Search Results for author:
Found 363 papers, 185 papers with code
FoveaBox: Beyond Anchor-based Object Detector
In FoveaBox, an instance is assigned to adjacent feature levels to make the model more accurate. We demonstrate its effectiveness on standard benchmarks and report extensive experimental analysis.
Ranked #82 on
Object Detection
on COCO test-dev
(APM metric)
SOLO: Segmenting Objects by Locations
We present a new, embarrassingly simple approach to instance segmentation in images.
Ranked #67 on
Instance Segmentation
on COCO test-dev
SOLOv2: Dynamic and Fast Instance Segmentation
Importantly, we take one step further by dynamically learning the mask head of the object segmenter such that the mask head is conditioned on the location.
Ranked #10 on
Real-time Instance Segmentation
on MSCOCO
Sparse R-CNN: End-to-End Object Detection with Learnable Proposals
In our method, however, a fixed sparse set of learned object proposals, total length of $N$, are provided to object recognition head to perform classification and location.
Ranked #5 on
2D Object Detection
on CeyMo
LightSeq: A High Performance Inference Library for Transformers
Transformer, BERT and their variants have achieved great success in natural language processing.
LightSeq2: Accelerated Training for Transformer-based Models on GPUs
In this paper, we present LightSeq2, a system to accelerate training for a general family of Transformer models on GPUs.
Dense Contrastive Learning for Self-Supervised Visual Pre-Training
Compared to the baseline method MoCo-v2, our method introduces negligible computation overhead (only <1% slower), but demonstrates consistently superior performance when transferring to downstream dense prediction tasks including object detection, semantic segmentation and instance segmentation; and outperforms the state-of-the-art methods by a large margin.
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base population.
On Analyzing the Role of Image for Visual-enhanced Relation Extraction
Multimodal relation extraction is an essential task for knowledge graph construction.
One Model for All Domains: Collaborative Domain-Prefix Tuning for Cross-Domain NER
Previous typical solutions mainly obtain a NER model by pre-trained language models (PLMs) with data from a rich-resource domain and adapt it to the target domain.
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting
Most NER methods rely on extensive labeled data for model training, which struggles in the low-resource scenarios with limited training data.
EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing
The success of Pre-Trained Models (PTMs) has reshaped the development of Natural Language Processing (NLP).
EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models
In recent years, instruction tuning has gained increasing attention and emerged as a crucial technique to enhance the capabilities of Large Language Models (LLMs).
A Survey on In-context Learning
With the increasing ability of large language models (LLMs), in-context learning (ICL) has become a new paradigm for natural language processing (NLP), where LLMs make predictions only based on contexts augmented with a few examples.
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.
Ranked #1 on
Semantic Similarity
on CHIP-STS
BRITS: Bidirectional Recurrent Imputation for Time Series
It is ubiquitous that time series contains many missing values.
General Classification
Multivariate Time Series Forecasting
+5
On the Sentence Embeddings from Pre-trained Language Models
Pre-trained contextual representations like BERT have achieved great success in natural language processing.
Ranked #16 on
Semantic Textual Similarity
on STS16
Relation Extraction as Open-book Examination: Retrieval-enhanced Prompt Tuning
Note that the previous parametric learning paradigm can be viewed as memorization regarding training data as a book and inference as the close-book test.
Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning
Specifically, vanilla prompt learning may struggle to utilize atypical instances by rote during fully-supervised training or overfit shallow patterns with low-shot data.
AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
The homepage of this challenge is at https://github. com/RenYang-home/AIM22_CompressSR.
Provable Robust Watermarking for AI-Generated Text
We propose a robust and high-quality watermark method, Unigram-Watermark, by extending an existing approach with a simplified fixed grouping strategy.
Vocabulary Learning via Optimal Transport for Neural Machine Translation
The choice of token vocabulary affects the performance of machine translation.
Towards Making the Most of BERT in Neural Machine Translation
Our experiments in machine translation show CTNMT gains of up to 3 BLEU score on the WMT14 English-German language pair which even surpasses the previous state-of-the-art pre-training aided NMT by 1. 4 BLEU score.
NeurST: Neural Speech Translation Toolkit
NeurST is an open-source toolkit for neural speech translation.
Ranked #1 on
Speech-to-Text Translation
on libri-trans
Finding Sparse Structures for Domain Specific Neural Machine Translation
Neural machine translation often adopts the fine-tuning approach to adapt to specific domains.
