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Found 47 papers, 9 papers with code
Incremental Few-Shot Meta-Learning via Indirect Discriminant Alignment
This process enables incrementally improving the model by processing multiple learning episodes, each representing a different learning task, even with few training examples.
TOT: Topology-Aware Optimal Transport For Multimodal Hate Detection
Multimodal hate detection, which aims to identify harmful content online such as memes, is crucial for building a wholesome internet environment.
HierCat: Hierarchical Query Categorization from Weakly Supervised Data at Facebook Marketplace
Query categorization at customer-to-customer e-commerce platforms like Facebook Marketplace is challenging due to the vagueness of search intent, noise in real-world data, and imbalanced training data across languages.
OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization
To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks.
Ranked #15 on
Natural Language Inference
on RTE
Structure-Guided Image Completion with Image-level and Object-level Semantic Discriminators
Moreover, the object-level discriminators take aligned instances as inputs to enforce the realism of individual objects.
SceneComposer: Any-Level Semantic Image Synthesis
We propose a new framework for conditional image synthesis from semantic layouts of any precision levels, ranging from pure text to a 2D semantic canvas with precise shapes.
A Light-weight, Effective and Efficient Model for Label Aggregation in Crowdsourcing
Due to the dynamic nature, the proposed algorithms can also estimate true labels online without re-visiting historical data.
Proceedings of Principle and practice of data and Knowledge Acquisition Workshop 2022 (PKAW 2022)
Over the past two decades, PKAW has provided a forum for researchers and practitioners to discuss the state-of-the-arts in the area of knowledge acquisition and machine intelligence (MI, also Artificial Intelligence, AI).
Exploring Contextual Relationships for Cervical Abnormal Cell Detection
To mimic these behaviors, we propose to explore contextual relationships to boost the performance of cervical abnormal cell detection.
Recent Advances for Quantum Neural Networks in Generative Learning
Due to the intrinsic probabilistic nature of quantum mechanics, it is reasonable to postulate that quantum generative learning models (QGLMs) may surpass their classical counterparts.
Unified Structure Generation for Universal Information Extraction
Information extraction suffers from its varying targets, heterogeneous structures, and demand-specific schemas.
Few-shot Named Entity Recognition with Self-describing Networks
In this paper, we propose a self-describing mechanism for few-shot NER, which can effectively leverage illustrative instances and precisely transfer knowledge from external resources by describing both entity types and mentions using a universal concept set.
Uncovering system vulnerability and criticality of human brain under evolving neuropathological events in Alzheimer's Disease
Despite the striking efforts in investigating neurobiological factors behind the acquisition of beta-amyloid (A), protein tau (T), and neurodegeneration ([N]) biomarkers, the mechanistic pathways of how AT[N] biomarkers spread throughout the brain remain elusive.
Deep Open Set Identification for RF Devices
Artificial intelligence (AI) based device identification improves the security of the internet of things (IoT), and accelerates the authentication process.
M2MRF: Many-to-Many Reassembly of Features for Tiny Lesion Segmentation in Fundus Images
It reassembles features in a dimension-reduced feature space and simultaneously aggregates multiple features inside a large predefined region into multiple target features.
A Light-weight Interpretable Compositional Model for Nuclei Detection and Weakly-Supervised Segmentation
It only requires annotations on isolated nucleus, rather than on all nuclei in the dataset.
Context-aware Reranking with Utility Maximization for Recommendation
As a critical task for large-scale commercial recommender systems, reranking has shown the potential of improving recommendation results by uncovering mutual influence among items.
Fine-grained Entity Typing via Label Reasoning
Conventional entity typing approaches are based on independent classification paradigms, which make them difficult to recognize inter-dependent, long-tailed and fine-grained entity types.
Ranked #7 on
Entity Typing
on Open Entity
Modeling time evolving COVID-19 uncertainties with density dependent asymptomatic infections and social reinforcement
Different from existing COVID-19 models, SUDR characterizes the undocumented infections during unknown transmission processes.
AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction
The field-wise transfer policy decides how the pre-trained embedding representations are frozen or fine-tuned based on the given instance from the target domain.
Visual analogy: Deep learning versus compositional models
We compared human performance to that of two recent deep learning models (Siamese Network and Relation Network) directly trained to solve these analogy problems, as well as to that of a compositional model that assesses relational similarity between part-based representations.
Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems
Finally, we evaluate our system using a typical movie ticket booking task and show that the dialogue simulator is an essential component of the system that leads to over $50\%$ improvement in turn-level action signature prediction accuracy.
COVID-19 Modeling: A Review
The SARS-CoV-2 virus and COVID-19 disease have posed unprecedented and overwhelming demand, challenges and opportunities to domain, model and data driven modeling.
Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehicles
We believe our dataset provides a rich testbed to study UDA for part segmentation and will help to significantly push forward research in this area.
Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency
However, existing approaches which rely only on image-level class labels predominantly suffer from errors due to (a) partial segmentation of objects and (b) missing object predictions.
