Search Results for author:
Found 51 papers, 32 papers with code
Persona-Guided Planning for Controlling the Protagonist’s Persona in Story Generation
In this paper, we aim to control the protagonist’s persona in story generation, i. e., generating a story from a leading context and a persona description, where the protagonist should exhibit the specified personality through a coherent event sequence.
Theoretical Benefit and Limitation of Diffusion Language Model
In this paper, we present a rigorous theoretical analysis of a widely used type of diffusion language model, the Masked Diffusion Model (MDM), and find that its effectiveness heavily depends on the target evaluation metric.
Human Decision-making is Susceptible to AI-driven Manipulation
Through a randomized controlled trial with 233 participants, we examined human susceptibility to such manipulation in financial (e. g., purchases) and emotional (e. g., conflict resolution) decision-making contexts.
Fusion of Millimeter-wave Radar and Pulse Oximeter Data for Low-burden Diagnosis of Obstructive Sleep Apnea-Hypopnea Syndrome
Objective: The aim of the study is to develop a novel method for improved diagnosis of obstructive sleep apnea-hypopnea syndrome (OSAHS) in clinical or home settings, with the focus on achieving diagnostic performance comparable to the gold-standard polysomnography (PSG) with significantly reduced monitoring burden.
Spectral-Temporal Fusion Representation for Person-in-Bed Detection
This study is based on the ICASSP 2025 Signal Processing Grand Challenge's Accelerometer-Based Person-in-Bed Detection Challenge, which aims to determine bed occupancy using accelerometer signals.
Attacking Voice Anonymization Systems with Augmented Feature and Speaker Identity Difference
This study focuses on the First VoicePrivacy Attacker Challenge within the ICASSP 2025 Signal Processing Grand Challenge, which aims to develop speaker verification systems capable of determining whether two anonymized speech signals are from the same speaker.
Graph-Enhanced Dual-Stream Feature Fusion with Pre-Trained Model for Acoustic Traffic Monitoring
We propose a graph-enhanced dual-stream feature fusion strategy which consists of a vehicle type feature extraction (VTFE) branch, a vehicle direction feature extraction (VDFE) branch, and a frame-level feature fusion module to combine the type and direction feature for enhanced performance.
Band Prompting Aided SAR and Multi-Spectral Data Fusion Framework for Local Climate Zone Classification
In this paper, a novel band prompting aided data fusion framework is proposed for LCZ classification, namely BP-LCZ, which utilizes textual prompts associated with band groups to guide the model in learning the physical attributes of different bands and semantics of various categories inherent in SAR and multi-spectral data to augment the fused feature, thus enhancing LCZ classification performance.
Towards a Comprehensive Benchmark for Pathological Lymph Node Metastasis in Breast Cancer Sections
Advances in optical microscopy scanning have significantly contributed to computational pathology (CPath) by converting traditional histopathological slides into whole slide images (WSIs).
Independent Feature Enhanced Crossmodal Fusion for Match-Mismatch Classification of Speech Stimulus and EEG Response
Specifically, our IFE-CF contains a crossmodal encoder to encode the speech stimulus and the EEG response with a two-branch structure connected via crossmodal attention mechanism in the encoding process, a multi-channel fusion module to fuse features of two modalities by aggregating the interaction feature obtained from the crossmodal encoder and the independent feature obtained from the speech stimulus and EEG response, and a predictor to give the matching result.
2D-TPE: Two-Dimensional Positional Encoding Enhances Table Understanding for Large Language Models
Tables are ubiquitous across various domains for concisely representing structured information.
Detection of Sleep Apnea-Hypopnea Events Using Millimeter-wave Radar and Pulse Oximeter
Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a sleep-related breathing disorder associated with significant morbidity and mortality worldwide.
Unlocking Reasoning Potential in Large Langauge Models by Scaling Code-form Planning
To train CodePlan, we construct a large-scale dataset of 2M examples that integrate code-form plans with standard prompt-response pairs from existing corpora.
