Search Results for author:
Found 258 papers, 96 papers with code
COPNER: Contrastive Learning with Prompt Guiding for Few-shot Named Entity Recognition
Second, the referents may be far from representing corresponding entity classes due to the label scarcity in the few-shot setting.
Chinese Grammatical Error Diagnosis with Graph Convolution Network and Multi-task Learning
For the correction subtask, we utilize the masked language model, the seq2seq model and the spelling check model to generate corrections based on the detection results.
TAD-Bench: A Comprehensive Benchmark for Embedding-Based Text Anomaly Detection
Text anomaly detection is crucial for identifying spam, misinformation, and offensive language in natural language processing tasks.
UI-TARS: Pioneering Automated GUI Interaction with Native Agents
This paper introduces UI-TARS, a native GUI agent model that solely perceives the screenshots as input and performs human-like interactions (e. g., keyboard and mouse operations).
HUNYUANPROVER: A Scalable Data Synthesis Framework and Guided Tree Search for Automated Theorem Proving
We introduce HunyuanProver, an language model finetuned from the Hunyuan 7B for interactive automatic theorem proving with LEAN4.
Gx2Mol: De Novo Generation of Hit-like Molecules from Gene Expression Profiles via Deep Learning
De novo generation of hit-like molecules is a challenging task in the drug discovery process.
DISC: Plug-and-Play Decoding Intervention with Similarity of Characters for Chinese Spelling Check
One key characteristic of the Chinese spelling check (CSC) task is that incorrect characters are usually similar to the correct ones in either phonetics or glyph.
RemDet: Rethinking Efficient Model Design for UAV Object Detection
On the challenging UAV dataset VisDrone, our methods not only provided state-of-the-art results, improving detection by more than 3. 4%, but also achieve 110 FPS on a single 4090.
Reinforcement Learning Within the Classical Robotics Stack: A Case Study in Robot Soccer
Robot decision-making in partially observable, real-time, dynamic, and multi-agent environments remains a difficult and unsolved challenge.
TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Model
Accurately modeling multi-class cell topology is crucial in digital pathology, as it provides critical insights into tissue structure and pathology.
Efficient Deployment of Transformer Models in Analog In-Memory Computing Hardware
Moreover, it supports on-chip adaptation to new hardware constraints and tasks without updating analog weights, providing a flexible and versatile solution for real-world AI applications.
Noise Adaptor: Enhancing Low-Latency Spiking Neural Networks through Noise-Injected Low-Bit ANN Conversion
We present Noise Adaptor, a novel method for constructing competitive low-latency spiking neural networks (SNNs) by converting noise-injected, low-bit artificial neural networks (ANNs).
Morph: A Motion-free Physics Optimization Framework for Human Motion Generation
Human motion generation plays a vital role in applications such as digital humans and humanoid robot control.
Stacking Brick by Brick: Aligned Feature Isolation for Incremental Face Forgery Detection
Incremental Face Forgery Detection (IFFD), involving gradually adding new forgery data to fine-tune the previously trained model, has been introduced as a promising strategy to deal with evolving forgery methods.
DiHuR: Diffusion-Guided Generalizable Human Reconstruction
We introduce DiHuR, a novel Diffusion-guided model for generalizable Human 3D Reconstruction and view synthesis from sparse, minimally overlapping images.
PPLLaVA: Varied Video Sequence Understanding With Prompt Guidance
In this paper, we identify the key issue as the redundant content in videos.
Situational Scene Graph for Structured Human-centric Situation Understanding
Based on our proposed representation, we introduce the task of situational scene graph generation and propose a multi-stage pipeline Interactive and Complementary Network (InComNet) to address the task.
MVSDet: Multi-View Indoor 3D Object Detection via Efficient Plane Sweeps
However, the geometry extracted from NeRF is generally inaccurate, which leads to sub-optimal detection performance.
Evaluating AI-Generated Essays with GRE Analytical Writing Assessment
The recent revolutionary advance in generative AI enables the generation of realistic and coherent texts by large language models (LLMs).
BELM: Bidirectional Explicit Linear Multi-step Sampler for Exact Inversion in Diffusion Models
In this paper, we introduce a generic formulation, \emph{Bidirectional Explicit Linear Multi-step} (BELM) samplers, of the exact inversion samplers, which includes all previously proposed heuristic exact inversion samplers as special cases.