The Volctrans Neural Speech Translation System for IWSLT 2021
For offline speech translation, our best end-to-end model achieves 8. 1 BLEU improvements over the benchmark on the MuST-C test set and is even approaching the results of a strong cascade solution.
Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker
Existing methods are not effective due to two challenges of this task: a) the target event arguments are scattered across sentences; b) the correlation among events in a document is non-trivial to model.
Ranked #2 on
Document-level Event Extraction
on ChFinAnn
Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation
Mamba-UNet adopts a pure Visual Mamba (VMamba)-based encoder-decoder structure, infused with skip connections to preserve spatial information across different scales of the network.
PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency
Removing outlier correspondences is one of the critical steps for successful feature-based point cloud registration.
Scale-aware Automatic Augmentation for Object Detection
We propose Scale-aware AutoAug to learn data augmentation policies for object detection.
Dynamically Fused Graph Network for Multi-hop Reasoning
However, many difficult questions require multiple supporting evidence from scattered text among two or more documents.
Ranked #33 on
Question Answering
on HotpotQA
PARAGEN : A Parallel Generation Toolkit
PARAGEN is a PyTorch-based NLP toolkit for further development on parallel generation.
Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information
We investigate the following question for machine translation (MT): can we develop a single universal MT model to serve as the common seed and obtain derivative and improved models on arbitrary language pairs?
Ranked #3 on
Machine Translation
on WMT2014 English-French
(using extra training data)
Contrastive Learning for Many-to-many Multilingual Neural Machine Translation
Existing multilingual machine translation approaches mainly focus on English-centric directions, while the non-English directions still lag behind.
CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling
In real-world applications of natural language generation, there are often constraints on the target sentences in addition to fluency and naturalness requirements.
Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion
Since most MKGs are far from complete, extensive knowledge graph completion studies have been proposed focusing on the multimodal entity, relation extraction and link prediction.
Multimodal Analogical Reasoning over Knowledge Graphs
Analogical reasoning is fundamental to human cognition and holds an important place in various fields.
Double Graph Based Reasoning for Document-level Relation Extraction
Document-level relation extraction aims to extract relations among entities within a document.
Ranked #12 on
Relation Extraction
on DocRED
Glancing Transformer for Non-Autoregressive Neural Machine Translation
With GLM, we develop Glancing Transformer (GLAT) for machine translation.
Ranked #69 on
Machine Translation
on WMT2014 English-German
Invisible Image Watermarks Are Provably Removable Using Generative AI
However, if we do not require the watermarked image to look the same as the original one, watermarks that keep the image semantically similar can be an alternative defense against our attack.
Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification
Data augmentation aims to enrich training samples for alleviating the overfitting issue in low-resource or class-imbalanced situations.
Large Language Models are not Fair Evaluators
In this paper, we uncover a systematic bias in the evaluation paradigm of adopting large language models~(LLMs), e. g., GPT-4, as a referee to score and compare the quality of responses generated by candidate models.
UniRE: A Unified Label Space for Entity Relation Extraction
Entities and relations are represented by squares and rectangles in the table.
Personalized Prompt Learning for Explainable Recommendation
In the latter case, ID vectors are randomly initialized but the model is trained in advance on large corpora, so they are actually in different learning stages.
Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning
In-context learning (ICL) emerges as a promising capability of large language models (LLMs) by providing them with demonstration examples to perform diverse tasks.
Good Visual Guidance Makes A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction
To deal with these issues, we propose a novel Hierarchical Visual Prefix fusion NeTwork (HVPNeT) for visual-enhanced entity and relation extraction, aiming to achieve more effective and robust performance.
Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction
Multimodal named entity recognition and relation extraction (MNER and MRE) is a fundamental and crucial branch in information extraction.
Personalized Transformer for Explainable Recommendation
Transformer, which is demonstrated with strong language modeling capability, however, is not personalized and fails to make use of the user and item IDs since the ID tokens are not even in the same semantic space as the words.
Segment Anything Model for Medical Images?
To fully validate SAM's performance on medical data, we collected and sorted 53 open-source datasets and built a large medical segmentation dataset with 18 modalities, 84 objects, 125 object-modality paired targets, 1050K 2D images, and 6033K masks.
On Tree-Based Neural Sentence Modeling
To study the effectiveness of different tree structures, we replace the parsing trees with trivial trees (i. e., binary balanced tree, left-branching tree and right-branching tree) in the encoders.
Ranked #9 on
Sentiment Analysis
on Amazon Review Full
Task-Aware Monocular Depth Estimation for 3D Object Detection
In this paper, we first analyse the data distributions and interaction of foreground and background, then propose the foreground-background separated monocular depth estimation (ForeSeE) method, to estimate the foreground depth and background depth using separate optimization objectives and depth decoders.