Mixture-of-Modules: Reinventing Transformers as Dynamic Assemblies of Modules
More interestingly, with a fixed parameter budget, MoM-large enables an over 38% increase in depth for computation graphs compared to GPT-2-large, resulting in absolute gains of 1. 4 on GLUE and 1 on XSUM.
From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data Synthesis
To overcome the challenge, we first introduce a least-to-most visual reasoning paradigm, which interleaves steps of decomposing a question into sub-questions and invoking external tools for resolving sub-questions.
FastDrag: Manipulate Anything in One Step
Drag-based image editing using generative models provides precise control over image contents, enabling users to manipulate anything in an image with a few clicks.
SISP: A Benchmark Dataset for Fine-grained Ship Instance Segmentation in Panchromatic Satellite Images
To this end, we propose a benchmark dataset for fine-grained Ship Instance Segmentation in Panchromatic satellite images, namely SISP, which contains 56, 693 well-annotated ship instances with four fine-grained categories across 10, 000 sliced images, and all the images are collected from SuperView-1 satellite with the resolution of 0. 5m.
AMOR: A Recipe for Building Adaptable Modular Knowledge Agents Through Process Feedback
We present AMOR, an agent framework based on open-source LLMs, which reasons with external knowledge bases and adapts to specific domains through human supervision to the reasoning process.
Language Models Hallucinate, but May Excel at Fact Verification
Recent progress in natural language processing (NLP) owes much to remarkable advances in large language models (LLMs).
Hierarchical Metadata Information Constrained Self-Supervised Learning for Anomalous Sound Detection Under Domain Shift
Self-supervised learning methods have achieved promising performance for anomalous sound detection (ASD) under domain shift, where the type of domain shift is considered in feature learning by incorporating section IDs.
Mitigating the Learning Bias towards Repetition by Self-Contrastive Training for Open-Ended Generation
Despite the huge progress in myriad generation tasks, pretrained language models (LMs) such as GPT2 still tend to generate repetitive texts with maximization-based decoding algorithms for open-ended generation.
Time-weighted Frequency Domain Audio Representation with GMM Estimator for Anomalous Sound Detection
This paper presents Time-Weighted Frequency Domain Representation (TWFR) with the GMM method (TWFR-GMM) for anomalous sound detection.
Re$^3$Dial: Retrieve, Reorganize and Rescale Dialogue Corpus for Long-Turn Open-Domain Dialogue Pre-training
However, the pre-trained dialogue model's ability to utilize long-range context is limited due to the scarcity of long-turn dialogue sessions.
Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretraining
This paper uses contrastive learning to refine audio representations for each machine ID, rather than for each audio sample.
EARL: An Elliptical Distribution aided Adaptive Rotation Label Assignment for Oriented Object Detection in Remote Sensing Images
In addition, a dynamic elliptical distribution aided sampling (DED) strategy is proposed to make the sample distribution more flexible to fit the shapes and orientations of targets, and filter out low-quality samples.
A Benchmark for Understanding and Generating Dialogue between Characters in Stories
We build a new dataset DialStory, which consists of 105k Chinese stories with a large amount of dialogue weaved into the plots to support the evaluation.
StoryTrans: Non-Parallel Story Author-Style Transfer with Discourse Representations and Content Enhancing
Moreover, to enhance content preservation, we design a mask-and-fill framework to explicitly fuse style-specific keywords of source texts into generation.
Generating Coherent Narratives by Learning Dynamic and Discrete Entity States with a Contrastive Framework
Despite advances in generating fluent texts, existing pretraining models tend to attach incoherent event sequences to involved entities when generating narratives such as stories and news.
Persona-Guided Planning for Controlling the Protagonist's Persona in Story Generation
Endowing the protagonist with a specific personality is essential for writing an engaging story.
A Corpus for Understanding and Generating Moral Stories
Teaching morals is one of the most important purposes of storytelling.
Local Information Assisted Attention-free Decoder for Audio Captioning
Although this method effectively captures global information within audio data via the self-attention mechanism, it may ignore the event with short time duration, due to its limitation in capturing local information in an audio signal, leading to inaccurate prediction of captions.
