2 citations · 3 across the 7 of their papers we have counts for
7 papers
Indirect Attention: Turning Context Misalignment into a Feature
Bissmella Bahaduri, Hicham Talaoubrid, Fangchen Feng +2
The attention mechanism has become a cornerstone of modern deep learning architectures, where keys and values are typically derived from the same underlying sequence or representat…
Analyzing the Impact of Low-Rank Adaptation for Cross-Domain Few-Shot Object Detection in Aerial Images
Hicham Talaoubrid, Anissa Mokraoui, Ismail Ben Ayed +4
This paper investigates the application of Low-Rank Adaptation (LoRA) to small models for cross-domain few-shot object detection in aerial images. Originally designed for large-sca…
Interactive Masked Image Modeling for Multimodal Object Detection in Remote Sensing
Minh-Duc Vu, Zuheng Ming, Fangchen Feng +2
Object detection in remote sensing imagery plays a vital role in various Earth observation applications. However, unlike object detection in natural scene images, this task is part…
Convolutional Transformer-Based Image Compression
Bouzid Arezki, Fangchen Feng, Anissa Mokraoui
In this paper, we present a novel transformer-based architecture for end-to-end image compression. Our architecture incorporates blocks that effectively capture local dependencies…
A Comparative Attention Framework for Better Few-Shot Object Detection on Aerial Images
Pierre Le Jeune, Anissa Mokraoui
Few-Shot Object Detection (FSOD) methods are mainly designed and evaluated on natural image datasets such as Pascal VOC and MS COCO. However, it is not clear whether the best metho…
Experience feedback using Representation Learning for Few-Shot Object Detection on Aerial Images
Pierre Le Jeune, Mustapha Lebbah, Anissa Mokraoui +1
This paper proposes a few-shot method based on Faster R-CNN and representation learning for object detection in aerial images. The two classification branches of Faster R-CNN are r…