Search Results for author:
Found 9 papers, 5 papers with code
ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection
Deep learning has shown remarkable success in remote sensing change detection (CD), aiming to identify semantic change regions between co-registered satellite image pairs acquired at distinct time stamps.
DDAM-PS: Diligent Domain Adaptive Mixer for Person Search
The objective of the two bridge losses is to guide the moderate mixed-domain representations to maintain an appropriate distance from both the source and target domain representations.
SA2-Net: Scale-aware Attention Network for Microscopic Image Segmentation
Specifically, we propose scale-aware attention (SA2) module designed to capture inherent variations in scales and shapes of microscopic regions, such as cells, for accurate segmentation.
Remote Sensing Change Detection With Transformers Trained from Scratch
Current transformer-based change detection (CD) approaches either employ a pre-trained model trained on large-scale image classification ImageNet dataset or rely on first pre-training on another CD dataset and then fine-tuning on the target benchmark.
PS-ARM: An End-to-End Attention-aware Relation Mixer Network for Person Search
Our PS-ARM achieves state-of-the-art performance on both datasets.
Brain MRI Segmentation using Rule-Based Hybrid Approach
Performance of these classifiers is investigated over different images of brain MRI and the variation in the performance of these classifiers is observed for different brain tissues.
Handcrafted and Deep Trackers: Recent Visual Object Tracking Approaches and Trends
In order to overcome the drawbacks of the existing benchmarks, a new benchmark Object Tracking and Temple Color (OTTC) has also been proposed and used in the evaluation of different algorithms.
Tracking Noisy Targets: A Review of Recent Object Tracking Approaches
In the second part of this work, we experimentally evaluate tracking algorithms for robustness in the presence of additive white Gaussian noise.
Comparative Study of ECO and CFNet Trackers in Noisy Environment
Object tracking is one of the most challenging task and has secured significant attention of computer vision researchers in the past two decades.