Hardware-Software Co-optimised Fast and Accurate Deep Reconfigurable Spiking Inference Accelerator Architecture Design Methodology
In this paper, we develop a hardware-software co-optimisation strategy to port software-trained deep neural networks (DNN) to reduced-precision spiking models demonstrating fast and accurate inference in a novel event-driven CMOS reconfigurable spiking inference accelerator.
A Simple yet Effective Training-free Prompt-free Approach to Chinese Spelling Correction Based on Large Language Models
This work proposes a simple training-free prompt-free approach to leverage large language models (LLMs) for the Chinese spelling correction (CSC) task, which is totally different from all previous CSC approaches.
Tailored Federated Learning: Leveraging Direction Regulation & Knowledge Distillation
To address such a challenge, we propose an FL optimization algorithm that integrates model delta regularization, personalized models, federated knowledge distillation, and mix-pooling.
When Molecular GAN Meets Byte-Pair Encoding
Deep generative models, such as generative adversarial networks (GANs), are pivotal in discovering novel drug-like candidates via de novo molecular generation.
Spatial Visibility and Temporal Dynamics: Revolutionizing Field of View Prediction in Adaptive Point Cloud Video Streaming
Such approaches do not explicitly consider video content's impact on viewer attention, and the conversion from FoV to point visibility is often error-prone and time-consuming.
Leveraging Estimated Transferability Over Human Intuition for Model Selection in Text Ranking
As a promising alternative to human intuition and brute-force fine-tuning, Transferability Estimation (TE) has emerged as an effective approach to model selection.
Spatial Diffusion for Cell Layout Generation
In downstream tasks, we show that the generated cell layouts can be used to guide the generation of high-quality pathology images.
Can We Leave Deepfake Data Behind in Training Deepfake Detector?
The accumulation of forgery information should be oriented and progressively increasing during this transition process.
HistoGym: A Reinforcement Learning Environment for Histopathological Image Analysis
In pathological research, education, and clinical practice, the decision-making process based on pathological images is critically important.
DePatch: Towards Robust Adversarial Patch for Evading Person Detectors in the Real World
Recent years have seen an increasing interest in physical adversarial attacks, which aim to craft deployable patterns for deceiving deep neural networks, especially for person detectors.
ED$^4$: Explicit Data-level Debiasing for Deepfake Detection
Learning intrinsic bias from limited data has been considered the main reason for the failure of deepfake detection with generalizability.
TreeSBA: Tree-Transformer for Self-Supervised Sequential Brick Assembly
Based on the LEGO-Tree structure, we then design a class-agnostic tree-transformer framework to predict the sequential assembly actions from the input multi-view images.
We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?
More notably, the primary challenge of GPT-4o has significantly transitioned from IK to IG, establishing it as the first LMM advancing towards the knowledge generalization stage.
PFME: A Modular Approach for Fine-grained Hallucination Detection and Editing of Large Language Models
In editing tasks, PFME further enhances the FActScore of FActScore-Alpaca13B and FActScore-ChatGPT datasets, increasing by 16. 2pp and 4. 6pp, respectively.
How Does Distribution Matching Help Domain Generalization: An Information-theoretic Analysis
Domain generalization aims to learn invariance across multiple training domains, thereby enhancing generalization against out-of-distribution data.
Generalizable Human Gaussians from Single-View Image
To mitigate the potential generation of unrealistic human poses and shapes, we incorporate human priors from the SMPL-X model as a dual branch, propagating image features from the SMPL-X volume to the image Gaussians using sparse convolution and attention mechanisms.
Mamba YOLO: A Simple Baseline for Object Detection with State Space Model
Our contributions are as follows: 1) We propose that the ODMamba backbone introduce a \textbf{S}tate \textbf{S}pace \textbf{M}odel (\textbf{SSM}) with linear complexity to address the quadratic complexity of self-attention.
VCR-GauS: View Consistent Depth-Normal Regularizer for Gaussian Surface Reconstruction
Although 3D Gaussian Splatting has been widely studied because of its realistic and efficient novel-view synthesis, it is still challenging to extract a high-quality surface from the point-based representation.
