Search Results for author:
Found 77 papers, 33 papers with code
Building a Bridge: A Method for Image-Text Sarcasm Detection Without Pretraining on Image-Text Data
We use the pretrained Multi-Head Attention of BERT to model the text and image.
Correcting the Misuse: A Method for the Chinese Idiom Cloze Test
The cloze test for Chinese idioms is a new challenge in machine reading comprehension: given a sentence with a blank, choosing a candidate Chinese idiom which matches the context.
Text in the Dark: Extremely Low-Light Text Image Enhancement
We also labeled texts in the extremely low-light See In the Dark (SID) and ordinary LOw-Light (LOL) datasets to allow for objective assessment of extremely low-light image enhancement through scene text tasks.
Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts
With our method, the origin language models can cover several times longer contexts while keeping the computing requirements close to the baseline.
Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark
To promote the improvement of Chinese LLMs' ability to answer dynamic questions, in this paper, we introduce CDQA, a Chinese Dynamic QA benchmark containing question-answer pairs related to the latest news on the Chinese Internet.
Mirror: A Multiple-perspective Self-Reflection Method for Knowledge-rich Reasoning
While Large language models (LLMs) have the capability to iteratively reflect on their own outputs, recent studies have observed their struggles with knowledge-rich problems without access to external resources.
Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond
Task embedding, a meta-learning technique that captures task-specific information, has become prevalent, especially in areas such as multi-task learning, model editing, and interpretability.
PointMamba: A Simple State Space Model for Point Cloud Analysis
Recently, state space models (SSM), a new family of deep sequence models, have presented great potential for sequence modeling in NLP tasks.
Global Tropical Cyclone Intensity Forecasting with Multi-modal Multi-scale Causal Autoregressive Model
Accurate forecasting of Tropical cyclone (TC) intensity is crucial for formulating disaster risk reduction strategies.
An open dataset for oracle bone script recognition and decipherment
Additionally, all images and labels have been reviewed and corrected by experts in oracle bone studies.
An open dataset for the evolution of oracle bone characters: EVOBC
We conducted validation and simulated deciphering on the constructed dataset, and the results demonstrate its high efficacy in aiding the study of oracle bone script.
ModaVerse: Efficiently Transforming Modalities with LLMs
This alignment process, which synchronizes a language model trained on textual data with encoders and decoders trained on multi-modal data, often necessitates extensive training of several projection layers in multiple stages.
Enhancing Edge Intelligence with Highly Discriminant LNT Features
Along this line, we present a novel supervised learning method to generate highly discriminant complementary features based on the least-squares normal transform (LNT).
Simultaneous Synthesis and Verification of Neural Control Barrier Functions through Branch-and-Bound Verification-in-the-loop Training
In this work, we leverage bound propagation techniques and the Branch-and-Bound scheme to efficiently verify that a neural network satisfies the conditions to be a CBF over the continuous state space.
Holistic Evaluation of GPT-4V for Biomedical Imaging
GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain.
Efficient Bottom-Up Synthesis for Programs with Local Variables
Our algorithm can reduce the space of programs with local variables.
A Scalable Framework for Table of Contents Extraction from Complex ESG Annual Reports
Table of contents (ToC) extraction centres on structuring documents in a hierarchical manner.
A Unified Remote Sensing Anomaly Detector Across Modalities and Scenes via Deviation Relationship Learning
Firstly, we reformulate the anomaly detection task as an undirected bilayer graph based on the deviation relationship, where the anomaly score is modeled as the conditional probability, given the pattern of the background and normal objects.
SwitchGPT: Adapting Large Language Models for Non-Text Outputs
To bridge this gap, we propose a novel approach, \methodname, from a modality conversion perspective that evolves a text-based LLM into a multi-modal one.
Exploiting Machine Unlearning for Backdoor Attacks in Deep Learning System
In recent years, the security issues of artificial intelligence have become increasingly prominent due to the rapid development of deep learning research and applications.
