Search Results for author:
Found 13 papers, 10 papers with code
Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representations
In this paper, we present an end-to-end framework Structure-CLIP, which integrates Scene Graph Knowledge (SGK) to enhance multi-modal structured representations.
Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer
Through experiments, we justify that the pretrained KGTransformer could be used off the shelf as a general and effective KRF module across KG-related tasks.
Analogical Inference Enhanced Knowledge Graph Embedding
And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding.
MEAformer: Multi-modal Entity Alignment Transformer for Meta Modality Hybrid
Multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) whose entities are associated with relevant images.
Ranked #1 on
Entity Alignment
on FBYG15k
(using extra training data)
Tele-Knowledge Pre-training for Fault Analysis
In this work, we share our experience on tele-knowledge pre-training for fault analysis, a crucial task in telecommunication applications that requires a wide range of knowledge normally found in both machine log data and product documents.
Target-oriented Sentiment Classification with Sequential Cross-modal Semantic Graph
Multi-modal aspect-based sentiment classification (MABSC) is task of classifying the sentiment of a target entity mentioned in a sentence and an image.
LaKo: Knowledge-driven Visual Question Answering via Late Knowledge-to-Text Injection
Visual question answering (VQA) often requires an understanding of visual concepts and language semantics, which relies on external knowledge.
Ranked #21 on
Visual Question Answering (VQA)
on OK-VQA
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning
Specifically, we (1) developed a cross-modal semantic grounding network to investigate the model's capability of disentangling semantic attributes from the images; (2) applied an attribute-level contrastive learning strategy to further enhance the model's discrimination on fine-grained visual characteristics against the attribute co-occurrence and imbalance; (3) proposed a multi-task learning policy for considering multi-model objectives.
Ranked #1 on
Zero-Shot Learning
on CUB-200-2011
Disentangled Ontology Embedding for Zero-shot Learning
In this paper, we focus on ontologies for augmenting ZSL, and propose to learn disentangled ontology embeddings guided by ontology properties to capture and utilize more fine-grained class relationships in different aspects.
Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learning
Then these negative adversaries are trained end-to-end together with the backbone representation network to enhance the discriminative information and promote factor disentanglement performance by maximizing the adversarial contrastive loss.
NeuralKG: An Open Source Library for Diverse Representation Learning of Knowledge Graphs
NeuralKG is an open-source Python-based library for diverse representation learning of knowledge graphs.
Unpaired Deep Image Deraining Using Dual Contrastive Learning
Learning single image deraining (SID) networks from an unpaired set of clean and rainy images is practical and valuable as acquiring paired real-world data is almost infeasible.
Multi-Scale Hourglass Hierarchical Fusion Network for Single Image Deraining
Rain streaks bring serious blurring and visual quality degradation, which often vary in size, direction and density.