Medication Recommendation via Dual Molecular Modalities and Multi-Step Enhancement
Existing works based on molecular knowledge neglect the 3D geometric structure of molecules and fail to learn the high-dimensional information of medications, leading to structural confusion.
LabObf: A Label Protection Scheme for Vertical Federated Learning Through Label Obfuscation
In this paper, we first propose an embedding extension attack manipulating embeddings to undermine existing defense strategies, which rely on constraining the correlation between the embeddings uploaded by participants and the labels.
Detecting Adversarial Data via Perturbation Forgery
By learning to distinguish this open covering from the distribution of natural data, we can develop a detector with strong generalization capabilities against all types of adversarial attacks.
Multi-View Attentive Contextualization for Multi-View 3D Object Detection
We present Multi-View Attentive Contextualization (MvACon), a simple yet effective method for improving 2D-to-3D feature lifting in query-based multi-view 3D (MV3D) object detection.
SEED-Data-Edit Technical Report: A Hybrid Dataset for Instructional Image Editing
In this technical report, we introduce SEED-Data-Edit: a unique hybrid dataset for instruction-guided image editing, which aims to facilitate image manipulation using open-form language.
SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation
We hope that our work will inspire future research into what can be achieved by versatile multimodal foundation models in real-world applications.
Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior Recommendation
This model uses relationships in the knowledge graph to construct intents, aiming to mine the connections between users' multi-behaviors from the perspective of intents to achieve more accurate recommendations.
CausalMed: Causality-Based Personalized Medication Recommendation Centered on Patient health state
However, these methods are limited to capturing personalized patient representations due to the following primary limitations: (i) unable to capture the differences in the impact of diseases/procedures on patients across various patient health states; (ii) fail to model the direct causal relationships between medications and specific health state of patients, resulting in an inability to determine which specific disease each medication is treating.
Closely Interactive Human Reconstruction with Proxemics and Physics-Guided Adaption
Specifically, we first design a latent representation based on Vector Quantised-Variational AutoEncoder (VQ-VAE) to model human interaction.
Portrait3D: Text-Guided High-Quality 3D Portrait Generation Using Pyramid Representation and GANs Prior
Existing neural rendering-based text-to-3D-portrait generation methods typically make use of human geometry prior and diffusion models to obtain guidance.
Advancing Aspect-Based Sentiment Analysis through Deep Learning Models
This study introduces an innovative edge-enhanced GCN, named SentiSys, to navigate the syntactic graph while preserving intact feature information, leading to enhanced performance.
DiSR-NeRF: Diffusion-Guided View-Consistent Super-Resolution NeRF
We further introduce Renoised Score Distillation (RSD), a novel score-distillation objective for 2D image resolution.
ST-LLM: Large Language Models Are Effective Temporal Learners
In this paper, we investigate a straightforward yet unexplored question: Can we feed all spatial-temporal tokens into the LLM, thus delegating the task of video sequence modeling to the LLMs?
Molecular Generative Adversarial Network with Multi-Property Optimization
Deep generative models, such as generative adversarial networks (GANs), have been employed for $de~novo$ molecular generation in drug discovery.
Enhancing Industrial Transfer Learning with Style Filter: Cost Reduction and Defect-Focus
Addressing the challenge of data scarcity in industrial domains, transfer learning emerges as a pivotal paradigm.
mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding
In this work, we emphasize the importance of structure information in Visual Document Understanding and propose the Unified Structure Learning to boost the performance of MLLMs.
Tackling the Singularities at the Endpoints of Time Intervals in Diffusion Models
Most diffusion models assume that the reverse process adheres to a Gaussian distribution.
Enhancing Multi-Hop Knowledge Graph Reasoning through Reward Shaping Techniques
In the realm of computational knowledge representation, Knowledge Graph Reasoning (KG-R) stands at the forefront of facilitating sophisticated inferential capabilities across multifarious domains.
Stochastic Analysis of Touch-Tone Frequency Recognition in Two-Way Radio Systems for Dialed Telephone Number Identification
This paper focuses on recognizing dialed numbers in a touch-tone telephone system based on the Dual Tone MultiFrequency (DTMF) signaling technique with analysis of stochastic aspects during the noise and random duration of characters.
