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Found 62 papers, 23 papers with code
Learning Event-Driven Video Deblurring and Interpolation
Event-based sensors, which have a response if the change of pixel intensity exceeds a triggering threshold, can capture high-speed motion with microsecond accuracy.
Lexicon of Changes: Towards the Evaluation of Diachronic Semantic Shift in Chinese
Recent research has brought a wind of using computational approaches to the classic topic of semantic change, aiming to tackle one of the most challenging issues in the evolution of human language.
ALSS-YOLO: An Adaptive Lightweight Channel Split and Shuffling Network for TIR Wildlife Detection in UAV Imagery
This improves the extraction of blurry features, crucial for handling jitter-induced blur and overlapping targets.
Privacy-preserving Universal Adversarial Defense for Black-box Models
Comparative evaluations between the certified defenses of the surrogate and target models demonstrate the effectiveness of our approach.
Neural Architecture Search based Global-local Vision Mamba for Palm-Vein Recognition
In this paper, first, we propose a hybrid network structure named Global-local Vision Mamba (GLVM), to learn the local correlations in images explicitly and global dependencies among tokens for vein feature representation.
SG-JND: Semantic-Guided Just Noticeable Distortion Predictor For Image Compression
Just noticeable distortion (JND), representing the threshold of distortion in an image that is minimally perceptible to the human visual system (HVS), is crucial for image compression algorithms to achieve a trade-off between transmission bit rate and image quality.
ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models
To address this challenge, we introduce a comprehensive diagnostic benchmark, ToolBH.
HoLLMwood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing
Generative AI has demonstrated unprecedented creativity in the field of computer vision, yet such phenomena have not been observed in natural language processing.
Beware of Overestimated Decoding Performance Arising from Temporal Autocorrelations in Electroencephalogram Signals
Since these EEG data were usually collected with well-designed paradigms in labs, the reliability and robustness of the corresponding decoding methods were doubted by some researchers, and they argued that such decoding accuracy was overestimated due to the inherent temporal autocorrelation of EEG signals.
Cons-training tensor networks
In this study, we introduce a novel family of tensor networks, termed \textit{constrained matrix product states} (MPS), designed to incorporate exactly arbitrary discrete linear constraints, including inequalities, into sparse block structures.
CLAD: Robust Audio Deepfake Detection Against Manipulation Attacks with Contrastive Learning
The detection models exhibited vulnerabilities, with FAR rising to 36. 69%, 31. 23%, and 51. 28% under volume control, fading, and noise injection, respectively.
Towards Independence Criterion in Machine Unlearning of Features and Labels
This work delves into the complexities of machine unlearning in the face of distributional shifts, particularly focusing on the challenges posed by non-uniform feature and label removal.
Human Activity Recognition with Low-Resolution Infrared Array Sensor Using Semi-supervised Cross-domain Neural Networks for Indoor Environment
The label classifier obtained from training the source domain data improves the recognition of target domain activities due to the semi-supervised learning utilized in training the target domain data.
PowerSkel: A Device-Free Framework Using CSI Signal for Human Skeleton Estimation in Power Station
WiFi-based human pose estimation is a suitable method for monitoring power operations due to its low cost, device-free, and robustness to various illumination conditions. In this paper, a novel Channel State Information (CSI)-based pose estimation framework, namely PowerSkel, is developed to address these challenges.
Crafting a Good Prompt or Providing Exemplary Dialogues? A Study of In-Context Learning for Persona-based Dialogue Generation
Previous in-context learning (ICL) research has focused on tasks such as classification, machine translation, text2table, etc., while studies on whether ICL can improve human-like dialogue generation are scarce.
Wavelet-Decoupling Contrastive Enhancement Network for Fine-Grained Skeleton-Based Action Recognition
Skeleton-based action recognition has attracted much attention, benefiting from its succinctness and robustness.
ConvConcatNet: a deep convolutional neural network to reconstruct mel spectrogram from the EEG
To investigate the processing of speech in the brain, simple linear models are commonly used to establish a relationship between brain signals and speech features.
Self-supervised speech representation and contextual text embedding for match-mismatch classification with EEG recording
Contrastive learning was used to relate EEG features to speech features.
Stockformer: A Price-Volume Factor Stock Selection Model Based on Wavelet Transform and Multi-Task Self-Attention Networks
As the Chinese stock market continues to evolve and its market structure grows increasingly complex, traditional quantitative trading methods are facing escalating challenges.
