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Found 11 papers, 4 papers with code
Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discovery
The key challenges of GRD are how to mitigate the serious model biases caused by labeled pre-defined relations to learn effective relational representations and how to determine the specific semantics of novel relations during classifying or clustering unlabeled instances.
Mind Reasoning Manners: Enhancing Type Perception for Generalized Zero-shot Logical Reasoning over Text
(2) How to enhance the perception of reasoning types for the models?
GPTR: Gestalt-Perception Transformer for Diagram Object Detection
These lead to the fact that traditional data-driven detection model is not suitable for diagrams.
Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach
In this paper, by considering time-varying network environment, a dynamic computation offloading and resource allocation problem in F-RANs is formulated to minimize the task execution delay and energy consumption of MDs.
Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning
Firstly, we introduce different extraction strategies to split the text into two sets of logical units, and construct the logical graph and the syntax graph respectively.
Ranked #14 on
Reading Comprehension
on ReClor
Consistent Style Transfer
Recently, attentional arbitrary style transfer methods have been proposed to achieve fine-grained results, which manipulates the point-wise similarity between content and style features for stylization.
MoCA: Incorporating Multi-stage Domain Pretraining and Cross-guided Multimodal Attention for Textbook Question Answering
Textbook Question Answering (TQA) is a complex multimodal task to infer answers given large context descriptions and abundant diagrams.
Learning First-Order Rules with Relational Path Contrast for Inductive Relation Reasoning
Relation reasoning in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant paradigm is learning the embeddings of relations and entities, which is limited to a transductive setting and has restriction on processing unseen entities in an inductive situation.
RL-CSDia: Representation Learning of Computer Science Diagrams
Another type of diagrams such as from Computer Science is composed of graphics containing complex topologies and relations, and research on this type of diagrams is still blank.
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.
ZSTAD: Zero-Shot Temporal Activity Detection
An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos.
