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Found 303 papers, 83 papers with code
软件标识符的自然语言规范性研究(Research on the Natural Language Normalness of Software Identifiers)
“软件源代码的理解则是软件协同开发与维护的核心, 而源代码中占半数以上的标识符的理解则在软件理解中起到重要作用, 传统软件工程主要研究通过命名规范限制标识符的命名过程以构造更易理解和交流的标识符。本文则在梳理分析常见编程语言命名规范的基础上, 提出一种全新的标识符可理解性评价标准。具体而言, 本文首先总结梳理了常见主流编程语言中的命名规范并类比自然语言语素概念本文提出基于软件语素的标识符构成过程, 即标识符的构成可被视为软件语素的生成、排列和连接过程。在此基础上, 本文提出一种结合自然语料库的软件标识符规范性评价方法, 用来衡量软件标识符是否易于理解。最后, 本文通过源代码理解数据集和乇乩乴乨乵乢平台中开源项目对规范性指标进行了验证性实验, 结果表明本文提出的规范性分数能够很好衡量软件项目的可理解性。”
Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits
We first present a policy evaluation procedure in the ambiguous environment and also give a heuristic algorithm to solve the distributionally robust policy learning problems efficiently.
EndoChat: Grounded Multimodal Large Language Model for Endoscopic Surgery
Recently, Multimodal Large Language Models (MLLMs) have demonstrated their immense potential in computer-aided diagnosis and decision-making.
UltraFusion: Ultra High Dynamic Imaging using Exposure Fusion
In this work, we propose UltraFusion, the first exposure fusion technique that can merge input with 9 stops differences.
GeoPix: Multi-Modal Large Language Model for Pixel-level Image Understanding in Remote Sensing
In addition, to address the absence of large-scale datasets for training pixel-level RS MLLMs, we construct the GeoPixInstruct dataset, comprising 65, 463 images and 140, 412 instances, with each instance annotated with text descriptions, bounding boxes, and masks.
X-LeBench: A Benchmark for Extremely Long Egocentric Video Understanding
Leveraging the advanced text processing capabilities of large language models (LLMs), X-LeBench develops a life-logging simulation pipeline that produces realistic, coherent daily plans aligned with real-world video data.
FlipedRAG: Black-Box Opinion Manipulation Attacks to Retrieval-Augmented Generation of Large Language Models
By leveraging instruction engineering, we obtain partial retrieval model outputs from black-box RAG system, facilitating the training of surrogate models to enhance the effectiveness of opinion manipulation attack.
Artificial Intelligence in Creative Industries: Advances Prior to 2025
This paper explores the significant technological shifts since our previous review in 2022, highlighting how these developments have expanded creative opportunities and efficiency.
SGTC: Semantic-Guided Triplet Co-training for Sparsely Annotated Semi-Supervised Medical Image Segmentation
To address these issues, we propose a novel Semantic-Guided Triplet Co-training (SGTC) framework, which achieves high-end medical image segmentation by only annotating three orthogonal slices of a few volumetric samples, significantly alleviating the burden of radiologists.
Efficient Quantization-Aware Training on Segment Anything Model in Medical Images and Its Deployment
Medical image segmentation is a critical component of clinical practice, and the state-of-the-art MedSAM model has significantly advanced this field.
Efficient Gravitational Wave Parameter Estimation via Knowledge Distillation: A ResNet1D-IAF Approach
With the rapid development of gravitational wave astronomy, the increasing number of detected events necessitates efficient methods for parameter estimation and model updates.
Evaluation Agent: Efficient and Promptable Evaluation Framework for Visual Generative Models
Moreover, existing evaluation methods rely on rigid pipelines that overlook specific user needs and provide numerical results without clear explanations.
Adaptive Epsilon Adversarial Training for Robust Gravitational Wave Parameter Estimation Using Normalizing Flows
Adversarial training with Normalizing Flow (NF) models is an emerging research area aimed at improving model robustness through adversarial samples.
Diff5T: Benchmarking Human Brain Diffusion MRI with an Extensive 5.0 Tesla K-Space and Spatial Dataset
Diffusion magnetic resonance imaging (dMRI) provides critical insights into the microstructural and connectional organization of the human brain.
HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolution
Recent advances in implicit neural representations (INRs) have shown significant promise in modeling visual signals for various low-vision tasks including image super-resolution (ISR).
