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Found 48 papers, 14 papers with code
Relieving Universal Label Noise for Unsupervised Visible-Infrared Person Re-Identification by Inferring from Neighbors
Unsupervised visible-infrared person re-identification (USL-VI-ReID) is of great research and practical significance yet remains challenging due to the absence of annotations.
Exploring structure diversity in atomic resolution microscopy with graph neural networks
The emergence of deep learning (DL) has provided great opportunities for the high-throughput analysis of atomic-resolution micrographs.
AutoFeedback: An LLM-based Framework for Efficient and Accurate API Request Generation
AutoFeedback achieves an accuracy of 100. 00\% on a real-world API dataset and reduces the cost of interaction with GPT-3. 5 Turbo by 23. 44\%, and GPT-4 Turbo by 11. 85\%.
Enhancing Decision-Making for LLM Agents via Step-Level Q-Value Models
Agents significantly enhance the capabilities of standalone Large Language Models (LLMs) by perceiving environments, making decisions, and executing actions.
Contrastive Learning-based Chaining-Cluster for Multilingual Voice-Face Association
This paper introduces our novel solution to the Face-Voice Association in Multilingual Environments (FAME) 2024 challenge, focusing on a contrastive learning-based chaining-cluster method to enhance face-voice association.
D-FaST: Cognitive Signal Decoding with Disentangled Frequency-Spatial-Temporal Attention
In this paper, we introduce a novel paradigm for CLP referred to as Disentangled Frequency-Spatial-Temporal Attention(D-FaST).
Online Self-Preferring Language Models
To overcome this limitation, we propose Online Self-Preferring (OSP) language models to learn from self-generated response pairs and self-judged preference strengths.
MER 2024: Semi-Supervised Learning, Noise Robustness, and Open-Vocabulary Multimodal Emotion Recognition
However, this process may lead to inaccurate annotations, such as ignoring non-majority or non-candidate labels.
Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey
This paper reviews the NTIRE 2024 RAW Image Super-Resolution Challenge, highlighting the proposed solutions and results.
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.
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
This paper reviews the NTIRE 2024 challenge on image super-resolution ($\times$4), highlighting the solutions proposed and the outcomes obtained.
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge
The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets.
Optimistic Model Rollouts for Pessimistic Offline Policy Optimization
ORPO generates Optimistic model Rollouts for Pessimistic offline policy Optimization.
Uncertainty-Penalized Reinforcement Learning from Human Feedback with Diverse Reward LoRA Ensembles
Reinforcement learning from human feedback (RLHF) emerges as a promising paradigm for aligning large language models (LLMs).
Cheap-fake Detection with LLM using Prompt Engineering
We enhance the baseline algorithm by incorporating a Large Language Model (LLM), GPT3. 5, as a feature extractor.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report
While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices.
Dynamic Memory-based Curiosity: A Bootstrap Approach for Exploration
The curiosity arouses if memorized information can not deal with the current state, and the information gap between dual learners can be formulated as the intrinsic reward for agents, and then such state information can be consolidated into the dynamic memory.
Self-Supervised Exploration via Temporal Inconsistency in Reinforcement Learning
Our method involves training a self-supervised prediction model, saving snapshots of the model parameters, and using nuclear norm to evaluate the temporal inconsistency between the predictions of different snapshots as intrinsic rewards.
Wound Segmentation with Dynamic Illumination Correction and Dual-view Semantic Fusion
Wound image segmentation is a critical component for the clinical diagnosis and in-time treatment of wounds.
Trusted Multi-Scale Classification Framework for Whole Slide Image
Leveraging the Vision Transformer as the backbone for multi branches, our framework can jointly classification modeling, estimating the uncertainty of each magnification of a microscope and integrate the evidence from different magnification.
Nuclear Norm Maximization Based Curiosity-Driven Learning
To handle the sparsity of the extrinsic rewards in reinforcement learning, researchers have proposed intrinsic reward which enables the agent to learn the skills that might come in handy for pursuing the rewards in the future, such as encouraging the agent to visit novel states.
Unsupervised Voice-Face Representation Learning by Cross-Modal Prototype Contrast
We present an approach to learn voice-face representations from the talking face videos, without any identity labels.
Multi-task Pre-training Language Model for Semantic Network Completion
To demonstrate the effectiveness of our method, we conduct extensive experiments on three widely-used datasets, WN18RR, FB15k-237, and UMLS.