Common 7B Language Models Already Possess Strong Math Capabilities
This paper shows that the LLaMA-2 7B model with common pre-training already exhibits strong mathematical abilities, as evidenced by its impressive accuracy of 97. 7% and 72. 0% on the GSM8K and MATH benchmarks, respectively, when selecting the best response from 256 random generations.
CIDGMed: Causal Inference-Driven Medication Recommendation with Enhanced Dual-Granularity Learning
Medication recommendation aims to integrate patients' long-term health records to provide accurate and safe medication combinations for specific health states.
Partially Recentralization Softmax Loss for Vision-Language Models Robustness
However, it has been shown that multimodal NLP are vulnerable to adversarial attacks, where the outputs of a model can be dramatically changed by a perturbation to the input.
Bayesian Inference Accelerator for Spiking Neural Networks
Bayesian neural networks offer better estimates of model uncertainty compared to frequentist networks.
Consensus Focus for Object Detection and minority classes
Ensemble methods exploit the availability of a given number of classifiers or detectors trained in single or multiple source domains and tasks to address machine learning problems such as domain adaptation or multi-source transfer learning.
SwitchTab: Switched Autoencoders Are Effective Tabular Learners
Self-supervised representation learning methods have achieved significant success in computer vision and natural language processing, where data samples exhibit explicit spatial or semantic dependencies.
ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image Classification
Specifically we propose a dual-scale visual descriptive text prompt based on the frozen large language model (LLM) to boost the performance of VLM effectively.
ESCAPE: Encoding Super-keypoints for Category-Agnostic Pose Estimation
For the first step we propose a learnable matching network to capture the relationship between the novel keypoints and the super-keypoints resulting in a more reliable matching.
Practical Measurements of Translucent Materials with Inter-Pixel Translucency Prior
Material appearance is a key component of photorealism with a pronounced impact on human perception.
Multi-Granularity Information Interaction Framework for Incomplete Utterance Rewriting
Recent approaches in Incomplete Utterance Rewriting (IUR) fail to capture the source of important words, which is crucial to edit the incomplete utterance, and introduce words from irrelevant utterances.
Relation-Aware Question Answering for Heterogeneous Knowledge Graphs
To address this issue, we construct a \textbf{dual relation graph} where each node denotes a relation in the original KG (\textbf{primal entity graph}) and edges are constructed between relations sharing same head or tail entities.
External Knowledge Augmented Polyphone Disambiguation Using Large Language Model
One of the key issues in Mandarin Chinese text-to-speech (TTS) systems is polyphone disambiguation when doing grapheme-to-phoneme (G2P) conversion.
Noise Adaptor in Spiking Neural Networks
Our research utilizes the ResNet model for a comprehensive analysis of the impact of the noise adaptor on low-latency SNNs.
How to Determine the Most Powerful Pre-trained Language Model without Brute Force Fine-tuning? An Empirical Survey
However, there is a lack of a comprehensive comparison between these estimation methods yet.
NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidance
In this work, we propose a neural implicit surface reconstruction pipeline with guidance from 3D Gaussian Splatting to recover highly detailed surfaces.
Optimal Power Flow in Highly Renewable Power System Based on Attention Neural Networks
The Optimal Power Flow (OPF) problem is pivotal for power system operations, guiding generator output and power distribution to meet demand at minimized costs, while adhering to physical and engineering constraints.
Vision-Language Instruction Tuning: A Review and Analysis
Instruction tuning is a crucial supervised training phase in Large Language Models (LLMs), aiming to enhance the LLM's ability to generalize instruction execution and adapt to user preferences.
FP8-LM: Training FP8 Large Language Models
In this paper, we explore FP8 low-bit data formats for efficient training of large language models (LLMs).
Improving Seq2Seq Grammatical Error Correction via Decoding Interventions
In this paper, we propose a unified decoding intervention framework that employs an external critic to assess the appropriateness of the token to be generated incrementally, and then dynamically influence the choice of the next token.
Ranked #1 on
Grammatical Error Correction
on MuCGEC
Making LLaMA SEE and Draw with SEED Tokenizer
We identify two crucial design principles: (1) Image tokens should be independent of 2D physical patch positions and instead be produced with a 1D causal dependency, exhibiting intrinsic interdependence that aligns with the left-to-right autoregressive prediction mechanism in LLMs.