HOT3D: Hand and Object Tracking in 3D from Egocentric Multi-View Videos
We introduce HOT3D, a publicly available dataset for egocentric hand and object tracking in 3D.
DiM-Gestor: Co-Speech Gesture Generation with Adaptive Layer Normalization Mamba-2
DiM-Gestor features a dual-component framework: (1) a fuzzy feature extractor and (2) a speech-to-gesture mapping module, both built on the Mamba-2.
RTSR: A Real-Time Super-Resolution Model for AV1 Compressed Content
To address this issue, this paper proposes a low-complexity SR method, RTSR, designed to enhance the visual quality of compressed video content, focusing on resolution up-scaling from a) 360p to 1080p and from b) 540p to 4K.
VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models
Video generation has witnessed significant advancements, yet evaluating these models remains a challenge.
Advancing Large Language Models for Spatiotemporal and Semantic Association Mining of Similar Environmental Events
Retrieval and recommendation are two essential tasks in modern search tools.
AsynEIO: Asynchronous Monocular Event-Inertial Odometry Using Gaussian Process Regression
Event cameras, when combined with inertial sensors, show significant potential for motion estimation in challenging scenarios, such as high-speed maneuvers and low-light environments.
BVI-CR: A Multi-View Human Dataset for Volumetric Video Compression
However, the development and validation of efficient 3D data compression methods are constrained by the lack of comprehensive and high-quality volumetric video datasets, which typically require much more effort to acquire and consume increased resources compared to 2D image and video databases.
MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI
There is a pressing need to harmonize the preprocessing of DW-MRI datasets to ensure the derivation of robust quantitative diffusion metrics across acquisitions.
A Novel Deep Learning Tractography Fiber Clustering Framework for Functionally Consistent White Matter Parcellation Using Multimodal Diffusion MRI and Functional MRI
Tractography fiber clustering using diffusion MRI (dMRI) is a crucial strategy for white matter (WM) parcellation.
Human-inspired Perspectives: A Survey on AI Long-term Memory
With the rapid advancement of AI systems, their abilities to store, retrieve, and utilize information over the long term - referred to as long-term memory - have become increasingly significant.
AdaptiveISP: Learning an Adaptive Image Signal Processor for Object Detection
Based on this, AdaptiveISP utilizes deep reinforcement learning to automatically generate an optimal ISP pipeline and the associated ISP parameters to maximize the detection performance.
TractShapeNet: Efficient Multi-Shape Learning with 3D Tractography Point Clouds
In this work, we investigate the possibility of utilizing a deep learning model to compute shape measures of the brain's white matter connections.
The shape of the brain's connections is predictive of cognitive performance: an explainable machine learning study
Our results demonstrate that shape measures are predictive of individual cognitive performance.
Resolution Enhancement of Under-sampled Photoacoustic Microscopy Images using Implicit Neural Representations
Acoustic-Resolution Photoacoustic Microscopy (AR-PAM) is promising for subcutaneous vascular imaging, but its spatial resolution is constrained by the Point Spread Function (PSF).
Few Exemplar-Based General Medical Image Segmentation via Domain-Aware Selective Adaptation
Medical image segmentation poses challenges due to domain gaps, data modality variations, and dependency on domain knowledge or experts, especially for low- and middle-income countries (LMICs).
UW-GS: Distractor-Aware 3D Gaussian Splatting for Enhanced Underwater Scene Reconstruction
3D Gaussian splatting (3DGS) offers the capability to achieve real-time high quality 3D scene rendering.
DualDn: Dual-domain Denoising via Differentiable ISP
The raw domain denoising adapts to sensor-specific noise as well as spatially varying noise levels, while the sRGB domain denoising adapts to ISP variations and removes residual noise amplified by the ISP.
Ranked #1 on
Image Denoising
on DND
Emu3: Next-Token Prediction is All You Need
While next-token prediction is considered a promising path towards artificial general intelligence, it has struggled to excel in multimodal tasks, which are still dominated by diffusion models (e. g., Stable Diffusion) and compositional approaches (e. g., CLIP combined with LLMs).
Ranked #132 on
Visual Question Answering
on MM-Vet
Cloud Adversarial Example Generation for Remote Sensing Image Classification
Common Perlin noise based cloud generation is a random, non-optimizable process, which cannot be directly used to attack the target models.
NVRC: Neural Video Representation Compression
In this paper, rather than focusing on representation architectures as in many existing works, we propose a novel INR-based video compression framework, Neural Video Representation Compression (NVRC), targeting compression of the representation.
