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Found 16 papers, 6 papers with code
Can We Improve Channel Reciprocity via Loop-back Compensation for RIS-assisted Physical Layer Key Generation
The results demonstrate a significant improvement in both the rate of key generation assisted by the RIS and the consistency of the generated keys, showing great potential for the practical deployment of our LoCKey in future wireless systems.
Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks
This finding motivates us to present a novel method that aims to harden GCNs by automatically learning Latent Homophilic Structures over heterophilic graphs.
Ranked #3 on
Node Classification
on Actor
DocMSU: A Comprehensive Benchmark for Document-level Multimodal Sarcasm Understanding
Multimodal Sarcasm Understanding (MSU) has a wide range of applications in the news field such as public opinion analysis and forgery detection.
Passive Eavesdropping Can Significantly Slow Down RIS-Assisted Secret Key Generation
Reconfigurable Intelligent Surface (RIS) assisted physical layer key generation has shown great potential to secure wireless communications by smartly controlling signals such as phase and amplitude.
3D-IDS: Doubly Disentangled Dynamic Intrusion Detection
This motivates us to propose 3D-IDS, a novel method that aims to tackle the above issues through two-step feature disentanglements and a dynamic graph diffusion scheme.
Physical-layer Adversarial Robustness for Deep Learning-based Semantic Communications
We argue that central to the success of ESC is the robust interpretation of conveyed semantics at the receiver side, especially for security-critical applications such as automatic driving and smart healthcare.
Boosting Physical Layer Black-Box Attacks with Semantic Adversaries in Semantic Communications
To this end, we present SemBLK, a novel method that can learn to generate destructive physical layer semantic attacks for an ESC system under the black-box setting, where the adversaries are imperceptible to humans.
You Can Ground Earlier than See: An Effective and Efficient Pipeline for Temporal Sentence Grounding in Compressed Videos
To handle the raw video bit-stream input, we propose a novel Three-branch Compressed-domain Spatial-temporal Fusion (TCSF) framework, which extracts and aggregates three kinds of low-level visual features (I-frame, motion vector and residual features) for effective and efficient grounding.
Fusion with Hierarchical Graphs for Mulitmodal Emotion Recognition
Automatic emotion recognition (AER) based on enriched multimodal inputs, including text, speech, and visual clues, is crucial in the development of emotionally intelligent machines.
Uncovering Main Causalities for Long-tailed Information Extraction
Information Extraction (IE) aims to extract structural information from unstructured texts.
Speaker-Oriented Latent Structures for Dialogue-Based Relation Extraction
Dialogue-based relation extraction (DiaRE) aims to detect the structural information from unstructured utterances in dialogues.
Interventional Video Grounding with Dual Contrastive Learning
2) Meanwhile, we introduce a dual contrastive learning approach (DCL) to better align the text and video by maximizing the mutual information (MI) between query and video clips, and the MI between start/end frames of a target moment and the others within a video to learn more informative visual representations.
Video Corpus Moment Retrieval with Contrastive Learning
We adopt the first approach and introduce two contrastive learning objectives to refine video encoder and text encoder to learn video and text representations separately but with better alignment for VCMR.
Integrating Subgraph-aware Relation and DirectionReasoning for Question Answering
Question Answering (QA) models over Knowledge Bases (KBs) are capable of providing more precise answers by utilizing relation information among entities.
Counterfactual Thinking for Long-tailed Information Extraction
However, in practice, the long-tailed and imbalanced data may lead to severe bias issues for deep learning models, due to very few training instances available for the tail classes.
Reasoning with Latent Structure Refinement for Document-Level Relation Extraction
Document-level relation extraction requires integrating information within and across multiple sentences of a document and capturing complex interactions between inter-sentence entities.
Ranked #9 on
Relation Extraction
on GDA
