Search Results for author:
Found 8 papers, 7 papers with code
VideoINSTA: Zero-shot Long Video Understanding via Informative Spatial-Temporal Reasoning with LLMs
The challenge of information redundancy in long videos prompts the question of what specific information is essential for large language models (LLMs) and how to leverage them for complex spatial-temporal reasoning in long-form video analysis.
EchoScene: Indoor Scene Generation via Information Echo over Scene Graph Diffusion
The scheme ensures that the denoising processes are influenced by a holistic understanding of the scene graph, facilitating the generation of globally coherent scenes.
zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models
We first input the text descriptions of KG relations into large language models (LLMs) for generating relation representations, and then introduce them into embedding-based TKGF methods.
GraphextQA: A Benchmark for Evaluating Graph-Enhanced Large Language Models
The proposed dataset is designed to evaluate graph-language models' ability to understand graphs and make use of it for answer generation.
GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models
Extensive experiments have shown that GenTKG outperforms conventional methods of temporal relational forecasting with low computation resources using extremely limited training data as few as 16 samples.
A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
This paper aims to provide a comprehensive survey of cutting-edge research in prompt engineering on three types of vision-language models: multimodal-to-text generation models (e. g. Flamingo), image-text matching models (e. g.
ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs
To this end, we propose ForecastTKGQA, a TKGQA model that employs a TKG forecasting module for future inference, to answer all three types of questions.
ECOLA: Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations
Since conventional knowledge embedding models cannot take full advantage of the abundant textual information, there have been extensive research efforts in enhancing knowledge embedding using texts.