DDEvENet: Evidence-based Ensemble Learning for Uncertainty-aware Brain Parcellation Using Diffusion MRI
To do so, we design an evidence-based ensemble learning framework for uncertainty-aware parcellation to leverage the multiple dMRI parameters derived from diffusion MRI.
Renormalized Connection for Scale-preferred Object Detection in Satellite Imagery
In a series of FPN experiments on the scale-preferred tasks, we found that the ``divide-and-conquer'' idea of FPN severely hampers the detector's learning in the right direction due to the large number of large-scale negative samples and interference from background noise.
Transmit Beamforming Design for ISAC with Stacked Intelligent Metasurfaces
This paper proposes a transmit beamforming strategy for the integrated sensing and communication (ISAC) systems enabled by the novel stacked intelligent metasurface (SIM) architecture, where the base station (BS) simultaneously performs downlink communication and radar target detection via different beams.
Affordance-based Robot Manipulation with Flow Matching
We present a framework for assistive robot manipulation, which focuses on two fundamental challenges: first, efficiently adapting large-scale models to downstream scene affordance understanding tasks, especially in daily living scenarios where gathering multi-task data involving humans requires strenuous effort; second, effectively learning robot trajectories by grounding the visual affordance model.
PNVC: Towards Practical INR-based Video Compression
Neural video compression has recently demonstrated significant potential to compete with conventional video codecs in terms of rate-quality performance.
When Diffusion MRI Meets Diffusion Model: A Novel Deep Generative Model for Diffusion MRI Generation
This achievement underscores a substantial progression in enhancing dMRI quality, highlighting the potential of our novel generative approach to revolutionize dMRI imaging standards.
BVI-UGC: A Video Quality Database for User-Generated Content Transcoding
Based on this collected subjective data, we benchmarked the performance of 10 full-reference and 11 no-reference quality metrics.
Mesh deformation-based single-view 3D reconstruction of thin eyeglasses frames with differentiable rendering
Specifically, based on the construction of a synthetic eyeglasses frame dataset, we first define a class-specific eyeglasses frame template with pre-defined keypoints.
Benchmarking Conventional and Learned Video Codecs with a Low-Delay Configuration
Recent advances in video compression have seen significant coding performance improvements with the development of new standards and learning-based video codecs.
BVI-AOM: A New Training Dataset for Deep Video Compression Optimization
Deep learning is now playing an important role in enhancing the performance of conventional hybrid video codecs.
DiM-Gesture: Co-Speech Gesture Generation with Adaptive Layer Normalization Mamba-2 framework
Speech-driven gesture generation is an emerging domain within virtual human creation, where current methods predominantly utilize Transformer-based architectures that necessitate extensive memory and are characterized by slow inference speeds.
Diffusion Feedback Helps CLIP See Better
We demonstrate that DIVA improves CLIP's performance on the challenging MMVP-VLM benchmark which assesses fine-grained visual abilities to a large extent (e. g., 3-7%), and enhances the performance of MLLMs and vision models on multimodal understanding and segmentation tasks.
White Matter Geometry-Guided Score-Based Diffusion Model for Tissue Microstructure Imputation in Tractography Imaging
In this work, we propose a novel deep-learning model to impute tissue microstructure: the White Matter Geometry-guided Diffusion (WMG-Diff) model.
Deep multimodal saliency parcellation of cerebellar pathways: linking microstructure and individual function through explainable multitask learning
We refer to our method as Deep Multimodal Saliency Parcellation (DeepMSP), as it computes the saliency of structural measures for predicting cognitive and motor functional performance, with these saliencies being applied to the task of parcellation.
Black-Box Opinion Manipulation Attacks to Retrieval-Augmented Generation of Large Language Models
Retrieval-Augmented Generation (RAG) is applied to solve hallucination problems and real-time constraints of large language models, but it also induces vulnerabilities against retrieval corruption attacks.
LTRL: Boosting Long-tail Recognition via Reflective Learning
In real-world scenarios, where knowledge distributions exhibit long-tail.
TractGraphFormer: Anatomically Informed Hybrid Graph CNN-Transformer Network for Classification from Diffusion MRI Tractography
The proposed approach highlights the potential of integrating local anatomical information and global feature dependencies to improve prediction performance in machine learning with diffusion MRI tractography.
