Search Results for author:
Found 11 papers, 5 papers with code
Towards Generative Class Prompt Learning for Fine-grained Visual Recognition
Specifically, we propose two novel methods: Generative Class Prompt Learning (GCPL) and Contrastive Multi-class Prompt Learning (CoMPLe).
Active Learning for Fine-Grained Sketch-Based Image Retrieval
The ability to retrieve a photo by mere free-hand sketching highlights the immense potential of Fine-grained sketch-based image retrieval (FG-SBIR).
Dynamically Scaled Temperature in Self-Supervised Contrastive Learning
In contemporary self-supervised contrastive algorithms like SimCLR, MoCo, etc., the task of balancing attraction between two semantically similar samples and repulsion between two samples of different classes is primarily affected by the presence of hard negative samples.
BeAts: Bengali Speech Acts Recognition using Multimodal Attention Fusion
Spoken languages often utilise intonation, rhythm, intensity, and structure, to communicate intention, which can be interpreted differently depending on the rhythm of speech of their utterance.
Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR
This paper advances the fine-grained sketch-based image retrieval (FG-SBIR) literature by putting forward a strong baseline that overshoots prior state-of-the-arts by ~11%.
An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis
In this paper, we seek to tackle these concerns head-on and systematically explore the applicability of non-contrastive self-supervised learning (SSL) algorithms under federated learning (FL) simulations for medical image analysis.
Exploring Self-Supervised Representation Learning For Low-Resource Medical Image Analysis
The success of self-supervised learning (SSL) has mostly been attributed to the availability of unlabeled yet large-scale datasets.
IDEAL: Improved DEnse locAL Contrastive Learning for Semi-Supervised Medical Image Segmentation
To this end, we extend the concept of metric learning to the segmentation task, using a dense (dis)similarity learning for pre-training a deep encoder network, and employing a semi-supervised paradigm to fine-tune for the downstream task.
SWIS: Self-Supervised Representation Learning For Writer Independent Offline Signature Verification
Writer independent offline signature verification is one of the most challenging tasks in pattern recognition as there is often a scarcity of training data.
A Brief Overview of Physics-inspired Metaheuristic Optimization Techniques
Metaheuristic algorithms are methods devised to efficiently solve computationally challenging optimization problems.
SURDS: Self-Supervised Attention-guided Reconstruction and Dual Triplet Loss for Writer Independent Offline Signature Verification
This results in robust discriminative learning of the embedding space.
