
The fundamental challenge of small inter-person variation requires Person Re-Identification (Re-ID) models to capture sufficient fine-grained information.
EANet: Enhancing Alignment for Cross-Domain Person Re-identification
Person re-identification (ReID) has achieved significant improvement under the single-domain setting.