DenseFusion-1M: Merging Vision Experts for Comprehensive Multimodal Perception
To facilitate the cutting-edge research of MLLMs on comprehensive vision perception, we thereby propose Perceptual Fusion, using a low-budget but highly effective caption engine for complete and accurate image descriptions.
Ranked #123 on
Visual Question Answering
on MM-Vet
Graph-Guided Test-Time Adaptation for Glaucoma Diagnosis using Fundus Photography
Glaucoma is a leading cause of irreversible blindness worldwide.
DaBiT: Depth and Blur informed Transformer for Joint Refocusing and Super-Resolution
In many real-world scenarios, recorded videos suffer from accidental focus blur, and while video deblurring methods exist, most specifically target motion blur.
GraphKAN: Enhancing Feature Extraction with Graph Kolmogorov Arnold Networks
Massive number of applications involve data with underlying relationships embedded in non-Euclidean space.
Introducing HOT3D: An Egocentric Dataset for 3D Hand and Object Tracking
The dataset offers over 833 minutes (more than 3. 7M images) of multi-view RGB/monochrome image streams showing 19 subjects interacting with 33 diverse rigid objects, multi-modal signals such as eye gaze or scene point clouds, as well as comprehensive ground truth annotations including 3D poses of objects, hands, and cameras, and 3D models of hands and objects.
MVAD: A Multiple Visual Artifact Detector for Video Streaming
In this paper, we propose a Multiple Visual Artifact Detector, MVAD, for video streaming which, for the first time, is able to detect multiple artifacts using a single framework that is not reliant on video quality assessment models.
FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP Optimization
This study contributes to PFL by establishing a solid theoretical foundation for the proposed method and offering a robust, ready-to-use framework that effectively addresses the challenges posed by non-IID data in FL.
Survey on Visual Signal Coding and Processing with Generative Models: Technologies, Standards and Optimization
This paper provides a survey of the latest developments in visual signal coding and processing with generative models.
EntropyStop: Unsupervised Deep Outlier Detection with Loss Entropy
Most deep UOD models are trained exclusively on clean datasets to learn the distribution of the normal data, which requires huge manual efforts to clean the real-world data if possible.
DEMO: A Statistical Perspective for Efficient Image-Text Matching
Image-text matching has been a long-standing problem, which seeks to connect vision and language through semantic understanding.
RMT-BVQA: Recurrent Memory Transformer-based Blind Video Quality Assessment for Enhanced Video Content
The proposed method, RMT-BVQA, has been evaluated on the VDPVE (VQA Dataset for Perceptual Video Enhancement) database through a five-fold cross validation.
Capabilities of Gemini Models in Medicine
We evaluate Med-Gemini on 14 medical benchmarks, establishing new state-of-the-art (SoTA) performance on 10 of them, and surpass the GPT-4 model family on every benchmark where a direct comparison is viable, often by a wide margin.
Ranked #1 on
Question Answering
on MedQA
(using extra training data)
Minimal Evidence Group Identification for Claim Verification
Claim verification in real-world settings (e. g. against a large collection of candidate evidences retrieved from the web) typically requires identifying and aggregating a complete set of evidence pieces that collectively provide full support to the claim.
LEAF: Unveiling Two Sides of the Same Coin in Semi-supervised Facial Expression Recognition
LEAF introduces a hierarchical expression-aware aggregation strategy that operates at three levels: semantic, instance, and category.
Facial Expression Recognition
Facial Expression Recognition (FER)
Disturbance Rejection-Guarded Learning for Vibration Suppression of Two-Inertia Systems
On the other hand, machine learning benefits from an additional assurance layer provided by the ESO, as any imperfections in the machine learning model can be compensated for by the ESO.
MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution
Knowledge distillation (KD) has emerged as a promising technique in deep learning, typically employed to enhance a compact student network through learning from their high-performance but more complex teacher variant.
A diffusion MRI tractography atlas for concurrent white matter mapping across Eastern and Western populations
However, a comprehensive investigation into WM fiber tracts between Eastern and Western populations is challenged due to the lack of a cross-population WM atlas and the large site-specific variability of dMRI data.
Cross-domain Fiber Cluster Shape Analysis for Language Performance Cognitive Score Prediction
Overall, our results indicate that the shape of the brain's connections is predictive of human language function.
Ensemble Adversarial Defense via Integration of Multiple Dispersed Low Curvature Models
In this work, we aim to enhance ensemble diversity by reducing attack transferability.