End-to-End Learning Local Multi-view Descriptors for 3D Point Clouds
In this work, we propose an end-to-end framework to learn local multi-view descriptors for 3D point clouds.
Ranked #5 on
Point Cloud Registration
on 3DMatch Benchmark
Cross-modal Contrastive Learning for Speech Translation
Learning similar representations for semantically similar speech and text is important for speech translation.
INSTRUCTSCORE: Explainable Text Generation Evaluation with Finegrained Feedback
By harnessing both explicit human instruction and the implicit knowledge of GPT-4, we fine-tune a text evaluation metric based on LLaMA, producing both a score for generated text and a human readable diagnostic report.
Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond
In this paper, we present a comprehensive review of the development and recent advances in deep learning for trajectory computing (DL4Traj).
Self-Supervised Speech Denoising Using Only Noisy Audio Signals
The first module adopts a random audio sub-sampler on each noisy audio to generate training pairs.
VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
We also introduce two tasks for video-and-language research based on VATEX: (1) Multilingual Video Captioning, aimed at describing a video in various languages with a compact unified captioning model, and (2) Video-guided Machine Translation, to translate a source language description into the target language using the video information as additional spatiotemporal context.
Deepfake Network Architecture Attribution
With the rapid progress of generation technology, it has become necessary to attribute the origin of fake images.
LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification
The final claim verification is based on all latent variables.
Learning Shared Semantic Space for Speech-to-Text Translation
By projecting audio and text features to a common semantic representation, Chimera unifies MT and ST tasks and boosts the performance on ST benchmarks, MuST-C and Augmented Librispeech, to a new state-of-the-art.
Learning Logic Rules for Document-level Relation Extraction
LogiRE treats logic rules as latent variables and consists of two modules: a rule generator and a relation extractor.
Ranked #21 on
Relation Extraction
on DocRED
Weak-to-Strong Jailbreaking on Large Language Models
In this paper, we propose the weak-to-strong jailbreaking attack, an efficient method to attack aligned LLMs to produce harmful text.
Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision
How do we perform efficient inference while retaining high translation quality?
Generating Sentences from Disentangled Syntactic and Semantic Spaces
In this paper, we propose to generate sentences from disentangled syntactic and semantic spaces.
Pun-GAN: Generative Adversarial Network for Pun Generation
It consists of a generator to produce pun sentences, and a discriminator to distinguish between the generated pun sentences and the real sentences with specific word senses.
FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation
The multi-aspect categorization of FETV enables fine-grained analysis of the metrics' reliability in different scenarios.
Automatic Generation of Personalized Comment Based on User Profile
Comments on social media are very diverse, in terms of content, style and vocabulary, which make generating comments much more challenging than other existing natural language generation~(NLG) tasks.
Dynamic Knowledge Distillation for Pre-trained Language Models
Knowledge distillation~(KD) has been proved effective for compressing large-scale pre-trained language models.
Consecutive Decoding for Speech-to-text Translation
The key idea is to generate source transcript and target translation text with a single decoder.
"Listen, Understand and Translate": Triple Supervision Decouples End-to-end Speech-to-text Translation
Can we build a system to fully utilize signals in a parallel ST corpus?
Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models
However, in this paper, we find that it is possible to hack the model in a data-free way by modifying one single word embedding vector, with almost no accuracy sacrificed on clean samples.
STEMM: Self-learning with Speech-text Manifold Mixup for Speech Translation
How to learn a better speech representation for end-to-end speech-to-text translation (ST) with limited labeled data?
Rethinking the Unpretentious U-net for Medical Ultrasound Image Segmentation
However, variable tumor morphology, blurred boundary, and similar intensity distributions bring challenges for accurate segmentation of breast tumors.
Contextual Representation Learning beyond Masked Language Modeling
How do masked language models (MLMs) such as BERT learn contextual representations?
Progressive Domain Expansion Network for Single Domain Generalization
A series of strategies are introduced to guarantee the safety and effectiveness of the expanded domains.
Well-classified Examples are Underestimated in Classification with Deep Neural Networks
The conventional wisdom behind learning deep classification models is to focus on bad-classified examples and ignore well-classified examples that are far from the decision boundary.
Can We Edit Factual Knowledge by In-Context Learning?
Inspired by in-context learning (ICL), a new paradigm based on demonstration contexts without parameter updating, we explore whether ICL can edit factual knowledge.
