Mustansar Fiaz's repositories
ScratchFormer
ScratchFormer: Remote Sensing Change Detection With Transformers Trained from Scratch
PS-ARM
Abstract. Person search is a challenging problem with various real- world applications, that aims at joint person detection and re-identification of a query person from uncropped gallery images. Although, previous study focuses on rich feature information learning, it’s still hard to re- trieve the query person due to the occurrence of appearance deformations and background distractors. In this paper, we propose a novel attention- aware relation mixer (ARM) module for person search, which exploits the global relation between different local regions within RoI of a per- son and make it robust against various appearance deformations and occlusion. The proposed ARM is composed of a relation mixer block and a spatio-channel attention layer. The relation mixer block introduces a spatially attended spatial mixing and a channel-wise attended channel mixing for effectively capturing discriminative relation features within an RoI. These discriminative relation features are further enriched by intro- ducing a spatio-channel attention where the foreground and background discriminability is empowered in a joint spatio-channel space. Our ARM module is generic and it does not rely on fine-grained supervisions or topological assumptions, hence being easily integrated into any Faster R-CNN based person search methods. Comprehensive experiments are performed on two challenging benchmark datasets: CUHK-SYSU [1] and PRW [2]. Our PS-ARM achieves state-of-the-art performance on both datasets. On the challenging PRW dataset, our PS-ARM achieves an absolute gain of 5% in the mAP score over SeqNet, while operating at a comparable speed
SiamTrackers
(2020)The PyTorch version of Siamese ,SiamFC,SiamRPN,DaSiamRPN,UpdateNet,SiamDW,SiamRPN++, SiamMask,and SiamFC++ ; Visual object tracking based on deep learning
Awesome-Transformer-Attention
An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites
benchmark_results
Visual Tracking Paper List
Directional-Deep-Embedding-and-Appearance-Learning-for-Fast-Video-Object-Segmentation
We propose a directional deep embedding and appearance learning (DDEAL) method, which is free of the online fine-tuning process, for fast VOS. DDEAL achieves a J & F mean score of 74.8% on DAVIS 2017 dataset and an overall score G of 71.3% on the large-scale YouTube-VOS dataset, while retaining a speed of 25 fps with a single NVIDIA TITAN Xp GPU. Furthermore, our faster version runs 31 fps with only a little accuracy loss.
Computer-Vision-Video-Lectures
A curated list of free, high-quality, university-level courses with video lectures related to the field of Computer Vision.
elgcnet
ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection
HyRect-Change
HYRET-CHANGE: A HYBRID RETENTIVE NETWORK FOR REMOTE SENSING CHANGE DETECTION
ThirdParty
Modifications to third party software used by UE4