Search Results for author:
Found 31 papers, 18 papers with code
Backpropagation-free Network for 3D Test-time Adaptation
Here, we propose a novel method that uses a backpropagation-free approach for TTA for the specific case of 3D data.
Spatial Transcriptomics Analysis of Zero-shot Gene Expression Prediction
Spatial transcriptomics (ST) captures gene expression within distinct regions (i. e., windows) of a tissue slide.
ChatGPT-guided Semantics for Zero-shot Learning
Then, we enrich word vectors by combining the word embeddings from class names and descriptions generated by ChatGPT.
Continual Test-time Domain Adaptation via Dynamic Sample Selection
DSS consists of dynamic thresholding, positive learning, and negative learning processes.
LumiNet: The Bright Side of Perceptual Knowledge Distillation
In knowledge distillation literature, feature-based methods have dominated due to their ability to effectively tap into extensive teacher models.
Ranked #1 on
Classification
on CIFAR-100
Uncertainty Aware Neural Network from Similarity and Sensitivity
In the proposed NN training method for UQ, first, we train a shallow NN for the point prediction.
COLT: Cyclic Overlapping Lottery Tickets for Faster Pruning of Convolutional Neural Networks
This research aims to generate winning lottery tickets from a set of lottery tickets that can achieve similar accuracy to the original unpruned network.
Exemplar Guided Deep Neural Network for Spatial Transcriptomics Analysis of Gene Expression Prediction
This paper proposes an Exemplar Guided Network (EGN) to accurately and efficiently predict gene expression directly from each window of a tissue slide image.
Less is More: Facial Landmarks can Recognize a Spontaneous Smile
Smile veracity classification is a task of interpreting social interactions.
Prompt-guided Scene Generation for 3D Zero-Shot Learning
To address this problem, we propose a prompt-guided 3D scene generation and supervision method that augments 3D data to learn the network better, exploring the complex interplay of seen and unseen objects.
Few-shot Class-incremental Learning for 3D Point Cloud Objects
Few-shot class-incremental learning (FSCIL) aims to incrementally fine-tune a model (trained on base classes) for a novel set of classes using a few examples without forgetting the previous training.
Rethinking Task-Incremental Learning Baselines
A common goal of task-incremental methods is to design a network that can operate on minimal size, maintaining decent performance.
DPST: De Novo Peptide Sequencing with Amino-Acid-Aware Transformers
De novo peptide sequencing aims to recover amino acid sequences of a peptide from tandem mass spectrometry (MS) data.
Learning without Forgetting for 3D Point Cloud Objects
Experimenting on three 3D point cloud recognition backbones (PointNet, DGCNN, and PointConv) and synthetic (ModelNet40, ModelNet10) and real scanned (ScanObjectNN) datasets, we establish new baseline results on learning without forgetting for 3D data.
Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning
Few-shot class incremental learning (FSCIL) portrays the problem of learning new concepts gradually, where only a few examples per concept are available to the learner.
Synthesized Feature Based Few-Shot Class-Incremental Learning on a Mixture of Subspaces
In this paper, we propose addressing this problem using a mixture of subspaces.
S2FGAN: Semantically Aware Interactive Sketch-to-Face Translation
Instead of constraining the translation process by using a reference image, the users can command the model to retouch the generated images by involving the semantic information in the generation process.
Classifying Eye-Tracking Data Using Saliency Maps
A plethora of research in the literature shows how human eye fixation pattern varies depending on different factors, including genetics, age, social functioning, cognitive functioning, and so on.
Synthesizing the Unseen for Zero-shot Object Detection
The existing zero-shot detection approaches project visual features to the semantic domain for seen objects, hoping to map unseen objects to their corresponding semantics during inference.
Ranked #1 on
Zero-Shot Object Detection
on ImageNet Detection
RealSmileNet: A Deep End-To-End Network for Spontaneous and Posed Smile Recognition
Smiles play a vital role in the understanding of social interactions within different communities, and reveal the physical state of mind of people in both real and deceptive ways.
Any-Shot Object Detection
Any-shot detection offers unique challenges compared to conventional novel object detection such as, a high imbalance between unseen, few-shot and seen object classes, susceptibility to forget base-training while learning novel classes and distinguishing novel classes from the background.
Transductive Zero-Shot Learning for 3D Point Cloud Classification
This paper extends, for the first time, transductive Zero-Shot Learning (ZSL) and Generalized Zero-Shot Learning (GZSL) approaches to the domain of 3D point cloud classification.
Transductive Learning for Zero-Shot Object Detection
To the best of our knowledge, we are the first to propose a transductive zero-shot object detection approach that convincingly reduces the domain-shift and model-bias against unseen classes.
Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects
In this paper, we therefore propose a loss to specifically address the hubness problem.
Zero-shot Learning of 3D Point Cloud Objects
A challenge for a 3D point cloud recognition system is, then, to classify objects from new, unseen, classes.
Polarity Loss for Zero-shot Object Detection
This setting gives rise to the need for correct alignment between visual and semantic concepts, so that the unseen objects can be identified using only their semantic attributes.
Ranked #5 on
Zero-Shot Object Detection
on PASCAL VOC'07
Task-generalizable Adversarial Attack based on Perceptual Metric
Deep neural networks (DNNs) can be easily fooled by adding human imperceptible perturbations to the images.
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts
We hypothesize that this setting is ill-suited for real-world applications where unseen objects appear only as a part of a complex scene, warranting both the `recognition' and `localization' of an unseen category.
Deep Multiple Instance Learning for Zero-shot Image Tagging
In-line with the success of deep learning on traditional recognition problem, several end-to-end deep models for zero-shot recognition have been proposed in the literature.
A Unified approach for Conventional Zero-shot, Generalized Zero-shot and Few-shot Learning
Then, it learns how to combine these directions to obtain the principal direction for each unseen class such that the CAPD of the test image is aligned with the semantic embedding of the true class, and opposite to the other classes.
Saliency, Scale and Information: Towards a Unifying Theory
In this paper we present a definition for visual saliency grounded in information theory.
