Search Results for author:
Found 9 papers, 4 papers with code
PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning
PLLaVA achieves new state-of-the-art performance on modern benchmark datasets for both video question-answer and captioning tasks.
Ranked #1 on
Zero-Shot Video Question Answer
on TGIF-QA
Video-based Generative Performance Benchmarking (Consistency)
Video-based Generative Performance Benchmarking (Contextual Understanding)
+4
MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration
Large Language Models (LLMs) have marked a significant advancement in the field of natural language processing, demonstrating exceptional capabilities in reasoning, tool usage, and memory.
ARES: Locally Adaptive Reconstruction-based Anomaly Scoring
However, the anomaly scoring function is not adaptive to the natural variation in reconstruction error across the range of normal samples, which hinders their ability to detect real anomalies.
SparGE: Sparse Coding-based Patient Similarity Learning via Low-rank Constraints and Graph Embedding
SparGE measures similarity by jointly sparse coding and graph embedding.
LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks
This allows us to introduce learnability into local outlier methods, in the form of a neural network, for greater flexibility and expressivity: specifically, we propose LUNAR, a novel, graph neural network-based anomaly detection method.
Utilizing Temporal Information for Taxonomy Construction
Taxonomies play an important role in many applications by organizing domain knowledge into a hierarchy of {`}is-a{'} relations between terms.
