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Found 13 papers, 7 papers with code
MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation
The proposed MSRF-Net allows to capture object variabilities and provides improved results on different biomedical datasets.
Ranked #3 on
Medical Image Segmentation
on 2018 Data Science Bowl
GMSRF-Net: An improved generalizability with global multi-scale residual fusion network for polyp segmentation
The repeated fusion operations gated by CMSA and MSFS demonstrate improved generalizability of the network.
Ranked #13 on
Medical Image Segmentation
on Kvasir-SEG
Exploiting Multi-Scale Fusion, Spatial Attention and Patch Interaction Techniques for Text-Independent Writer Identification
Our methods are based on the hypothesis that handwritten text images have specific spatial regions which are more unique to a writer's style, multi-scale features propagate characteristic features with respect to individual writers and patch-based features give more general and robust representations that helps to discriminate handwriting from different writers.
EZ-CLIP: Efficient Zeroshot Video Action Recognition
Recent advancements in large-scale pre-training of visual-language models on paired image-text data have demonstrated impressive generalization capabilities for zero-shot tasks.
PLSM: A Parallelized Liquid State Machine for Unintentional Action Detection
Reservoir Computing (RC) offers a viable option to deploy AI algorithms on low-end embedded system platforms.
Pho(SC)-CTC -- A Hybrid Approach Towards Zero-shot Word Image Recognition
Annotating words in a historical document image archive for word image recognition purpose demands time and skilled human resource (like historians, paleographers).
LoOp: Looking for Optimal Hard Negative Embeddings for Deep Metric Learning
Deep metric learning has been effectively used to learn distance metrics for different visual tasks like image retrieval, clustering, etc.
Zero-Shot Learning Based Approach For Medieval Word Recognition Using Deep-Learned Features
A Zero-shot learning algorithm is capable of handling unseen classes, provided the algorithm has been fortified with rich discriminating features and reliable “attribute description” per class during training.
Semantic Text-to-Face GAN -ST^2FG
Our approach is capable of generating images that are very accurately aligned to the exhaustive textual descriptions of faces with many fine detail features of the face and helps in generating better images.
PAANet: Progressive Alternating Attention for Automatic Medical Image Segmentation
We develop progressive alternating attention dense (PAAD) blocks, which construct a guiding attention map (GAM) after every convolutional layer in the dense blocks using features from all scales.
AGA-GAN: Attribute Guided Attention Generative Adversarial Network with U-Net for Face Hallucination
The performance of facial super-resolution methods relies on their ability to recover facial structures and salient features effectively.
GradML: A Gradient-based Loss for Deep Metric Learning
Deep metric learning (ML) uses a carefully designed loss function to learn distance metrics for improving the discriminatory ability for tasks like clustering and retrieval.
Static and Dynamic Synthesis of Bengali and Devanagari Signatures
This paper aims to adapt this scheme for the generation of synthetic signatures in two Indic scripts, Bengali (Bangla), and Devanagari (Hindi).