Class Prior-Free Positive-Unlabeled Learning with Taylor Variational Loss for Hyperspectral Remote Sensing Imagery
Positive-unlabeled learning (PU learning) in hyperspectral remote sensing imagery (HSI) is aimed at learning a binary classifier from positive and unlabeled data, which has broad prospects in various earth vision applications.
DiLogics: Creating Web Automation Programs With Diverse Logics
Knowledge workers frequently encounter repetitive web data entry tasks, like updating records or placing orders.
New Interaction Paradigm for Complex EDA Software Leveraging GPT
In the rapidly growing field of electronic design automation (EDA), professional software such as KiCad, Cadence , and Altium Designer provide increasingly extensive design functionalities.
A Badminton Recognition and Tracking System Based on Context Multi-feature Fusion
Ball recognition and tracking have traditionally been the main focus of computer vision researchers as a crucial component of sports video analysis.
Green Steganalyzer: A Green Learning Approach to Image Steganalysis
A novel learning solution to image steganalysis based on the green learning paradigm, called Green Steganalyzer (GS), is proposed in this work.
Document-Level Multi-Event Extraction with Event Proxy Nodes and Hausdorff Distance Minimization
By directly minimizing Hausdorff distance, the model is trained towards the global optimum directly, which improves performance and reduces training time.
IMAP: Intrinsically Motivated Adversarial Policy
Our experiments validate the effectiveness of the four types of adversarial intrinsic regularizers and BR in enhancing black-box adversarial policy learning across a variety of environments.
Transition Propagation Graph Neural Networks for Temporal Networks
The proposed TIP-GNN focuses on the bilevel graph structure in temporal networks: besides the explicit interaction graph, a node's sequential interactions can also be constructed as a transition graph.
Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation
The whole temporal neighborhood of nodes reveals the varying preferences of nodes.
One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning
In this paper, an unsupervised transferred direct detection (TDD) model is proposed, which is optimized directly for the anomaly detection task (one-step paradigm) and has transferability.
Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors
Anomaly segmentation in high spatial resolution (HSR) remote sensing imagery is aimed at segmenting anomaly patterns of the earth deviating from normal patterns, which plays an important role in various Earth vision applications.
SPTS v2: Single-Point Scene Text Spotting
Within the context of our SPTS v2 framework, our experiments suggest a potential preference for single-point representation in scene text spotting when compared to other representations.
Ranked #15 on
Text Spotting
on ICDAR 2015
Named Entity and Relation Extraction with Multi-Modal Retrieval
MoRe contains a text retrieval module and an image-based retrieval module, which retrieve related knowledge of the input text and image in the knowledge corpus respectively.
Ranked #1 on
Multi-modal Named Entity Recognition
on SNAP (MNER)
Multi-modal Named Entity Recognition
Named Entity Recognition
+4
NeurIPS 2022 Competition: Driving SMARTS
The proposed competition supports methodologically diverse solutions, such as reinforcement learning (RL) and offline learning methods, trained on a combination of naturalistic AD data and open-source simulation platform SMARTS.
RITA: Boost Driving Simulators with Realistic Interactive Traffic Flow
High-quality traffic flow generation is the core module in building simulators for autonomous driving.
One-Class Risk Estimation for One-Class Hyperspectral Image Classification
Hyperspectral imagery (HSI) one-class classification is aimed at identifying a single target class from the HSI by using only knowing positive data, which can significantly reduce the requirements for annotation.
Static Knowledge vs. Dynamic Argumentation: A Dual Theory Based on Kripke Semantics
This paper establishes a dual theory about knowledge and argumentation.
Statistical Attention Localization (SAL): Methodology and Application to Object Classification
SAL consists of three steps: 1) preliminary attention window selection via decision statistics, 2) attention map refinement, and 3) rectangular attention region finalization.
Automated Evaluation for Student Argumentative Writing: A Survey
This paper surveys and organizes research works in an under-studied area, which we call automated evaluation for student argumentative writing.
