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Found 9 papers, 7 papers with code
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
Specifically, we propose a novel relation-steering contrastive learning scheme to impose two critical properties of the relation prompt: 1) The relation prompt should capture the interaction between objects, enforced by the preposition prior.
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.
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
