Search Results for author:
Found 32 papers, 16 papers with code
Online Semantic Parsing for Latency Reduction in Task-Oriented Dialogue
Standard conversational semantic parsing maps a complete user utterance into an executable program, after which the program is executed to respond to the user.
Toward Robust and Accurate Adversarial Camouflage Generation against Vehicle Detectors
However, existing methods often struggle to capture environmental characteristics during the rendering process or produce adversarial textures that can precisely map to the target vehicle.
"It's a conversation, not a quiz": A Risk Taxonomy and Reflection Tool for LLM Adoption in Public Health
Recent breakthroughs in large language models (LLMs) have generated both interest and concern about their potential adoption as accessible information sources or communication tools across different domains.
SocialGPT: Prompting LLMs for Social Relation Reasoning via Greedy Segment Optimization
The manual prompt design process for LLMs at the reasoning phase is tedious and an automated prompt optimization method is desired.
Ranked #1 on
Visual Social Relationship Recognition
on PIPA
CNCA: Toward Customizable and Natural Generation of Adversarial Camouflage for Vehicle Detectors
By sampling the optimal texture image from the diffusion model with a user-specific text prompt, our method can generate natural and customizable adversarial camouflage while maintaining high attack performance.
A Novel Hybrid Approach for Tornado Prediction in the United States: Kalman-Convolutional BiLSTM with Multi-Head Attention
Tornadoes are among the most intense atmospheric vortex phenomena and pose significant challenges for detection and forecasting.
A Community-Centric Perspective for Characterizing and Detecting Anti-Asian Violence-Provoking Speech
In contrast to prior work that has demonstrated the effectiveness of such classifiers in detecting hateful speech ($F_1 = 0. 89$), our work shows that accurate and reliable detection of violence-provoking speech is a challenging task ($F_1 = 0. 69$).
MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?
Compared with open-source VLMs, smaller-sized scoring models can provide better feedback regarding text-image alignment and image quality, while VLMs provide more accurate feedback regarding safety and generation bias due to their stronger reasoning capabilities.
InfRS: Incremental Few-Shot Object Detection in Remote Sensing Images
In this paper, we explore the intricate task of incremental few-shot object detection in remote sensing images.
Semi-Parametric Retrieval via Binary Token Index
The landscape of information retrieval has broadened from search services to a critical component in various advanced applications, where indexing efficiency, cost-effectiveness, and freshness are increasingly important yet remain less explored.
Few-shot Oriented Object Detection with Memorable Contrastive Learning in Remote Sensing Images
Few-shot object detection (FSOD) has garnered significant research attention in the field of remote sensing due to its ability to reduce the dependency on large amounts of annotated data.
HALC: Object Hallucination Reduction via Adaptive Focal-Contrast Decoding
While large vision-language models (LVLMs) have demonstrated impressive capabilities in interpreting multi-modal contexts, they invariably suffer from object hallucinations (OH).
RAUCA: A Novel Physical Adversarial Attack on Vehicle Detectors via Robust and Accurate Camouflage Generation
However, existing methods often struggle to capture environmental characteristics during the rendering process or produce adversarial textures that can precisely map to the target vehicle, resulting in suboptimal attack performance.
LangSplat: 3D Language Gaussian Splatting
Humans live in a 3D world and commonly use natural language to interact with a 3D scene.
Quick Back-Translation for Unsupervised Machine Translation
The field of unsupervised machine translation has seen significant advancement from the marriage of the Transformer and the back-translation algorithm.
CLIPTrans: Transferring Visual Knowledge with Pre-trained Models for Multimodal Machine Translation
Simultaneously, there has been an influx of multilingual pre-trained models for NMT and multimodal pre-trained models for vision-language tasks, primarily in English, which have shown exceptional generalisation ability.
3D Face Arbitrary Style Transfer
To solve this problem, we propose a novel method, namely Face-guided Dual Style Transfer (FDST).
Retrieval-based Disentangled Representation Learning with Natural Language Supervision
In light of this, we present Vocabulary Disentangled Retrieval (VDR), a retrieval-based framework that harnesses natural language as proxies of the underlying data variation to drive disentangled representation learning.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
Inducing and Using Alignments for Transition-based AMR Parsing
These alignments are learned separately from parser training and require a complex pipeline of rule-based components, pre-processing, and post-processing to satisfy domain-specific constraints.
Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering
To alleviate the data scarcity problem in training question answering systems, recent works propose additional intermediate pre-training for dense passage retrieval (DPR).
Structure-aware Fine-tuning of Sequence-to-sequence Transformers for Transition-based AMR Parsing
We provide a detailed comparison with recent progress in AMR parsing and show that the proposed parser retains the desirable properties of previous transition-based approaches, while being simpler and reaching the new parsing state of the art for AMR 2. 0, without the need for graph re-categorization.
Ranked #9 on
AMR Parsing
on LDC2017T10
(using extra training data)
$$Research on fusion algorithm of multi-attribute decision making and reinforcement learning based on intuitionistic fuzzy number in wargame environment$$
It is also the first time in this field that an algorithm design for intelligent wargaming combines multi-attribute decision making with reinforcement learning.
Method for making multi-attribute decisions in wargames by combining intuitionistic fuzzy numbers with reinforcement learning
Researchers are increasingly focusing on intelligent games as a hot research area. The article proposes an algorithm that combines the multi-attribute management and reinforcement learning methods, and that combined their effect on wargaming, it solves the problem of the agent's low rate of winning against specific rules and its inability to quickly converge during intelligent wargame training. At the same time, this paper studied a multi-attribute decision making and reinforcement learning algorithm in a wargame simulation environment, and obtained data on red and blue conflict. Calculate the weight of each attribute based on the intuitionistic fuzzy number weight calculations.
AMR Parsing with Action-Pointer Transformer
In this work, we propose a transition-based system that combines hard-attention over sentences with a target-side action pointer mechanism to decouple source tokens from node representations and address alignments.
Ranked #1 on
AMR Parsing
on LDC2014T12
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics.
Ranked #1 on
Extreme Summarization
on GEM-XSum
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+5
On Statistical Efficiency in Learning
We propose a generalized notion of Takeuchi's information criterion and prove that the proposed method can asymptotically achieve the optimal out-sample prediction loss under reasonable assumptions.
Improving Non-autoregressive Neural Machine Translation with Monolingual Data
Non-autoregressive (NAR) neural machine translation is usually done via knowledge distillation from an autoregressive (AR) model.
Automating Botnet Detection with Graph Neural Networks
Botnets are now a major source for many network attacks, such as DDoS attacks and spam.
A Hierarchy of Graph Neural Networks Based on Learnable Local Features
We derive theoretical results about the discriminative power and feature representation capabilities of each class.
Simple Unsupervised Summarization by Contextual Matching
We propose an unsupervised method for sentence summarization using only language modeling.
Ranked #39 on
Text Summarization
on GigaWord
Local Structure Matching Driven by Joint-Saliency-Structure Adaptive Kernel Regression
For nonrigid image registration, matching the particular structures (or the outliers) that have missing correspondence and/or local large deformations, can be more difficult than matching the common structures with small deformations in the two images.
