Search Results for author:
Found 15 papers, 7 papers with code
Right for the Right Reason: Evidence Extraction for Trustworthy Tabular Reasoning
On the downstream tabular inference task, using only the automatically extracted evidence as the premise, our approach outperforms prior benchmarks.
EmoAgent: Multi-Agent Collaboration of Plan, Edit, and Critic, for Affective Image Manipulation
Affective Image Manipulation (AIM) aims to alter an image's emotional impact by adjusting multiple visual elements to evoke specific feelings. Effective AIM is inherently complex, necessitating a collaborative approach that involves identifying semantic cues within source images, manipulating these elements to elicit desired emotional responses, and verifying that the combined adjustments successfully evoke the target emotion. To address these challenges, we introduce EmoAgent, the first multi-agent collaboration framework for AIM.
M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understanding
However, there are difficulties in applying these methods in real-world scenarios: (a) questions often require information across different pages or documents, where MLMs cannot handle many long documents; (b) documents often have important information in visual elements such as figures, but text extraction tools ignore them.
GraphMamba: An Efficient Graph Structure Learning Vision Mamba for Hyperspectral Image Classification
Efficient extraction of spectral sequences and geospatial information has always been a hot topic in hyperspectral image classification.
Enhancing Question Answering on Charts Through Effective Pre-training Tasks
To address these issues, we propose three simple pre-training tasks that enforce the existing model in terms of both structural-visual knowledge, as well as its understanding of numerical questions.
document understanding
Optical Character Recognition (OCR)
+1
Enhanced Object Tracking by Self-Supervised Auxiliary Depth Estimation Learning
RGB-D tracking significantly improves the accuracy of object tracking.
TempTabQA: Temporal Question Answering for Semi-Structured Tables
Given these results, our dataset has the potential to serve as a challenging benchmark to improve the temporal reasoning capabilities of NLP models.
Anchor-Intermediate Detector: Decoupling and Coupling Bounding Boxes for Accurate Object Detection
The corner-aware head is able to score the corners of each bounding box to facilitate the coupling between corner points.
Pedestrian-Robot Interactions on Autonomous Crowd Navigation: Reactive Control Methods and Evaluation Metrics
Autonomous navigation in highly populated areas remains a challenging task for robots because of the difficulty in guaranteeing safe interactions with pedestrians in unstructured situations.
Automatic Construction of Enterprise Knowledge Base
In this paper, we present an automatic knowledge base construction system from large scale enterprise documents with minimal efforts of human intervention.
MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning
We propose a selective learning method using meta-learning and deep reinforcement learning for medical image interpretation in the setting of limited labeling resources.
MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square
The rapid development of autonomous driving and mobile mapping calls for off-the-shelf LiDAR SLAM solutions that are adaptive to LiDARs of different specifications on various complex scenarios.
Cross-Lingual Named Entity Recognition Using Parallel Corpus: A New Approach Using XLM-RoBERTa Alignment
We built an entity alignment model on top of XLM-RoBERTa to project the entities detected on the English part of the parallel data to the target language sentences, whose accuracy surpasses all previous unsupervised models.
Ranked #2 on
Cross-Lingual NER
on CoNLL 2003
Towards Robust Visual Tracking for Unmanned Aerial Vehicle with Tri-Attentional Correlation Filters
Object tracking has been broadly applied in unmanned aerial vehicle (UAV) tasks in recent years.
Optimal Action Extraction for Random Forests and Boosted Trees
To address this problem, we present a novel framework to post-process any ATM classifier to extract an optimal actionable plan that can change a given input to a desired class with a minimum cost.