On the Relationship between Explanation and Recommendation: Learning to Rank Explanations for Improved Performance
Explaining to users why some items are recommended is critical, as it can help users to make better decisions, increase their satisfaction, and gain their trust in recommender systems (RS).
EXTRA: Explanation Ranking Datasets for Explainable Recommendation
To achieve a standard way of evaluating recommendation explanations, we provide three benchmark datasets for EXplanaTion RAnking (denoted as EXTRA), on which explainability can be measured by ranking-oriented metrics.
$\textit{latent}$-GLAT: Glancing at Latent Variables for Parallel Text Generation
Recently, parallel text generation has received widespread attention due to its success in generation efficiency.
GLAT: Glancing at Latent Variables for Parallel Text Generation
Recently, parallel text generation has received widespread attention due to its success in generation efficiency.
Learning Language Specific Sub-network for Multilingual Machine Translation
These jointly trained models often suffer from performance degradation on rich-resource language pairs.
Calibrating Factual Knowledge in Pretrained Language Models
However, we find that facts stored in the PLMs are not always correct.
Variational Template Machine for Data-to-Text Generation
We propose the variational template machine (VTM), a novel method to generate text descriptions from data tables.
TempCompass: Do Video LLMs Really Understand Videos?
Motivated by these two problems, we propose the \textbf{TempCompass} benchmark, which introduces a diversity of temporal aspects and task formats.
Learning Kernel-Smoothed Machine Translation with Retrieved Examples
How to effectively adapt neural machine translation (NMT) models according to emerging cases without retraining?
Back to Optimization: Diffusion-based Zero-Shot 3D Human Pose Estimation
Learning-based methods have dominated the 3D human pose estimation (HPE) tasks with significantly better performance in most benchmarks than traditional optimization-based methods.
Ranked #11 on
3D Human Pose Estimation
on 3DPW
(PA-MPJPE metric)
Syntax Error-Free and Generalizable Tool Use for LLMs via Finite-State Decoding
Large language models (LLMs) have shown promising capabilities in using external tools to solve complex problems.
Efficient Domain Adaptation via Generative Prior for 3D Infant Pose Estimation
We further apply a guided diffusion model to domain adapt 3D adult pose to infant pose to supplement small datasets.
ALGO: Synthesizing Algorithmic Programs with LLM-Generated Oracle Verifiers
To address these challenges, we propose ALGO, a framework that synthesizes Algorithmic programs with LLM-Generated Oracles to guide the generation and verify their correctness.
Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation
To enhance the controllability and interpretability, one can replace the Gaussian prior with a mixture of Gaussian distributions (GM-VAE), whose mixture components could be related to hidden semantic aspects of data.
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i. e., Kuaishou/Kwai Platform.
Learning When to Translate for Streaming Speech
Given a usually long speech sequence, we develop an efficient monotonic segmentation module inside an encoder-decoder model to accumulate acoustic information incrementally and detect proper speech unit boundaries for the input in speech translation task.
Taxonomy Completion via Triplet Matching Network
Previous approaches focus on the taxonomy expansion, i. e. finding an appropriate hypernym concept from the taxonomy for a new query concept.
Multilingual Translation via Grafting Pre-trained Language Models
Can pre-trained BERT for one language and GPT for another be glued together to translate texts?
DmifNet:3D Shape Reconstruction Based on Dynamic Multi-Branch Information Fusion
Then, we use a separate side branch network to process the extracted data to better capture edge geometry and corners feature information.
Tool-Augmented Reward Modeling
We validate our approach across a wide range of domains, incorporating seven distinct external tools.
Delving into the Openness of CLIP
However, evaluating the openness of CLIP-like models is challenging, as the models are open to arbitrary vocabulary in theory, but their accuracy varies in practice.
Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis
Large language models (LLMs) have demonstrated remarkable potential in handling multilingual machine translation (MMT).
Extrapolating Large Language Models to Non-English by Aligning Languages
We start from targeting individual languages by performing cross-lingual instruction-tuning (CoIT) on LLaMA, i. e. tuning it with translation task data and cross-lingual general task data to obtain cross-lingual models (x-LLaMAs), and formulate underlying scaling laws to investigate the advantages of using scalable translation data.
3rd Place Solution to "Google Landmark Retrieval 2020"
Image retrieval is a fundamental problem in computer vision.
Rethinking Document-level Neural Machine Translation
This paper does not aim at introducing a novel model for document-level neural machine translation.
