Search Results for author:
Found 49 papers, 24 papers with code
KELLMRec: Knowledge-Enhanced Large Language Models for Recommendation
The utilization of semantic information is an important research problem in the field of recommender systems, which aims to complement the missing parts of mainstream ID-based approaches.
FormulaQA: A Question Answering Dataset for Formula-Based Numerical Reasoning
The application of formulas is a fundamental ability of humans when addressing numerical reasoning problems.
A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions
With the continuous growth of large Knowledge Graphs (KGs), extractive KG summarization becomes a trending task.
TransY-Net:Learning Fully Transformer Networks for Change Detection of Remote Sensing Images
To relieve these issues, in this work we propose a novel Transformer-based learning framework named TransY-Net for remote sensing image CD, which improves the feature extraction from a global view and combines multi-level visual features in a pyramid manner.
Furthest Reasoning with Plan Assessment: Stable Reasoning Path with Retrieval-Augmented Large Language Models
On one hand, Information Retriever (IR) is hindered by the low quality of generated queries by LLM.
Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning
The past few years have witnessed the immense success of object detection, while current excellent detectors struggle on tackling size-limited instances.
Ranked #1 on
Small Object Detection
on SODA-D
Enhancing In-Context Learning with Answer Feedback for Multi-Span Question Answering
Previous researches found that in-context learning is an effective approach to exploiting LLM, by using a few task-related labeled data as demonstration examples to construct a few-shot prompt for answering new questions.
Threatening Patch Attacks on Object Detection in Optical Remote Sensing Images
Advanced Patch Attacks (PAs) on object detection in natural images have pointed out the great safety vulnerability in methods based on deep neural networks.
Fewer is More: Efficient Object Detection in Large Aerial Images
Current mainstream object detection methods for large aerial images usually divide large images into patches and then exhaustively detect the objects of interest on all patches, no matter whether there exist objects or not.
DyRRen: A Dynamic Retriever-Reranker-Generator Model for Numerical Reasoning over Tabular and Textual Data
Numerical reasoning over hybrid data containing tables and long texts has recently received research attention from the AI community.
Query-based Industrial Analytics over Knowledge Graphs with Ontology Reshaping
Industrial analytics that includes among others equipment diagnosis and anomaly detection heavily relies on integration of heterogeneous production data.
Towards Ontology Reshaping for KG Generation with User-in-the-Loop: Applied to Bosch Welding
One important approach of KG generation is to map the raw data to a given KG schema, namely a domain ontology, and construct the entities and properties according to the ontology.
Knowledge Base Question Answering: A Semantic Parsing Perspective
In this survey, we situate KBQA in the broader literature of semantic parsing and give a comprehensive account of how existing KBQA approaches attempt to address the unique challenges.
Towards Large-Scale Small Object Detection: Survey and Benchmarks
Then, to catalyze the development of SOD, we construct two large-scale Small Object Detection dAtasets (SODA), SODA-D and SODA-A, which focus on the Driving and Aerial scenarios respectively.
Clues Before Answers: Generation-Enhanced Multiple-Choice QA
A trending paradigm for multiple-choice question answering (MCQA) is using a text-to-text framework.
Ranked #10 on
Question Answering
on OpenBookQA
Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation
Few-shot segmentation, which aims to segment unseen-class objects given only a handful of densely labeled samples, has received widespread attention from the community.
Ranked #47 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension
Recent machine reading comprehension datasets such as ReClor and LogiQA require performing logical reasoning over text.
Learning What Not to Segment: A New Perspective on Few-Shot Segmentation
Specifically, we apply an additional branch (base learner) to the conventional FSS model (meta learner) to explicitly identify the targets of base classes, i. e., the regions that do not need to be segmented.
Ranked #19 on
Few-Shot Semantic Segmentation
on PASCAL-5i (1-Shot)
Bilinear Systems Induced by Proper Lie Group Actions
In the study of induced bilinear systems, the classical Lie algebra rank condition (LARC) is known to be impractical since it requires computing the rank everywhere.
Adaptive neighborhood Metric learning
In this paper, we reveal that metric learning would suffer from serious inseparable problem if without informative sample mining.
Aerial Scene Parsing: From Tile-level Scene Classification to Pixel-wise Semantic Labeling
Finally, we perform ASP by unifying the tile-level scene classification and object-based image analysis to achieve pixel-wise semantic labeling.
Weakly Supervised Rotation-Invariant Aerial Object Detection Network
Object rotation is among long-standing, yet still unexplored, hard issues encountered in the task of weakly supervised object detection (WSOD) from aerial images.
