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Found 12 papers, 3 papers with code
C2P-GCN: Cell-to-Patch Graph Convolutional Network for Colorectal Cancer Grading
As C2P-GCN integrates the structural data of an entire WSI into a single graph, it allows our model to work with significantly fewer training data compared to the latest models for colorectal cancer.
Towards Granularity-adjusted Pixel-level Semantic Annotation
Specifically, we propose an approach to enable the Segment Anything Model (SAM) with semantic recognition capability to generate pixel-level annotations for images without any manual supervision.
CAVEN: An Embodied Conversational Agent for Efficient Audio-Visual Navigation in Noisy Environments
Audio-visual navigation of an agent towards locating an audio goal is a challenging task especially when the audio is sporadic or the environment is noisy.
AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments
Similar to audio-visual navigation tasks, the goal of our embodied agent is to localize an audio event via navigating the 3D visual world; however, the agent may also seek help from a human (oracle), where the assistance is provided in free-form natural language.
Exploiting Context for Robustness to Label Noise in Active Learning
Several works in computer vision have demonstrated the effectiveness of active learning for adapting the recognition model when new unlabeled data becomes available.
FLaPS: Federated Learning and Privately Scaling
FL uses the FedAvg algorithm, which is trained in the iterative model averaging way, on the non-iid and unbalanced distributed data, without depending on the data quantity.
Text-based Localization of Moments in a Video Corpus
This task poses a unique challenge as the system is required to perform: (i) retrieval of the relevant video where only a segment of the video corresponds with the queried sentence, and (ii) temporal localization of moment in the relevant video based on sentence query.
LAC : LSTM AUTOENCODER with Community for Insider Threat Detection
The employees of any organization, institute, or industry, spend a significant amount of time on a computer network, where they develop their own routine of activities in the form of network transactions over a time period.
Connecting the Dots: Detecting Adversarial Perturbations Using Context Inconsistency
There has been a recent surge in research on adversarial perturbations that defeat Deep Neural Networks (DNNs) in machine vision; most of these perturbation-based attacks target object classifiers.
Learning With Differential Privacy
The different aspects of differential privacy, it's application in privacy protection and leakage of information, a comparative discussion, on the current research approaches in this field, its utility in the real world as well as the trade-offs - will be discussed.
BUDS: Balancing Utility and Differential Privacy by Shuffling
In this work, after collecting one-hot encoded data from different sources and clients, a step of novel attribute shuffling technique using iterative shuffling (based on the query asked by the analyst) and loss estimation with an updation function and risk minimization produces a utility and privacy balanced differential private report.
ARA : Aggregated RAPPOR and Analysis for Centralized Differential Privacy
The Tf-Idf estimation model then estimates the reports on the basis of the occurrence of "on bit" in a particular position and its contribution to that position.