Cross-Modal Commentator: Automatic Machine Commenting Based on Cross-Modal Information
Automatic commenting of online articles can provide additional opinions and facts to the reader, which improves user experience and engagement on social media platforms.
UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction
Keyphrase Prediction (KP) task aims at predicting several keyphrases that can summarize the main idea of the given document.
Enhancing Cross-lingual Transfer by Manifold Mixup
Based on large-scale pre-trained multilingual representations, recent cross-lingual transfer methods have achieved impressive transfer performances.
3D Textured Shape Recovery with Learned Geometric Priors
3D textured shape recovery from partial scans is crucial for many real-world applications.
From Mimicking to Integrating: Knowledge Integration for Pre-Trained Language Models
We then design a Model Uncertainty--aware Knowledge Integration (MUKI) framework to recover the golden supervision for the student.
Can Language Models Understand Physical Concepts?
Language models~(LMs) gradually become general-purpose interfaces in the interactive and embodied world, where the understanding of physical concepts is an essential prerequisite.
End-to-end Speech Translation via Cross-modal Progressive Training
XSTNet takes both speech and text as input and outputs both transcription and translation text.
Kernelized Bayesian Softmax for Text Generation
Neural models for text generation require a softmax layer with proper token embeddings during the decoding phase.
Counter-Interference Adapter for Multilingual Machine Translation
Developing a unified multilingual model has long been a pursuit for machine translation.
WSDesc: Weakly Supervised 3D Local Descriptor Learning for Point Cloud Registration
Instead of using a predefined fixed-size local support in voxelization, we propose to learn the optimal support in a data-driven manner.
Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion
For the label fusion, we design a similarity estimation network (SimNet), which estimates the fusion weight of each atlas by measuring its similarity to the target image.
AtrialJSQnet: A New Framework for Joint Segmentation and Quantification of Left Atrium and Scars Incorporating Spatial and Shape Information
In this work, we develop a new framework, namely AtrialJSQnet, where LA segmentation, scar projection onto the LA surface, and scar quantification are performed simultaneously in an end-to-end style.
Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars.
BEATS: An Open-Source, High-Precision, Multi-Channel EEG Acquisition Tool System
Commonly used EEG acquisition system's hardware and software are usually closed-source.
Rethinking Text Attribute Transfer: A Lexical Analysis
We apply this framework to existing datasets and models and show that: (1) the pivot words are strong features for the classification of sentence attributes; (2) to change the attribute of a sentence, many datasets only requires to change certain pivot words; (3) consequently, many transfer models only perform the lexical-level modification, while leaving higher-level sentence structures unchanged.
Duplex Sequence-to-Sequence Learning for Reversible Machine Translation
Thus REDER enables reversible machine translation by simply flipping the input and output ends.
Not All Errors are Equal: Learning Text Generation Metrics using Stratified Error Synthesis
Is it possible to build a general and automatic natural language generation (NLG) evaluation metric?
Decoupling Predictions in Distributed Learning for Multi-Center Left Atrial MRI Segmentation
In this work, we propose a new framework of distributed learning that bridges the gap between two groups, and improves the performance for both generic and local data.
Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching
Our approach is not only accurate with near-isometric input, for which a high spectral resolution is typically preferred, but also robust and able to produce reasonable matching even in the presence of significant non-isometric distortion, which poses great challenges to existing methods.
SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic Mistakes
In this paper, we propose SESCORE2, a self-supervised approach for training a model-based metric for text generation evaluation.
RPCANet: Deep Unfolding RPCA Based Infrared Small Target Detection
Deep learning (DL) networks have achieved remarkable performance in infrared small target detection (ISTD).
VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Models
The ability to perceive how objects change over time is a crucial ingredient in human intelligence.
Adversarial Option-Aware Hierarchical Imitation Learning
In particular, we propose an Expectation-Maximization(EM)-style algorithm: an E-step that samples the options of expert conditioned on the current learned policy, and an M-step that updates the low- and high-level policies of agent simultaneously to minimize the newly proposed option-occupancy measurement between the expert and the agent.
Confidence Estimation Transformer for Long-term Renewable Energy Forecasting in Reinforcement Learning-based Power Grid Dispatching
The expansion of renewable energy could help realizing the goals of peaking carbon dioxide emissions and carbon neutralization.
Learning Design and Construction with Varying-Sized Materials via Prioritized Memory Resets
Can a robot autonomously learn to design and construct a bridge from varying-sized blocks without a blueprint?
ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrieval
Diffusion models show promising generation capability for a variety of data.