Stability of China's Stock Market: Measure and Forecast by Ricci Curvature on Network
As far as we know, this is the first paper to apply Ricci curvature to forecast the systemic stability of domestic stock market, and our results show that Ricci curvature has good explanatory power for the market stability and can be a good indicator to judge the future risk and volatility of the domestic market.
Transfinite Modal Logic: a Semi-quantitative Explanation for Bayesian Reasoning
Bayesian reasoning plays a significant role both in human rationality and in machine learning.
DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition
Our system wins 10 out of 13 tracks in the MultiCoNER shared task.
Multilingual Named Entity Recognition
Named Entity Recognition
+1
Repairing Adversarial Texts through Perturbation
Furthermore, such attacks are impossible to eliminate, i. e., the adversarial perturbation is still possible after applying mitigation methods such as adversarial training.
SPTS: Single-Point Text Spotting
For the first time, we demonstrate that training scene text spotting models can be achieved with an extremely low-cost annotation of a single-point for each instance.
Ranked #3 on
Text Spotting
on SCUT-CTW1500
ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition
As text representations take the most important role in MNER, in this paper, we propose {\bf I}mage-{\bf t}ext {\bf A}lignments (ITA) to align image features into the textual space, so that the attention mechanism in transformer-based pretrained textual embeddings can be better utilized.
Ranked #1 on
Multi-modal Named Entity Recognition
on Twitter-17
Multi-modal Named Entity Recognition
named-entity-recognition
+1
Learning Dynamic Preference Structure Embedding From Temporal Networks
Our proposed Dynamic Preference Structure (DPS) framework consists of two stages: structure sampling and graph fusion.
Fairness Testing of Deep Image Classification with Adequacy Metrics
DeepFAIT consists of several important components enabling effective fairness testing of deep image classification applications: 1) a neuron selection strategy to identify the fairness-related neurons; 2) a set of multi-granularity adequacy metrics to evaluate the model's fairness; 3) a test selection algorithm for fixing the fairness issues efficiently.
A-PixelHop: A Green, Robust and Explainable Fake-Image Detector
A novel method for detecting CNN-generated images, called Attentive PixelHop (or A-PixelHop), is proposed in this work.
Dependency Induction Through the Lens of Visual Perception
Our experiments find that concreteness is a strong indicator for learning dependency grammars, improving the direct attachment score (DAS) by over 50\% as compared to state-of-the-art models trained on pure text.
Automatic Fairness Testing of Neural Classifiers through Adversarial Sampling
In this work, we bridge the gap by proposing a scalable and effective approach for systematically searching for discriminatory samples while extending existing fairness testing approaches to address a more challenging domain, i. e., text classification.
ICDAR 2021 Competition on Integrated Circuit Text Spotting and Aesthetic Assessment
With hundreds of thousands of electronic chip components are being manufactured every day, chip manufacturers have seen an increasing demand in seeking a more efficient and effective way of inspecting the quality of printed texts on chip components.
Enhanced Universal Dependency Parsing with Automated Concatenation of Embeddings
This paper describes the system used in submission from SHANGHAITECH team to the IWPT 2021 Shared Task.
Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
We find empirically that the contextual representations computed on the retrieval-based input view, constructed through the concatenation of a sentence and its external contexts, can achieve significantly improved performance compared to the original input view based only on the sentence.
Ranked #1 on
Named Entity Recognition (NER)
on CMeEE
Real-time tracking of COVID-19 and coronavirus research updates through text mining
The novel coronavirus (SARS-CoV-2) which causes COVID-19 is an ongoing pandemic.
Enhance Information Propagation for Graph Neural Network by Heterogeneous Aggregations
Graph neural networks are emerging as continuation of deep learning success w. r. t.
WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets for hyperspectral image classification
Some start-of-art hyperspectral image classification methods benchmarked the WHU-Hi dataset, and the experimental results show that WHU-Hi is a challenging dataset.