BT-Adapter: Video Conversation is Feasible Without Video Instruction Tuning
Without bells and whistles, BT-Adapter achieves (1) state-of-the-art zero-shot results on various video tasks using thousands of fewer GPU hours.
Ranked #4 on
VCGBench-Diverse
on VideoInstruct
Video-based Generative Performance Benchmarking (Consistency)
Video-based Generative Performance Benchmarking (Contextual Understanding)
+6
InstructDiffusion: A Generalist Modeling Interface for Vision Tasks
We present InstructDiffusion, a unifying and generic framework for aligning computer vision tasks with human instructions.
A Visual Interpretation-Based Self-Improved Classification System Using Virtual Adversarial Training
The successful application of large pre-trained models such as BERT in natural language processing has attracted more attention from researchers.
GHuNeRF: Generalizable Human NeRF from a Monocular Video
In view of these limitations, we propose GHuNeRF to learn a generalizable human NeRF model from a monocular video of the human performer.
PHE-SICH-CT-IDS: A Benchmark CT Image Dataset for Evaluation Semantic Segmentation, Object Detection and Radiomic Feature Extraction of Perihematomal Edema in Spontaneous Intracerebral Hemorrhage
This study establishes a publicly available CT dataset named PHE-SICH-CT-IDS for perihematomal edema in spontaneous intracerebral hemorrhage.
Calibrating Uncertainty for Semi-Supervised Crowd Counting
A popular approach is to iteratively generate pseudo-labels for unlabeled data and add them to the training set.
AATCT-IDS: A Benchmark Abdominal Adipose Tissue CT Image Dataset for Image Denoising, Semantic Segmentation, and Radiomics Evaluation
In the comparative study of semantic segmentation of abdominal adipose tissue, the segmentation results of adipose tissue by each model show different structural characteristics.
ECPC-IDS:A benchmark endometrail cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions
As far as we know, this is the first publicly available dataset of endometrial cancer with a large number of multiple images, including a large amount of information required for image and target detection.
DETR Doesn't Need Multi-Scale or Locality Design
This paper presents an improved DETR detector that maintains a "plain" nature: using a single-scale feature map and global cross-attention calculations without specific locality constraints, in contrast to previous leading DETR-based detectors that reintroduce architectural inductive biases of multi-scale and locality into the decoder.
Weakly-supervised 3D Pose Transfer with Keypoints
The main challenges of 3D pose transfer are: 1) Lack of paired training data with different characters performing the same pose; 2) Disentangling pose and shape information from the target mesh; 3) Difficulty in applying to meshes with different topologies.
Confidence Estimation Using Unlabeled Data
We stipulate that even with limited training labels, we can still reasonably approximate the confidence of model on unlabeled samples by inspecting the prediction consistency through the training process.
PTVD: A Large-Scale Plot-Oriented Multimodal Dataset Based on Television Dramas
Art forms such as movies and television (TV) dramas are reflections of the real world, which have attracted much attention from the multimodal learning community recently.
Causal Intervention for Measuring Confidence in Drug-Target Interaction Prediction
Identifying and discovering drug-target interactions(DTIs) are vital steps in drug discovery and development.
NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts
We introduce NaSGEC, a new dataset to facilitate research on Chinese grammatical error correction (CGEC) for native speaker texts from multiple domains.
OD-NeRF: Efficient Training of On-the-Fly Dynamic Neural Radiance Fields
Dynamic neural radiance fields (dynamic NeRFs) have demonstrated impressive results in novel view synthesis on 3D dynamic scenes.
A unified front-end framework for English text-to-speech synthesis
The front-end is a critical component of English text-to-speech (TTS) systems, responsible for extracting linguistic features that are essential for a text-to-speech model to synthesize speech, such as prosodies and phonemes.
NL-CS Net: Deep Learning with Non-Local Prior for Image Compressive Sensing
NL-CS Net is composed of the up-sampling module and the recovery module.
Ripple Knowledge Graph Convolutional Networks For Recommendation Systems
Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model's interpretability and accuracy.
