Search Results for author:
Found 9 papers, 4 papers with code
Elysium: Exploring Object-level Perception in Videos via MLLM
Multi-modal Large Language Models (MLLMs) have demonstrated their ability to perceive objects in still images, but their application in video-related tasks, such as object tracking, remains understudied.
Ranked #1 on
Zero-Shot Single Object Tracking
on LaSOT
PaDeLLM-NER: Parallel Decoding in Large Language Models for Named Entity Recognition
In this study, we aim to reduce generation latency for Named Entity Recognition (NER) with Large Language Models (LLMs).
GloTSFormer: Global Video Text Spotting Transformer
In this paper, we propose a novel Global Video Text Spotting Transformer GloTSFormer to model the tracking problem as global associations and utilize the Gaussian Wasserstein distance to guide the morphological correlation between frames.
Rethinking Skip Connections in Encoder-decoder Networks for Monocular Depth Estimation
Skip connections are fundamental units in encoder-decoder networks, which are able to improve the feature propagtion of the neural networks.
Learning Oriented Remote Sensing Object Detection via Naive Geometric Computing
Detecting oriented objects along with estimating their rotation information is one crucial step for analyzing remote sensing images.
Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction
An evaluation in such a setup raises the question if a correct prediction is actually a new fact that was induced by reasoning over the open knowledge graph or if it can be trivially explained.
A Relational Tucker Decomposition for Multi-Relational Link Prediction
We propose the Relational Tucker3 (RT) decomposition for multi-relational link prediction in knowledge graphs.
On Evaluating Embedding Models for Knowledge Base Completion
In this paper, we explore whether recent models work well for knowledge base completion and argue that the current evaluation protocols are more suited for question answering rather than knowledge base completion.
On Multi-Relational Link Prediction with Bilinear Models
Bilinear models belong to the most basic models for this task, they are comparably efficient to train and use, and they can provide good prediction performance.