Towards Repairing Neural Networks Correctly
Neural networks are increasingly applied to support decision making in safety-critical applications (like autonomous cars, unmanned aerial vehicles and face recognition based authentication).
Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training
In this paper, we propose second-order graph-based neural dependency parsing using message passing and end-to-end neural networks.
Ranked #1 on
Dependency Parsing
on Chinese Treebank
Automated Concatenation of Embeddings for Structured Prediction
Pretrained contextualized embeddings are powerful word representations for structured prediction tasks.
Ranked #1 on
Chunking
on Penn Treebank
Structural Knowledge Distillation: Tractably Distilling Information for Structured Predictor
The objective function of knowledge distillation is typically the cross-entropy between the teacher and the student's output distributions.
AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network
The linear-chain Conditional Random Field (CRF) model is one of the most widely-used neural sequence labeling approaches.
Ranked #3 on
Chunking
on CoNLL 2003 (German)
More Embeddings, Better Sequence Labelers?
Recent work proposes a family of contextual embeddings that significantly improves the accuracy of sequence labelers over non-contextual embeddings.
Ranked #2 on
Chunking
on CoNLL 2003 (German)
Enhanced Universal Dependency Parsing with Second-Order Inference and Mixture of Training Data
This paper presents the system used in our submission to the \textit{IWPT 2020 Shared Task}.
ShanghaiTech at MRP 2019: Sequence-to-Graph Transduction with Second-Order Edge Inference for Cross-Framework Meaning Representation Parsing
This paper presents the system used in our submission to the \textit{CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing}.
Structure-Level Knowledge Distillation For Multilingual Sequence Labeling
Multilingual sequence labeling is a task of predicting label sequences using a single unified model for multiple languages.
On the General Value of Evidence, and Bilingual Scene-Text Visual Question Answering
Visual Question Answering (VQA) methods have made incredible progress, but suffer from a failure to generalize.
Syndrome-aware Herb Recommendation with Multi-Graph Convolution Network
Given a set of symptoms to treat, we aim to generate an overall syndrome representation by effectively fusing the embeddings of all the symptoms in the set, to mimic how a doctor induces the syndromes.
Exploring the Capacity of an Orderless Box Discretization Network for Multi-orientation Scene Text Detection
More importantly, based on OBD, we provide a detailed analysis of the impact of a collection of refinements, which may inspire others to build state-of-the-art text detectors.
Ranked #3 on
Scene Text Detection
on ICDAR 2017 MLT
There is Limited Correlation between Coverage and Robustness for Deep Neural Networks
In this work, we conduct an empirical study to evaluate the relationship between coverage, robustness and attack/defense metrics for DNN.
Personality-Aware Probabilistic Map for Trajectory Prediction of Pedestrians
We update this map dynamically based on the agents in the environment and prior trajectory of a pedestrian.
Towards Interpreting Recurrent Neural Networks through Probabilistic Abstraction
In this work, we propose an approach to extract probabilistic automata for interpreting an important class of neural networks, i. e., recurrent neural networks.
Sketch-Driven Regular Expression Generation from Natural Language and Examples
Our system achieves state-of-the-art performance on the prior datasets and solves 57% of the real-world dataset, which existing neural systems completely fail on.
Second-Order Semantic Dependency Parsing with End-to-End Neural Networks
Semantic dependency parsing aims to identify semantic relationships between words in a sentence that form a graph.
Ranked #3 on
Semantic Dependency Parsing
on DM
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
We thus first propose a measure of `sensitivity' and show empirically that normal samples and adversarial samples have distinguishable sensitivity.
Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing
Recently, it has been shown that deep neural networks (DNN) are subject to attacks through adversarial samples.
Event Representations for Automated Story Generation with Deep Neural Nets
We then present a technique for automated story generation whereby we decompose the problem into the generation of successive events (event2event) and the generation of natural language sentences from events (event2sentence).
Robust and Real-time Deep Tracking Via Multi-Scale Domain Adaptation
Visual tracking is a fundamental problem in computer vision.
