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Found 11 papers, 11 papers with code
V3D: Video Diffusion Models are Effective 3D Generators
To fully unleash the potential of video diffusion to perceive the 3D world, we further introduce geometrical consistency prior and extend the video diffusion model to a multi-view consistent 3D generator.
GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting
3D editing plays a crucial role in many areas such as gaming and virtual reality.
Text-to-3D using Gaussian Splatting
Specifically, our method adopts a progressive optimization strategy, which includes a geometry optimization stage and an appearance refinement stage.
PAR: Political Actor Representation Learning with Social Context and Expert Knowledge
Extensive experiments demonstrate that PAR is better at augmenting political text understanding and successfully advances the state-of-the-art in political perspective detection and roll call vote prediction.
BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency
In addition, given the stealing behavior of novel Twitter bots, BIC proposes to model semantic consistency in tweets based on attention weights while using it to augment the decision process.
KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion
Knowledge Graph Embeddings (KGE) aim to map entities and relations to low dimensional spaces and have become the \textit{de-facto} standard for knowledge graph completion.
TwiBot-22: Towards Graph-Based Twitter Bot Detection
Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse.
KCD: Knowledge Walks and Textual Cues Enhanced Political Perspective Detection in News Media
Previous approaches generally focus on leveraging textual content to identify stances, while they fail to reason with background knowledge or leverage the rich semantic and syntactic textual labels in news articles.
Attention based Broadly Self-guided Network for Low light Image Enhancement
During the past years, deep convolutional neural networks have achieved impressive success in low-light Image Enhancement. Existing deep learning methods mostly enhance the ability of feature extraction by stacking network structures and deepening the depth of the network. which causes more runtime cost on single image. In order to reduce inference time while fully extracting local features and global features. Inspired by SGN, we propose a Attention based Broadly self-guided network (ABSGN) for real world low-light image Enhancement. such a broadly strategy is able to handle the noise at different exposures. The proposed network is validated by many mainstream benchmark. Additional experimental results show that the proposed network outperforms most of state-of-the-art low-light image Enhancement solutions.
Legislator Representation Learning with Social Context and Expert Knowledge
Modeling the ideological perspectives of political actors is an essential task in computational political science with applications in many downstream tasks.
KGAP: Knowledge Graph Augmented Political Perspective Detection in News Media
Specifically, we construct a political knowledge graph to serve as domain-specific external knowledge.