Generative Learning of Continuous Data by Tensor Networks
Beyond their origin in modeling many-body quantum systems, tensor networks have emerged as a promising class of models for solving machine learning problems, notably in unsupervised generative learning.
A DenseNet-based method for decoding auditory spatial attention with EEG
ASAD methods are inspired by the brain lateralization of cortical neural responses during the processing of auditory spatial attention, and show promising performance for the task of auditory attention decoding (AAD) with neural recordings.
Agents: An Open-source Framework for Autonomous Language Agents
Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces.
Exploring the Intersection of Complex Aesthetics and Generative AI for Promoting Cultural Creativity in Rural China after the Post-Pandemic Era
This paper explores using generative AI and aesthetics to promote cultural creativity in rural China amidst COVID-19's impact.
What can Discriminator do? Towards Box-free Ownership Verification of Generative Adversarial Network
In recent decades, Generative Adversarial Network (GAN) and its variants have achieved unprecedented success in image synthesis.
Drugst.One -- A plug-and-play solution for online systems medicine and network-based drug repurposing
In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood.
LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities
We engage in experiments across eight diverse datasets, focusing on four representative tasks encompassing entity and relation extraction, event extraction, link prediction, and question-answering, thereby thoroughly exploring LLMs' performance in the domain of construction and inference.
Online Learning Under A Separable Stochastic Approximation Framework
In one routine of the proposed algorithm, the linear parameters are updated by the recursive least squares (RLS) algorithm, which is equivalent to a stochastic Newton method; then, based on the updated linear parameters, the nonlinear parameters are updated by the stochastic gradient method (SGD).
CodeKGC: Code Language Model for Generative Knowledge Graph Construction
However, large generative language model trained on structured data such as code has demonstrated impressive capability in understanding natural language for structural prediction and reasoning tasks.
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).
What can Discriminator do? Towards Box-free Ownership Verification of Generative Adversarial Networks
In recent decades, Generative Adversarial Network (GAN) and its variants have achieved unprecedented success in image synthesis.
EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks
Methods: This present study provided a new publicly available Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset for Image Segmentation Tasks (EBHI-Seg).
GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation Model
High-concurrency asynchronous training upon parameter server (PS) architecture and high-performance synchronous training upon all-reduce (AR) architecture are the most commonly deployed distributed training modes for recommendation models.
Relphormer: Relational Graph Transformer for Knowledge Graph Representations
To this end, we propose a new variant of Transformer for knowledge graph representations dubbed Relphormer.
Ranked #3 on
Link Prediction
on FB15k-237
Trajectory Prediction with Graph-based Dual-scale Context Fusion
In this paper, we present a graph-based trajectory prediction network named the Dual Scale Predictor (DSP), which encodes both the static and dynamical driving context in a hierarchical manner.
Backdoor Pre-trained Models Can Transfer to All
However, a pre-trained model with backdoor can be a severe threat to the applications.
Eye-gaze Estimation with HEOG and Neck EMG using Deep Neural Networks
These results indicated the feasibility to estimate eye-gaze with HEOG and NEMG.
Auditory Attention Decoding from EEG using Convolutional Recurrent Neural Network
In this paper, we proposed novel convolutional recurrent neural network (CRNN) based regression model and classification model, and compared them with both the linear model and the state-of-the-art DNN models.
Computational efficient deep neural network with difference attention maps for facial action unit detection
A large number of experimental results show that the proposed CEDNN is obviously better than the traditional deep learning method on DISFA+ and CK+ datasets.
HEU Emotion: A Large-scale Database for Multi-modal Emotion Recognition in the Wild
Existing multimodal emotion databases in the real-world conditions are few and small, with a limited number of subjects and expressed in a single language.
Review of data analysis in vision inspection of power lines with an in-depth discussion of deep learning technology
With the aim of providing a comprehensive overview for researchers who are interested in developing a deep-learning-based analysis system for power lines inspection data, this paper conducts a thorough review of the current literature and identifies the challenges for future research.
Efficient Uncertainty-aware Decision-making for Automated Driving Using Guided Branching
Decision-making in dense traffic scenarios is challenging for automated vehicles (AVs) due to potentially stochastic behaviors of other traffic participants and perception uncertainties (e. g., tracking noise and prediction errors, etc.).