CUGE: A Chinese Language Understanding and Generation Evaluation Benchmark
We argue that for general-purpose language intelligence evaluation, the benchmark itself needs to be comprehensive and systematic.
LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation
Therefore, we propose a story-centric benchmark named LOT for evaluating Chinese long text modeling, which aggregates two understanding tasks and two generation tasks.
CPM-2: Large-scale Cost-effective Pre-trained Language Models
We present a suite of cost-effective techniques for the use of PLMs to deal with the efficiency issues of pre-training, fine-tuning, and inference.
OpenMEVA: A Benchmark for Evaluating Open-ended Story Generation Metrics
Automatic metrics are essential for developing natural language generation (NLG) models, particularly for open-ended language generation tasks such as story generation.
Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence
Generating long and coherent text is an important but challenging task, particularly for open-ended language generation tasks such as story generation.
Stylized Story Generation with Style-Guided Planning
Current storytelling systems focus more ongenerating stories with coherent plots regard-less of the narration style, which is impor-tant for controllable text generation.
Low-dimensional Denoising Embedding Transformer for ECG Classification
However, the high-dimensional embedding obtained via 1-D convolution and positional encoding can lead to the loss of the signal's own temporal information and a large amount of training parameters.
Time-domain Speech Enhancement with Generative Adversarial Learning
Speech enhancement aims to obtain speech signals with high intelligibility and quality from noisy speech.
A Text GAN for Language Generation with Non-Autoregressive Generator
Despite the great success of Generative Adversarial Networks (GANs) in generating high-quality images, GANs for text generation still face two major challenges: first, most text GANs are unstable in training mainly due to ineffective optimization of the generator, and they heavily rely on maximum likelihood pretraining; second, most text GANs adopt autoregressive generators without latent variables, which largely limits the ability to learn latent representations for natural language text.
CPM: A Large-scale Generative Chinese Pre-trained Language Model
However, applying GPT-3 to address Chinese NLP tasks is still challenging, as the training corpus of GPT-3 is primarily English, and the parameters are not publicly available.
UNION: An Unreferenced Metric for Evaluating Open-ended Story Generation
Experiments on two story datasets demonstrate that UNION is a reliable measure for evaluating the quality of generated stories, which correlates better with human judgments and is more generalizable than existing state-of-the-art metrics.
CoTK: An Open-Source Toolkit for Fast Development and Fair Evaluation of Text Generation
In text generation evaluation, many practical issues, such as inconsistent experimental settings and metric implementations, are often ignored but lead to unfair evaluation and untenable conclusions.
A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation
To further capture the causal and temporal dependencies between the sentences in a reasonable story, we employ multi-task learning which combines a discriminative objective to distinguish true and fake stories during fine-tuning.
A Practical Solution for SAR Despeckling With Adversarial Learning Generated Speckled-to-Speckled Images
In this letter, we aim to address a synthetic aperture radar (SAR) despeckling problem with the necessity of neither clean (speckle-free) SAR images nor independent speckled image pairs from the same scene, and a practical solution for SAR despeckling (PSD) is proposed.
IENet: Interacting Embranchment One Stage Anchor Free Detector for Orientation Aerial Object Detection
First, a novel geometric transformation is employed to better represent the oriented object in angle prediction, then a branch interactive module with a self-attention mechanism is developed to fuse features from classification and box regression branches.
A Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation
Reinforcement learning is effective in optimizing policies for recommender systems.
Generative Adversarial Network
Model-based Reinforcement Learning
+4
Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation
Reinforcement learning is well suited for optimizing policies of recommender systems.
Generative Adversarial Network
Model-based Reinforcement Learning
+4
Story Ending Generation with Incremental Encoding and Commonsense Knowledge
This task requires not only to understand the context clues which play an important role in planning the plot but also to handle implicit knowledge to make a reasonable, coherent story.
Ranked #3 on
Image-guided Story Ending Generation
on VIST-E