Understanding the Generalization Ability of Deep Learning Algorithms: A Kernelized Renyi's Entropy Perspective
However, the current generalization error bounds within this framework are still far from optimal, while substantial improvements on these bounds are quite challenging due to the intractability of high-dimensional information quantities.
Overcoming the Trade-off Between Accuracy and Plausibility in 3D Hand Shape Reconstruction
However, the reconstructed meshes are prone to artifacts and do not appear as plausible hand shapes.
An Evidential Real-Time Multi-Mode Fault Diagnosis Approach Based on Broad Learning System
Fault diagnosis is a crucial area of research in industry.
TagGPT: Large Language Models are Zero-shot Multimodal Taggers
Given a new entity that needs tagging for distribution, TagGPT introduces two alternative options for zero-shot tagging, i. e., a generative method with late semantic matching with the tag set, and another selective method with early matching in prompts.
ScarceNet: Animal Pose Estimation with Scarce Annotations
To this end, we propose the ScarceNet, a pseudo label-based approach to generate artificial labels for the unlabeled images.
NeRF-DS: Neural Radiance Fields for Dynamic Specular Objects
We evaluate our model based on the novel view synthesis quality with a self-collected dataset of different moving specular objects in realistic environments.
Distribution-restrained Softmax Loss for the Model Robustness
Recently, the robustness of deep learning models has received widespread attention, and various methods for improving model robustness have been proposed, including adversarial training, model architecture modification, design of loss functions, certified defenses, and so on.
DataLight: Offline Data-Driven Traffic Signal Control
Reinforcement learning (RL) has emerged as a promising solution for addressing traffic signal control (TSC) challenges.
Unleashing the Potential of Spiking Neural Networks by Dynamic Confidence
In this regard, Dynamic Confidence represents a meaningful step toward realizing the potential of SNNs.
Efficient Diffusion Training via Min-SNR Weighting Strategy
Denoising diffusion models have been a mainstream approach for image generation, however, training these models often suffers from slow convergence.
Ranked #10 on
Image Generation
on ImageNet 256x256
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT
This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities.
QS-ADN: Quasi-Supervised Artifact Disentanglement Network for Low-Dose CT Image Denoising by Local Similarity Among Unpaired Data
Deep learning has been successfully applied to low-dose CT (LDCT) image denoising for reducing potential radiation risk.
ACTIVE: A Deep Model for Sperm and Impurity Detection in Microscopic Videos
The accurate detection of sperms and impurities is a very challenging task, facing problems such as the small size of targets, indefinite target morphologies, low contrast and resolution of the video, and similarity of sperms and impurities.
All in Tokens: Unifying Output Space of Visual Tasks via Soft Token
With these new techniques and other designs, we show that the proposed general-purpose task-solver can perform both instance segmentation and depth estimation well.
Ranked #24 on
Monocular Depth Estimation
on NYU-Depth V2
Unleashing the Potential of Spiking Neural Networks with Dynamic Confidence
In this regard, Dynamic Confidence represents a meaningful step toward realizing the potential of SNNs.
Weak-Shot Object Detection Through Mutual Knowledge Transfer
We propose a novel Knowledge Transfer (KT) loss which simultaneously distills the knowledge of objectness and class entropy from a proposal generator trained on the S dataset to optimize a multiple instance learning module on the T dataset.
DETR Does Not Need Multi-Scale or Locality Design
This paper presents an improved DETR detector that maintains a "plain" nature: using a single-scale feature map and global cross-attention calculations without specific locality constraints, in contrast to previous leading DETR-based detectors that reintroduce architectural inductive biases of multi-scale and locality into the decoder.
Ranked #12 on
Object Detection
on COCO test-dev
(using extra training data)
UNIKD: UNcertainty-filtered Incremental Knowledge Distillation for Neural Implicit Representation
In view of this, we explore the task of incremental learning for NIRs in this work.
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).
Robust and Fast Measure of Information via Low-rank Representation
The matrix-based R\'enyi's entropy allows us to directly quantify information measures from given data, without explicit estimation of the underlying probability distribution.