Robotics
A Heterogeneous Graphical Model to Understand User-Level Sentiments in Social Media
Social Media has seen a tremendous growth in the last decade and is continuing to grow at a rapid pace.
How to Ask Better Questions? A Large-Scale Multi-Domain Dataset for Rewriting Ill-Formed Questions
We present a large-scale dataset for the task of rewriting an ill-formed natural language question to a well-formed one.
Received Signal Strength Based Wireless Source Localization with Inaccurate Anchor Position
Received signal strength (RSS)-based wireless localization is easy to implement at low cost.
Safe Trajectory Generation for Complex Urban Environments Using Spatio-temporal Semantic Corridor
Planning safe trajectories for autonomous vehicles in complex urban environments is challenging since there are numerous semantic elements (such as dynamic agents, traffic lights and speed limits) to consider.
A Books Recommendation Approach Based on Online Bookstore Data
In the era of information explosion, facing complex information, it is difficult for users to choose the information of interest, and businesses also need detailed information on ways to let the ad stand out.
Deep Surface Normal Estimation with Hierarchical RGB-D Fusion
The growing availability of commodity RGB-D cameras has boosted the applications in the field of scene understanding.
Predicting Vehicle Behaviors Over An Extended Horizon Using Behavior Interaction Network
In this paper, we uncover that clues to vehicle behaviors over an extended horizon can be found in vehicle interaction, which makes it possible to anticipate the likelihood of a certain behavior, even in the absence of any clear maneuver pattern.
Biophysics at the coffee shop: lessons learned working with George Oster
Over the past 50 years, the use of mathematical models, derived from physical reasoning, to describe molecular and cellular systems has evolved from an art of the few to a cornerstone of biological inquiry.
Generative Adversarial Network based Speaker Adaptation for High Fidelity WaveNet Vocoder
Firstly, due to the noisy input signal of the model, there is still a gap between the quality of generated and natural waveforms.
Generative Adversarial Network
Vocal Bursts Intensity Prediction
The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification
As the largest manually annotated public Chinese SSEI corpus in the bank domain, the BQ corpus is not only useful for Chinese question semantic matching research, but also a significant resource for cross-lingual and cross-domain SSEI research.
LCQMC:A Large-scale Chinese Question Matching Corpus
In this paper, we first use a search engine to collect large-scale question pairs related to high-frequency words from various domains, then filter irrelevant pairs by the Wasserstein distance, and finally recruit three annotators to manually check the left pairs.
Learning-based Natural Geometric Matching with Homography Prior
Then, we parametrize homography transformation with 9 parameters in full connected layer of our network, to better characterize large viewpoint variations compared with affine transformation.
SmartSeed: Smart Seed Generation for Efficient Fuzzing
In total, our system discovers more than twice unique crashes and 5, 040 extra unique paths than the existing best seed selection strategy for the evaluated 12 applications.
Cryptography and Security
Peperomia at SemEval-2018 Task 2: Vector Similarity Based Approach for Emoji Prediction
First the distributed representation (tweet vector) for each tweet is generated, then the similarity between this tweet vector and each emoji{'}s embedding is evaluated.
Coarse-to-fine Seam Estimation for Image Stitching
For pixels on the seam, we develop a patch-point evaluation algorithm concentrating more on the correlation and variation of them.
Graph-based Hypothesis Generation for Parallax-tolerant Image Stitching
The seam-driven approach has been proven fairly effective for parallax-tolerant image stitching, whose strategy is to search for an invisible seam from finite representative hypotheses of local alignment.
Ratio-Preserving Half-Cylindrical Warps for Natural Image Stitching
The pixel selection strategy then samples the points in horizontal and reconstructs the image via interpolation to further reduce horizontal distortion by maintaining the ratio as similarity.
Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines
We compare and contrast the statistical physics and quantum physics inspired approaches for unsupervised generative modeling of classical data.
Equivalence of restricted Boltzmann machines and tensor network states
Conversely, we give sufficient and necessary conditions to determine whether a TNS can be transformed into an RBM of given architectures.
Unsupervised Cross-Domain Recognition by Identifying Compact Joint Subspaces
By adopting a natural and widely used assumption -- "the data samples from the same class should lay on a low-dimensional subspace, even if they come from different domains", the proposed method circumvents the limitation of the global domain shift, and solves the cross-domain recognition by finding the compact joint subspaces of source and target domain.
