129 citations · 494 across the 76 of their papers we have counts for
15 papers · 1 filter
Do DALL-E and Flamingo Understand Each Other?
Hang Li, Jindong Gu, Rajat Koner +2
The field of multimodal research focusing on the comprehension and creation of both images and text has witnessed significant strides. This progress is exemplified by the emergence…
CL-CrossVQA: A Continual Learning Benchmark for Cross-Domain Visual Question Answering
Yao Zhang, Haokun Chen, Ahmed Frikha +5
Visual Question Answering (VQA) is a multi-discipline research task. To produce the right answer, it requires an understanding of the visual content of images, the natural language…
Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information
Zifeng Ding, Jingpei Wu, Bailan He +3
Knowledge graph completion (KGC) aims to predict the missing links among knowledge graph (KG) entities. Though various methods have been developed for KGC, most of them can only de…
Named Entity Recognition in Industrial Tables using Tabular Language Models
Aneta Koleva, Martin Ringsquandl, Mark Buckley +2
Specialized transformer-based models for encoding tabular data have gained interest in academia. Although tabular data is omnipresent in industry, applications of table transformer…
InstanceFormer: An Online Video Instance Segmentation Framework
Rajat Koner, Tanveer Hannan, Suprosanna Shit +4
Recent transformer-based offline video instance segmentation (VIS) approaches achieve encouraging results and significantly outperform online approaches. However, their reliance on…
ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs
Zifeng Ding, Zongyue Li, Ruoxia Qi +8
Question answering over temporal knowledge graphs (TKGQA) has recently found increasing interest. TKGQA requires temporal reasoning techniques to extract the relevant information f…
SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness
Jindong Gu, Hengshuang Zhao, Volker Tresp +1
Deep neural network-based image classifications are vulnerable to adversarial perturbations. The image classifications can be easily fooled by adding artificial small and impercept…
On Calibration of Graph Neural Networks for Node Classification
Tong Liu, Yushan Liu, Marcel Hildebrandt +3
Graphs can model real-world, complex systems by representing entities and their interactions in terms of nodes and edges. To better exploit the graph structure, graph neural networ…
Biologically Inspired Neural Path Finding
Hang Li, Qadeer Khan, Volker Tresp +1
The human brain can be considered to be a graphical structure comprising of tens of billions of biological neurons connected by synapses. It has the remarkable ability to automatic…
Continuous Temporal Graph Networks for Event-Based Graph Data
Jin Guo, Zhen Han, Zhou Su +3
There has been an increasing interest in modeling continuous-time dynamics of temporal graph data. Previous methods encode time-evolving relational information into a low-dimension…
FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation
Haokun Chen, Ahmed Frikha, Denis Krompass +2
Federated Learning (FL) is a decentralized learning paradigm, in which multiple clients collaboratively train deep learning models without centralizing their local data, and hence…
Learning Meta Representations of One-shot Relations for Temporal Knowledge Graph Link Prediction
Zifeng Ding, Bailan He, Yunpu Ma +2
Few-shot relational learning for static knowledge graphs (KGs) has drawn greater interest in recent years, while few-shot learning for temporal knowledge graphs (TKGs) has hardly b…
A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs
Charles Tapley Hoyt, Max Berrendorf, Mikhail Galkin +2
The link prediction task on knowledge graphs without explicit negative triples in the training data motivates the usage of rank-based metrics. Here, we review existing rank-based m…
Relationformer: A Unified Framework for Image-to-Graph Generation
Suprosanna Shit, Rajat Koner, Bastian Wittmann +8
A comprehensive representation of an image requires understanding objects and their mutual relationship, especially in image-to-graph generation, e.g., road network extraction, blo…
ECOLA: Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations
Zhen Han, Ruotong Liao, Jindong Gu +6
Since conventional knowledge embedding models cannot take full advantage of the abundant textual information, there have been extensive research efforts in enhancing knowledge embe…