A Knowledge-based Learning Framework for Self-supervised Pre-training Towards Enhanced Recognition of Biomedical Microscopy Images
Self-supervised pre-training has become the priory choice to establish reliable neural networks for automated recognition of massive biomedical microscopy images, which are routinely annotation-free, without semantics, and without guarantee of quality.
DynamicLight: Two-Stage Dynamic Traffic Signal Timing
Reinforcement learning (RL) is gaining popularity as an effective approach for traffic signal control (TSC) and is increasingly applied in this domain.
SynGEC: Syntax-Enhanced Grammatical Error Correction with a Tailored GEC-Oriented Parser
Then, we obtain parse trees of the source incorrect sentences by projecting trees of the target correct sentences.
Predictive Edge Caching through Deep Mining of Sequential Patterns in User Content Retrievals
Edge caching plays an increasingly important role in boosting user content retrieval performance while reducing redundant network traffic.
Quasi-supervised Learning for Super-resolution PET
In this paper, we propose a quasi-supervised learning method, which is a new type of weakly-supervised learning methods, to recover HR PET images from LR counterparts by leveraging similarity between unpaired LR and HR image patches.
A Multi-Channel Next POI Recommendation Framework with Multi-Granularity Check-in Signals
Being equipped with three modules (i. e., global user behavior encoder, local multi-channel encoder, and region-aware weighting strategy), MCMG is capable of capturing both fine- and coarse-grained sequential regularities as well as exploring the dynamic impact of multi-channel by differentiating the region check-in patterns.
Segmentation of Weakly Visible Environmental Microorganism Images Using Pair-wise Deep Learning Features
The use of PDLFs enables the network to focus more on the foreground (EMs) by concatenating the pairwise deep learning features of each image to different blocks of the base model SegNet.
An Energy Activity Dataset for Smart Homes
The proposed energy activity dataset (EAD) has a high data type diversity in contrast to existing load monitoring datasets.
Artificial Neural Networks for Finger Vein Recognition: A Survey
To our best knowledge, this paper is the first comprehensive survey focusing on finger vein recognition based on artificial neural networks.
Simulation of snakes using vertical body bending to traverse terrain with large height variation
Remarkably, even when frictional drag is low (snake-terrain kinetic friction coefficient of 0. 20), the body must push against the wedge with a pressure 5 times that from body weight to generate sufficient forward propulsion to move forward.
Deep Learning for Finger Vein Recognition: A Brief Survey of Recent Trend
Finger vein image recognition technology plays an important role in biometric recognition and has been successfully applied in many fields.
Mining Error Templates for Grammatical Error Correction
We have accumulated 1, 119 error templates for Chinese GEC based on this method.
Adaptive Weighted Nonnegative Matrix Factorization for Robust Feature Representation
Nonnegative matrix factorization (NMF) has been widely used to dimensionality reduction in machine learning.
IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach
In addition, we conducted an ablation experiment and an interchangeability experiment to verify the ability and interchangeability of the three channels.
CVM-Cervix: A Hybrid Cervical Pap-Smear Image Classification Framework Using CNN, Visual Transformer and Multilayer Perceptron
Cervical cytopathology image classification is an important method to diagnose cervical cancer.
A Transistor Operations Model for Deep Learning Energy Consumption Scaling Law
At a general computational energy model level, there is both strong dependency to both the hardware architecture (e. g. generic processors with different configuration of inner components- CPU and GPU, programmable integrated circuits - FPGA), as well as different interacting energy consumption aspects (e. g., data movement, calculation, control).
A Comparative Study of Gastric Histopathology Sub-size Image Classification: from Linear Regression to Visual Transformer
Ensemble learning is a way to improve the accuracy of algorithms, and finding multiple learning models with complementarity types is the basis of ensemble learning.
Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer
The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagnosis.
Histopathological Image Classification
Image Classification
+3
Optimal Randomized Approximations for Matrix based Renyi's Entropy
The Matrix-based Renyi's entropy enables us to directly measure information quantities from given data without the costly probability density estimation of underlying distributions, thus has been widely adopted in numerous statistical learning and inference tasks.
MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction
This paper presents MuCGEC, a multi-reference multi-source evaluation dataset for Chinese Grammatical Error Correction (CGEC), consisting of 7, 063 sentences collected from three Chinese-as-a-Second-Language (CSL) learner sources.