2 citations · 6 across the 11 of their papers we have counts for
13 papers
FocusOVD: Geometric and Contextual Feature Refinement for Open-Vocabulary Small-Object Detection
Hanlin Kou, Beiqi Bai, Li Chen +3
Open-vocabulary object detection aims to detect both seen and unseen categories by leveraging the semantic knowledge of vision-language models. However, existing methods often stru…
Multi-label Classification with Panoptic Context Aggregation Networks
Mingyuan Jiu, Hailong Zhu, Wenchuan Wei +3
Context modeling is crucial for visual recognition, enabling highly discriminative image representations by integrating both intrinsic and extrinsic relationships between objects a…
Image Classification with Deep Reinforcement Active Learning
Mingyuan Jiu, Xuguang Song, Hichem Sahbi +5
Deep learning is currently reaching outstanding performances on different tasks, including image classification, especially when using large neural networks. The success of these m…
Multi-label Classification using Deep Multi-order Context-aware Kernel Networks
Mingyuan Jiu, Hailong Zhu, Hichem Sahbi
Multi-label classification is a challenging task in pattern recognition. Many deep learning methods have been proposed and largely enhanced classification performance. However, mos…
Few-Shot Object Detection with Sparse Context Transformers
Jie Mei, Mingyuan Jiu, Hichem Sahbi +2
Few-shot detection is a major task in pattern recognition which seeks to localize objects using models trained with few labeled data. One of the mainstream few-shot methods is tran…
Alternative design of DeepPDNet in the context of image restoration
Mingyuan Jiu, Nelly Pustelnik
This work designs an image restoration deep network relying on unfolded Chambolle-Pock primal-dual iterations. Each layer of our network is built from Chambolle-Pock iterations whe…
Image Annotation based on Deep Hierarchical Context Networks
Mingyuan Jiu, Hichem Sahbi
Context modeling is one of the most fertile subfields of visual recognition which aims at designing discriminant image representations while incorporating their intrinsic and extri…
A deep primal-dual proximal network for image restoration
Mingyuan Jiu, Nelly Pustelnik
Image restoration remains a challenging task in image processing. Numerous methods tackle this problem, often solved by minimizing a non-smooth penalized co-log-likelihood function…
End-to-end training of deep kernel map networks for image classification
Mingyuan Jiu, Hichem Sahbi
Deep kernel map networks have shown excellent performances in various classification problems including image annotation. Their general recipe consists in aggregating several layer…
Deep Context-Aware Kernel Networks
Mingyuan Jiu, Hichem Sahbi
Context plays a crucial role in visual recognition as it provides complementary clues for different learning tasks including image classification and annotation. As the performance…
Learning Explicit Deep Representations from Deep Kernel Networks
Mingyuan Jiu, Hichem Sahbi
Deep kernel learning aims at designing nonlinear combinations of multiple standard elementary kernels by training deep networks. This scheme has proven to be effective, but intract…
Learning Deep Context-Network Architectures for Image Annotation
Mingyuan Jiu, Hichem Sahbi
Context plays an important role in visual pattern recognition as it provides complementary clues for different learning tasks including image classification and annotation. In the…
Sparse hierarchical interaction learning with epigraphical projection
Mingyuan Jiu, Nelly Pustelnik, Stefan Janaqi +3
This work focuses on learning optimization problems with quadratical interactions between variables, which go beyond the additive models of traditional linear learning. We investig…