Analyzing the Spatiotemporal Interaction and Propagation of ATN Biomarkers in Alzheimer's Disease using Longitudinal Neuroimaging Data
Three major biomarkers: beta-amyloid (A), pathologic tau (T), and neurodegeneration (N), are recognized as valid proxies for neuropathologic changes of Alzheimer's disease.
Class Knowledge Overlay to Visual Feature Learning for Zero-Shot Image Classification
The approach also applies a semi-supervised learning process to re-train knowledge-to-visual model.
Cross Knowledge-based Generative Zero-Shot Learning Approach with Taxonomy Regularization
CKL enables more relevant semantic features to be trained for semantic-to-visual feature embedding in ZSL, while Taxonomy Regularization (TR) significantly improves the intersections with unseen images with more generalized visual features generated from generative network.
A Deep Retinal Image Quality Assessment Network with Salient Structure Priors
Dual-branch SalStructIQA contains two CNN branches and one is guided by large-size salient structures while the other is guided by tiny-size salient structures.
Dual-Branch Network with Dual-Sampling Modulated Dice Loss for Hard Exudate Segmentation from Colour Fundus Images
In detail, for the first branch, we use a uniform sampler to sample pixels from predicted segmentation mask for Dice loss calculation, which leads to this branch naturally be biased in favour of large hard exudates as Dice loss generates larger cost on misidentification of large hard exudates than small hard exudates.
U-rank: Utility-oriented Learning to Rank with Implicit Feedback
To this end, we propose a novel ranking framework called U-rank that directly optimizes the expected utility of the ranking list.
A Dark and Bright Channel Prior Guided Deep Network for Retinal Image Quality Assessment
Retinal image quality assessment is an essential task in the diagnosis of retinal diseases.
Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion
The structure of the compositional model enables CompositionalNets to decompose images into objects and context, as well as to further decompose object representations in terms of individual parts and the objects' pose.
Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion
Inspired by the success of compositional models at classifying partially occluded objects, we propose to integrate compositional models and DCNNs into a unified deep model with innate robustness to partial occlusion.
Incremental Meta-Learning via Indirect Discriminant Alignment
Majority of the modern meta-learning methods for few-shot classification tasks operate in two phases: a meta-training phase where the meta-learner learns a generic representation by solving multiple few-shot tasks sampled from a large dataset and a testing phase, where the meta-learner leverages its learnt internal representation for a specific few-shot task involving classes which were not seen during the meta-training phase.
Localizing Occluders with Compositional Convolutional Networks
Our experimental results demonstrate that the proposed extensions increase the model's performance at localizing occluders as well as at classifying partially occluded objects.
A Deep Gradient Boosting Network for Optic Disc and Cup Segmentation
To build connections among prediction branches, this paper introduces gradient boosting framework to deep classification model and proposes a gradient boosting network called BoostNet.
Dual-attention Focused Module for Weakly Supervised Object Localization
In each branch, the input feature map is deduced into an enhancement map and a mask map, thereby highlighting the most discriminative parts or hiding them.
Combining Compositional Models and Deep Networks For Robust Object Classification under Occlusion
In this work, we combine DCNNs and compositional object models to retain the best of both approaches: a discriminative model that is robust to partial occlusion and mask attacks.
DDNet: Cartesian-polar Dual-domain Network for the Joint Optic Disc and Cup Segmentation
In this paper, we propose a novel segmentation approach, named Cartesian-polar dual-domain network (DDNet), which for the first time considers the complementary of the Cartesian domain and the polar domain.
Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval
Sketch-based image retrieval (SBIR) is widely recognized as an important vision problem which implies a wide range of real-world applications.
Advancing the State of the Art in Open Domain Dialog Systems through the Alexa Prize
In the second iteration of the competition in 2018, university teams advanced the state of the art by using context in dialog models, leveraging knowledge graphs for language understanding, handling complex utterances, building statistical and hierarchical dialog managers, and leveraging model-driven signals from user responses.
Semantic Part Detection via Matching: Learning to Generalize to Novel Viewpoints from Limited Training Data
In particular, this enables images in the training dataset to be matched to a virtual 3D model of the object (for simplicity, we assume that the object viewpoint can be estimated by standard techniques).
Comparison-Based Convolutional Neural Networks for Cervical Cell/Clumps Detection in the Limited Data Scenario
In this paper we propose an efficient CNN-based object detection methods for cervical cancer cells/clumps detection.
Conversational AI: The Science Behind the Alexa Prize
This paper outlines the advances created by the university teams as well as the Alexa Prize team to achieve the common goal of solving the problem of Conversational
Unleashing the Potential of CNNs for Interpretable Few-Shot Learning
Our models are based on the idea of encoding objects in terms of visual concepts, which are interpretable visual cues represented by the feature vectors within CNNs.
Few-shot Learning by Exploiting Visual Concepts within CNNs
In this work, we address these limitations of CNNs by developing novel, flexible, and interpretable models for few-shot learning.
