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Found 18 papers, 13 papers with code
MM-Retinal V2: Transfer an Elite Knowledge Spark into Fundus Vision-Language Pretraining
Vision-language pretraining (VLP) has been investigated to generalize across diverse downstream tasks for fundus image analysis.
LLM+KG@VLDB'24 Workshop Summary
The unification of large language models (LLMs) and knowledge graphs (KGs) has emerged as a hot topic.
KGroot: Enhancing Root Cause Analysis through Knowledge Graphs and Graph Convolutional Neural Networks
To achieve this, KGroot uses event knowledge and the correlation between events to perform root cause reasoning by integrating knowledge graphs and GCNs for RCA.
Embedding Ontologies via Incorporating Extensional and Intensional Knowledge
Extensional knowledge provides information about the concrete instances that belong to specific concepts in the ontology, while intensional knowledge details inherent properties, characteristics, and semantic associations among concepts.
FreeInit: Bridging Initialization Gap in Video Diffusion Models
Though diffusion-based video generation has witnessed rapid progress, the inference results of existing models still exhibit unsatisfactory temporal consistency and unnatural dynamics.
VideoBooth: Diffusion-based Video Generation with Image Prompts
In this paper, we study the task of video generation with image prompts, which provide more accurate and direct content control beyond the text prompts.
VBench: Comprehensive Benchmark Suite for Video Generative Models
We will open-source VBench, including all prompts, evaluation methods, generated videos, and human preference annotations, and also include more video generation models in VBench to drive forward the field of video generation.
Robust Sequential DeepFake Detection
However, existing methods only focus on detecting one-step facial manipulation.
LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models
To this end, we propose LaVie, an integrated video generation framework that operates on cascaded video latent diffusion models, comprising a base T2V model, a temporal interpolation model, and a video super-resolution model.
Ranked #4 on
Text-to-Video Generation
on EvalCrafter Text-to-Video (ECTV) Dataset
(using extra training data)
Detecting and Grounding Multi-Modal Media Manipulation and Beyond
HAMMER performs 1) manipulation-aware contrastive learning between two uni-modal encoders as shallow manipulation reasoning, and 2) modality-aware cross-attention by multi-modal aggregator as deep manipulation reasoning.
AsdKB: A Chinese Knowledge Base for the Early Screening and Diagnosis of Autism Spectrum Disorder
To easily obtain the knowledge about autism spectrum disorder and help its early screening and diagnosis, we create AsdKB, a Chinese knowledge base on autism spectrum disorder.
DeepFake-Adapter: Dual-Level Adapter for DeepFake Detection
Unlike existing deepfake detection methods merely focusing on low-level forgery patterns, the forgery detection process of our model can be regularized by generalizable high-level semantics from a pre-trained ViT and adapted by global and local low-level forgeries of deepfake data.
Detecting and Grounding Multi-Modal Media Manipulation
In this paper, we highlight a new research problem for multi-modal fake media, namely Detecting and Grounding Multi-Modal Media Manipulation (DGM^4).
ReVersion: Diffusion-Based Relation Inversion from Images
In this work, we propose the Relation Inversion task, which aims to learn a specific relation (represented as "relation prompt") from exemplar images.
Detecting and Recovering Sequential DeepFake Manipulation
Moreover, we build a comprehensive benchmark and set up rigorous evaluation protocols and metrics for this new research problem.
Outlining and Filling: Hierarchical Query Graph Generation for Answering Complex Questions over Knowledge Graphs
The high-level decoding generates an AQG as a constraint to prune the search space and reduce the locally ambiguous query graph.
Ranked #1 on
Knowledge Base Question Answering
on LC-QuAD 1.0
Efficiently Embedding Dynamic Knowledge Graphs
Knowledge graph (KG) embedding encodes the entities and relations from a KG into low-dimensional vector spaces to support various applications such as KG completion, question answering, and recommender systems.
Semantic Guided and Response Times Bounded Top-k Similarity Search over Knowledge Graphs
We face two challenges on graph query over a knowledge graph: (1) the structural gap between $G_Q$ and the predefined schema in $G$ causes mismatch with query graph, (2) users cannot view the answers until the graph query terminates, leading to a longer system response time (SRT).
Databases