ImgTrojan: Jailbreaking Vision-Language Models with ONE Image
In this paper, we propose a novel jailbreaking attack against VLMs, aiming to bypass their safety barrier when a user inputs harmful instructions.
Deep Active Learning for Anchor User Prediction
Due to the expensive costs of labeling anchor users for training prediction models, we consider in this paper the problem of minimizing the number of user pairs across multiple networks for labeling as to improve the accuracy of the prediction.
Cross-Lingual Vision-Language Navigation
Commanding a robot to navigate with natural language instructions is a long-term goal for grounded language understanding and robotics.
CascadeBERT: Accelerating Inference of Pre-trained Language Models via Calibrated Complete Models Cascade
On the other hand, the exiting decisions made by internal classifiers are unreliable, leading to wrongly emitted early predictions.
AWSnet: An Auto-weighted Supervision Attention Network for Myocardial Scar and Edema Segmentation in Multi-sequence Cardiac Magnetic Resonance Images
Extensive experimental results on a publicly available dataset from Myocardial pathology segmentation combining multi-sequence CMR (MyoPS 2020) demonstrate our method can achieve promising performance compared with other state-of-the-art methods.
SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape Correspondence
In this work, we present a novel learning-based framework that combines the local accuracy of contrastive learning with the global consistency of geometric approaches, for robust non-rigid matching.
Protecting Language Generation Models via Invisible Watermarking
We can then detect the secret message by probing a suspect model to tell if it is distilled from the protected one.
DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences
In this paper, we introduces DIAS, a dataset specifically developed for IA segmentation in DSA sequences.
Unsupervised Editing for Counterfactual Stories
We also propose a new metric to alleviate the shortcomings of current automatic metrics and better evaluate the trade-off.
Distributional Correlation--Aware Knowledge Distillation for Stock Trading Volume Prediction
Traditional knowledge distillation in classification problems transfers the knowledge via class correlations in the soft label produced by teacher models, which are not available in regression problems like stock trading volume prediction.
Distillation-Resistant Watermarking for Model Protection in NLP
We prove that a protected model still retains the original accuracy within a certain bound.
Revisiting k-NN for Fine-tuning Pre-trained Language Models
Pre-trained Language Models (PLMs), as parametric-based eager learners, have become the de-facto choice for current paradigms of Natural Language Processing (NLP).
ImageNetVC: Zero- and Few-Shot Visual Commonsense Evaluation on 1000 ImageNet Categories
Recently, Large Language Models (LLMs) have been serving as general-purpose interfaces, posing a significant demand for comprehensive visual knowledge.
UniVSE: Robust Visual Semantic Embeddings via Structured Semantic Representations
We propose Unified Visual-Semantic Embeddings (UniVSE) for learning a joint space of visual and textual concepts.
Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter
However, as pre-trained language models (PLMs) continue to increase in size, the communication cost for transmitting parameters during synchronization has become a training speed bottleneck.
Knowledgeable Storyteller: A Commonsense-Driven Generative Model for Visual Storytelling
The visual storytelling (VST) task aims at generating a reasonable and coherent paragraph-level story with the image stream as input.
Ranked #21 on
Visual Storytelling
on VIST
ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction
Current state-of-the-art systems for joint entity relation extraction (Luan et al., 2019; Wad-den et al., 2019) usually adopt the multi-task learning framework.
Augmenting Legal Judgment Prediction with Contrastive Case Relations
Existing legal judgment prediction methods usually only consider one single case fact description as input, which may not fully utilize the information in the data such as case relations and frequency.
Converge to the Truth: Factual Error Correction via Iterative Constrained Editing
Given a possibly false claim sentence, how can we automatically correct it with minimal editing?
Say What You Mean! Large Language Models Speak Too Positively about Negative Commonsense Knowledge
Large language models (LLMs) have been widely studied for their ability to store and utilize positive knowledge.
Functional Geometry Guided Protein Sequence and Backbone Structure Co-Design
In this paper, we propose NAEPro, a model to jointly design Protein sequence and structure based on automatically detected functional sites.
Language Generation via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo Approach
Generating natural language under complex constraints is a principled formulation towards controllable text generation.
Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation
In this paper, we propose to leverage word-formation knowledge to enhance Chinese WSD.
Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translation
To address the needs of learning representations for all languages in a unified space, we propose a novel efficient training recipe, upon which we build an effective detachable model, Lego-MT.
Do You Have the Right Scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods
It has been a common approach to pre-train a language model on a large corpus and fine-tune it on task-specific data.