Ranked #1 on
Link Prediction
on UMLS
HEROHE Challenge: assessing HER2 status in breast cancer without immunohistochemistry or in situ hybridization
Breast cancer is the most common malignancy in women, being responsible for more than half a million deaths every year.
Multimodal Feature Fusion for Video Advertisements Tagging Via Stacking Ensemble
Automated tagging of video advertisements has been a critical yet challenging problem, and it has drawn increasing interests in last years as its applications seem to be evident in many fields.
FINT: Field-aware INTeraction Neural Network For CTR Prediction
As a critical component for online advertising and marking, click-through rate (CTR) prediction has draw lots of attentions from both industry and academia field.
NTIRE 2021 Multi-modal Aerial View Object Classification Challenge
In this paper, we introduce the first Challenge on Multi-modal Aerial View Object Classification (MAVOC) in conjunction with the NTIRE 2021 workshop at CVPR.
KnowSR: Knowledge Sharing among Homogeneous Agents in Multi-agent Reinforcement Learning
In this paper, we present an adaptation method of the majority of multi-agent reinforcement learning (MARL) algorithms called KnowSR which takes advantage of the differences in learning between agents.
NTIRE 2021 Challenge on Perceptual Image Quality Assessment
This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021.
KnowRU: Knowledge Reusing via Knowledge Distillation in Multi-agent Reinforcement Learning
In this paper, we propose a method, named "KnowRU" for knowledge reusing which can be easily deployed in the majority of the multi-agent reinforcement learning algorithms without complicated hand-coded design.
Convolutional Neural Network-Based Age Estimation Using B-Mode Ultrasound Tongue Image
Ultrasound tongue imaging is widely used for speech production research, and it has attracted increasing attention as its potential applications seem to be evident in many different fields, such as the visual biofeedback tool for second language acquisition and silent speech interface.
AIM 2020: Scene Relighting and Illumination Estimation Challenge
The first track considered one-to-one relighting; the objective was to relight an input photo of a scene with a different color temperature and illuminant orientation (i. e., light source position).
Quantification of Transducer Misalignment in Ultrasound Tongue Imaging
Ultrasound is an affordable and non-invasive imaging modality with relatively high temporal and spatial resolution to study the dynamic behavior of tongue during speech production.
Audio and Speech Processing Sound
Audio Tagging by Cross Filtering Noisy Labels
Yet, it is labor-intensive to accurately annotate large amount of audio data, and the dataset may contain noisy labels in the practical settings.
Attention-based Fault-tolerant Approach for Multi-agent Reinforcement Learning Systems
The aim of multi-agent reinforcement learning systems is to provide interacting agents with the ability to collaboratively learn and adapt to the behavior of other agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Predicting tongue motion in unlabeled ultrasound videos using convolutional LSTM neural network
A challenge in speech production research is to predict future tongue movements based on a short period of past tongue movements.
Learning data augmentation policies using augmented random search
AutoAugment searches for the augmentation polices in the discrete search space, which may lead to a sub-optimal solution.
General audio tagging with ensembling convolutional neural network and statistical features
Audio tagging is challenging due to the limited size of data and noisy labels.
Collaborative Deep Learning Across Multiple Data Centers
Valuable training data is often owned by independent organizations and located in multiple data centers.
Sample Dropout for Audio Scene Classification Using Multi-Scale Dense Connected Convolutional Neural Network
In this paper, we explore the use of multi-scale Dense connected convolutional neural network (DenseNet) for the classification task, with the goal to improve the classification performance as multi-scale features can be extracted from the time-frequency representation of the audio signal.
Multi-Scale DenseNet-Based Electricity Theft Detection
We compare the proposed approaches with the classical algorithms, and the experimental results demonstrate that the multiscale DenseNet approach can significantly improve the accuracy of the detection.
Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network
Audio scene classification, the problem of predicting class labels of audio scenes, has drawn lots of attention during the last several years.
Full-reference image quality assessment-based B-mode ultrasound image similarity measure
During the last decades, the number of new full-reference image quality assessment algorithms has been increasing drastically.
Tongue contour extraction from ultrasound images based on deep neural network
In this paper, we use an automatic labelling algorithm instead of time-consuming hand-labelling during the training process, and estimate the performances of both automatic labelling and contour extraction as compared to hand-labelling.
Development of a 3D tongue motion visualization platform based on ultrasound image sequences
This article describes the development of a platform designed to visualize the 3D motion of the tongue using ultrasound image sequences.
Contour-based 3d tongue motion visualization using ultrasound image sequences
This article describes a contour-based 3D tongue deformation visualization framework using B-mode ultrasound image sequences.