Exploring Effective Data for Surrogate Training Towards Black-Box Attack
Finally, in accordance with the in-depth observations for the methods based on proxy data, we argue that leveraging the proxy data is still an effective way for surrogate training.
Ensemble Recognition in Reproducing Kernel Hilbert Spaces through Aggregated Measurements
In this paper, we study the problem of learning dynamical properties of ensemble systems from their collective behaviors using statistical approaches in reproducing kernel Hilbert space (RKHS).
Anchor-free Oriented Proposal Generator for Object Detection
Nowadays, oriented detectors mostly use horizontal boxes as intermedium to derive oriented boxes from them.
When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions
Scenario-based question answering (SQA) requires retrieving and reading paragraphs from a large corpus to answer a question which is contextualized by a long scenario description.
Oriented R-CNN for Object Detection
Current state-of-the-art two-stage detectors generate oriented proposals through time-consuming schemes.
Ranked #10 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Weakly Supervised Object Localization and Detection: A Survey
As an emerging and challenging problem in the computer vision community, weakly supervised object localization and detection plays an important role for developing new generation computer vision systems and has received significant attention in the past decade.
TSQA: Tabular Scenario Based Question Answering
To solve the task, we extend state-of-the-art MRC methods with TTGen, a novel table-to-text generator.
Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities
Considering the rapid evolution of this field, this paper provides a systematic survey of deep learning methods for remote sensing image scene classification by covering more than 160 papers.
Enriching Documents with Compact, Representative, Relevant Knowledge Graphs
A prominent application of knowledge graph (KG) is document enrichment.
Neural Entity Summarization with Joint Encoding and Weak Supervision
In a large-scale knowledge graph (KG), an entity is often described by a large number of triple-structured facts.
SPARQA: Skeleton-based Semantic Parsing for Complex Questions over Knowledge Bases
This dedicated coarse-grained formalism with a BERT-based parsing algorithm helps to improve the accuracy of the downstream fine-grained semantic parsing.
ESBM: An Entity Summarization BenchMark
In this paper, we create an Entity Summarization BenchMark (ESBM) which overcomes the limitations of existing benchmarks and meets standard desiderata for a benchmark.
DeepLENS: Deep Learning for Entity Summarization
Entity summarization has been a prominent task over knowledge graphs.
Relaxing Relationship Queries on Graph Data
We prove that verifying the success of a sub-query turns into finding an entity (called a certificate) that satisfies a distance-based condition about the query entities.
Entity Summarization: State of the Art and Future Challenges
This has motivated fruitful research on automated generation of summaries for entity descriptions to satisfy users' information needs efficiently and effectively.
GREASE: A Generative Model for Relevance Search over Knowledge Graphs
The model applies to meta-path based relevance where a meta-path characterizes a particular type of semantics of relating the query entity to answer entities.
Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark
However, the current survey of datasets and deep learning based methods for object detection in optical remote sensing images is not adequate.
Towards More Usable Dataset Search: From Query Characterization to Snippet Generation
Reusing published datasets on the Web is of great interest to researchers and developers.
GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level
Scenario-based question answering (SQA) has attracted increasing research attention.
A Framework for Evaluating Snippet Generation for Dataset Search
Reusing existing datasets is of considerable significance to researchers and developers.
Reading Comprehension with Graph-based Temporal-Casual Reasoning
Complex questions in reading comprehension tasks require integrating information from multiple sentences.
Graph-based Ontology Summarization: A Survey
Ontologies have been widely used in numerous and varied applications, e. g., to support data modeling, information integration, and knowledge management.
Information Retrieval
ES-LDA: Entity Summarization using Knowledge-based Topic Modeling
With the advent of the Internet, the amount of Semantic Web documents that describe real-world entities and their inter-links as a set of statements have grown considerably.
Remote Sensing Image Scene Classification: Benchmark and State of the Art
During the past years, significant efforts have been made to develop various datasets or present a variety of approaches for scene classification from remote sensing images.
RIFD-CNN: Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection
This is achieved by introducing and learning a rotation-invariant layer and a Fisher discriminative layer, respectively, on the basis of the existing high-capacity CNN architectures.
A Survey on Object Detection in Optical Remote Sensing Images
This paper aims to provide a review of the recent progress in this field.
Learning Coarse-to-Fine Sparselets for Efficient Object Detection and Scene Classification
Part model-based methods have been successfully applied to object detection and scene classification and have achieved state-of-the-art results.