Right Ventricular Segmentation from Short- and Long-Axis MRIs via Information Transition
In this work, we propose an automatic RV segmentation framework, where the information from long-axis (LA) views is utilized to assist the segmentation of short-axis (SA) views via information transition.
$ \text{T}^3 $OMVP: A Transformer-based Time and Team Reinforcement Learning Scheme for Observation-constrained Multi-Vehicle Pursuit in Urban Area
Although there are some achievements in the field of MVP in the open space environment, the urban area brings complicated road structures and restricted moving spaces as challenges to the resolution of MVP games.
Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation
How to learn highly compact yet effective sentence representation?
Pre-trained Language Models Can be Fully Zero-Shot Learners
How can we extend a pre-trained model to many language understanding tasks, without labeled or additional unlabeled data?
Accelerating Antimicrobial Peptide Discovery with Latent Structure
By sampling in the latent space, LSSAMP can simultaneously generate peptides with ideal sequence attributes and secondary structures.
Learning from Mistakes via Cooperative Study Assistant for Large Language Models
In this paper, we propose Study Assistant for Large LAnguage Model (SALAM), a novel framework with an auxiliary agent to assist the main LLM in learning from mistakes through interactive cooperation.
Permute-and-Flip: An optimally robust and watermarkable decoder for LLMs
In this paper, we propose a new decoding method called Permute-and-Flip (PF) decoder.
Just ClozE! A Novel Framework for Evaluating the Factual Consistency Faster in Abstractive Summarization
The issue of factual consistency in abstractive summarization has received extensive attention in recent years, and the evaluation of factual consistency between summary and document has become an important and urgent task.
Generalizable Local Feature Pre-training for Deformable Shape Analysis
We observe that with proper training, learned features can be useful in such tasks, but, crucially, only with an appropriate choice of the receptive field size.
Importance Weighted Expectation-Maximization for Protein Sequence Design
How can we efficiently generate diverse and novel protein sequences with high fitness?
Progression Cognition Reinforcement Learning with Prioritized Experience for Multi-Vehicle Pursuit
For the more challenging problem of pursuing multiple evading vehicles, these algorithms typically select a fixed target evading vehicle for pursuing vehicles without considering dynamic traffic situation, which significantly reduces pursuing success rate.
Ada-Retrieval: An Adaptive Multi-Round Retrieval Paradigm for Sequential Recommendations
However, most existing retrieval models employ a single-round inference paradigm, which may not adequately capture the dynamic nature of user preferences and stuck in one area in the item space.
In Conclusion Not Repetition: Comprehensive Abstractive Summarization With Diversified Attention Based On Determinantal Point Processes
Various Seq2Seq learning models designed for machine translation were applied for abstractive summarization task recently.
Probabilistic Graph Reasoning for Natural Proof Generation
In this paper, we propose PRobr, a novel approach for joint answer prediction and proof generation.
MTG: A Benchmark Suite for Multilingual Text Generation
We introduce MTG, a new benchmark suite for training and evaluating multilingual text generation.
Gradient Knowledge Distillation for Pre-trained Language Models
Knowledge distillation (KD) is an effective framework to transfer knowledge from a large-scale teacher to a compact yet well-performing student.
MARS: Markov Molecular Sampling for Multi-objective Drug Discovery
Searching for novel molecules with desired chemical properties is crucial in drug discovery.
Unsupervised Multi-Modality Registration Network based on Spatially Encoded Gradient Information
However, it is still challenging to develop a multi-modality registration network due to the lack of robust criteria for network training.
WACO: Word-Aligned Contrastive Learning for Speech Translation
In this paper, we propose Word-Aligned COntrastive learning (WACO), a simple and effective method for extremely low-resource speech-to-text translation.
ACMo: Angle-Calibrated Moment Methods for Stochastic Optimization
Due to its simplicity and outstanding ability to generalize, stochastic gradient descent (SGD) is still the most widely used optimization method despite its slow convergence.
Subjective Bias in Abstractive Summarization
Due to the subjectivity of the summarization, it is a good practice to have more than one gold summary for each training document.
Provably Confidential Language Modelling
Large language models are shown to memorize privacy information such as social security numbers in training data.
On the Impact of Noises in Crowd-Sourced Data for Speech Translation
Training speech translation (ST) models requires large and high-quality datasets.
AutoPlan: Automatic Planning of Interactive Decision-Making Tasks With Large Language Models
However, LLMs frequently fail in complex decision-making tasks due to the misalignment between the pre-trained knowledge in LLMs and the actual rules in the environment.
Autocorrect in the Process of Translation -- Multi-task Learning Improves Dialogue Machine Translation
Automatic translation of dialogue texts is a much needed demand in many real life scenarios.
Improving Factual Error Correction for Abstractive Summarization via Data Distillation and Conditional-generation Cloze
Improving factual consistency in abstractive summarization has been a focus of current research.
DE-COP: Detecting Copyrighted Content in Language Models Training Data
We are motivated by the premise that a language model is likely to identify verbatim excerpts from its training text.
CHOPS: CHat with custOmer Profile Systems for Customer Service with LLMs
We have conducted extensive experiments to validate the performance of our proposed CHOPS architecture using the CPHOS-dataset, with the aim of demonstrating how LLMs can enhance or serve as alternatives to human customer service.
Reinforced Co-Training
However, the selection of samples in existing co-training methods is based on a predetermined policy, which ignores the sampling bias between the unlabeled and the labeled subsets, and fails to explore the data space.
On the diffusion approximation of nonconvex stochastic gradient descent
In addition, we discuss the effects of batch size for the deep neural networks, and we find that small batch size is helpful for SGD algorithms to escape unstable stationary points and sharp minimizers.
SAM: Semantic Attribute Modulation for Language Modeling and Style Variation
This paper presents a Semantic Attribute Modulation (SAM) for language modeling and style variation.
Fully Automatic Segmentation and Objective Assessment of Atrial Scars for Longstanding Persistent Atrial Fibrillation Patients Using Late Gadolinium-Enhanced MRI
In this study, we proposed a novel fully automatic pipeline to achieve an accurate and objective atrial scarring segmentation and assessment of LGE MRI scans for the AF patients.
Multiview Cauchy Estimator Feature Embedding for Depth and Inertial Sensor-Based Human Action Recognition
Since human actions can be characterized by multiple feature representations extracted from Kinect and inertial sensors, multiview features must be encoded into a unified space optimal for human action recognition.
Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural Network
The qualitative and quantitative evaluations of experimental results indicate that the proposed method show a stable and prospective performance on artifacts reduction and detail recovery for limited angle tomography.
CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases
We propose CFO, a Conditional Focused neural-network-based approach to answering factoid questions with knowledge bases.
Swift: Compiled Inference for Probabilistic Programming Languages
A probabilistic program defines a probability measure over its semantic structures.
Towards Practical Bayesian Parameter and State Estimation
Joint state and parameter estimation is a core problem for dynamic Bayesian networks.
An Open Source Testing Tool for Evaluating Handwriting Input Methods
This paper presents an open source tool for testing the recognition accuracy of Chinese handwriting input methods.
The Extended Parameter Filter
Drawing on an analogy to the extended Kalman filter, we develop and analyze, both theoretically and experimentally, a Taylor approximation to the parameter posterior that allows Storvik's method to be applied to a broader class of models.
Atrial scars segmentation via potential learning in the graph-cuts framework
Late Gadolinium Enhancement Magnetic Resonance Imaging (LGE MRI) emerged as a routine scan for patients with atrial fibrillation (AF).
Atrial fibrosis quantification based on maximum likelihood estimator of multivariate images
We present a fully-automated segmentation and quantification of the left atrial (LA) fibrosis and scars combining two cardiac MRIs, one is the target late gadolinium-enhanced (LGE) image, and the other is an anatomical MRI from the same acquisition session.
Face Presentation Attack Detection in Learned Color-liked Space
Face presentation attack detection (PAD) has become a thorny problem for biometric systems and numerous countermeasures have been proposed to address it.
Face Presentation Attack Detection
Generative Adversarial Network
Towards Linear Time Neural Machine Translation with Capsule Networks
In this study, we first investigate a novel capsule network with dynamic routing for linear time Neural Machine Translation (NMT), referred as \textsc{CapsNMT}.
Sketch-R2CNN: An Attentive Network for Vector Sketch Recognition
To bridge the gap between these two spaces in neural networks, we propose a neural line rasterization module to convert the vector sketch along with the attention estimated by RNN into a bitmap image, which is subsequently consumed by CNN.
Word Embedding and Topic Modeling Enhanced Multiple Features for Content Linking and Argument / Sentiment Labeling in Online Forums
Multiple grammatical and semantic features are adopted in content linking and argument/sentiment labeling for online forums in this paper.
Enhancing Automatic ICD-9-CM Code Assignment for Medical Texts with PubMed
Frequent diseases often have more training data, which helps its classification to perform better than that of an infrequent disease.