Unifying Lane-Level Traffic Prediction from a Graph Structural Perspective: Benchmark and Baseline
Traffic prediction has long been a focal and pivotal area in research, witnessing both significant strides from city-level to road-level predictions in recent years.
Speech-driven Personalized Gesture Synthetics: Harnessing Automatic Fuzzy Feature Inference
Finally, we employ the diffusion model to train and infer various gestures.
Gradient-Aware Logit Adjustment Loss for Long-tailed Classifier
Additionally, We find that most of the solutions to long-tailed problems are still biased towards head classes in the end, and we propose a simple and post hoc prediction re-balancing strategy to further mitigate the basis toward head class.
LTGC: Long-tail Recognition via Leveraging LLMs-driven Generated Content
Long-tail recognition is challenging because it requires the model to learn good representations from tail categories and address imbalances across all categories.
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Scaling up contrastive language-image pretraining (CLIP) is critical for empowering both vision and multimodal models.
Ranked #1 on
Zero-Shot Transfer Image Classification
on SUN
Image Classification
Zero-Shot Transfer Image Classification
Immersive Video Compression using Implicit Neural Representations
In this paper we, for the first time, extend their application to immersive (multi-view) videos, by proposing MV-HiNeRV, a new INR-based immersive video codec.
3D Landmark Detection on Human Point Clouds: A Benchmark and A Dual Cascade Point Transformer Framework
Comparative evaluations with popular point-based methods on HPoint103 and the public dataset DHP19 demonstrate the dramatic outperformance of our D-CPT.
MIMIC: Mask Image Pre-training with Mix Contrastive Fine-tuning for Facial Expression Recognition
In addition, when compared with the domain disparity existing between face datasets and FER datasets, the divergence between general datasets and FER datasets is more pronounced.
Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security
Next, we discuss several key challenges to achieve intelligent, efficient and secure Personal LLM Agents, followed by a comprehensive survey of representative solutions to address these challenges.
A Deep Network for Explainable Prediction of Non-Imaging Phenotypes using Anatomical Multi-View Data
We present an explainable multi-view network (EMV-Net) that can use different anatomical views to improve prediction performance.
Fine-grained Prototypical Voting with Heterogeneous Mixup for Semi-supervised 2D-3D Cross-modal Retrieval
In this paper we propose a semi-supervised approach named Fine-grained Prototypcical Voting with Heterogeneous Mixup (FIVE) which maps both 2D and 3D data into a common embedding space for cross-modal retrieval.
Compressing Deep Image Super-resolution Models
Deep learning techniques have been applied in the context of image super-resolution (SR), achieving remarkable advances in terms of reconstruction performance.
Emage: Non-Autoregressive Text-to-Image Generation
Compared with autoregressive baselines that needs to run one thousand times, our model only runs 16 times to generate images of competitive quality with an order of magnitude lower inference latency.
GreenScan: Towards large-scale terrestrial monitoring the health of urban trees using mobile sensing
Healthy urban greenery is a fundamental asset to mitigate climate change phenomena such as extreme heat and air pollution.
Generative Multimodal Models are In-Context Learners
The human ability to easily solve multimodal tasks in context (i. e., with only a few demonstrations or simple instructions), is what current multimodal systems have largely struggled to imitate.
Ranked #3 on
Personalized Image Generation
on DreamBooth
Atlantis: Enabling Underwater Depth Estimation with Stable Diffusion
Nonetheless, the performance of these methods is often constrained by the domain gap and looser constraints.
Full-reference Video Quality Assessment for User Generated Content Transcoding
In this work, we observe that existing full-/no-reference quality metrics fail to accurately predict the perceptual quality difference between transcoded UGC content and the corresponding unpristine references.
Device Scheduling for Relay-assisted Over-the-Air Aggregation in Federated Learning
we formulate a joint device scheduling, and power allocation problem to maximize the number of scheduled devices.
BVI-Artefact: An Artefact Detection Benchmark Dataset for Streamed Videos
Professionally generated content (PGC) streamed online can contain visual artefacts that degrade the quality of user experience.
RankDVQA-mini: Knowledge Distillation-Driven Deep Video Quality Assessment
Deep learning-based video quality assessment (deep VQA) has demonstrated significant potential in surpassing conventional metrics, with promising improvements in terms of correlation with human perception.
A Simple Framework to Enhance the Adversarial Robustness of Deep Learning-based Intrusion Detection System
In this paper, we propose a novel IDS architecture that can enhance the robustness of IDS against adversarial attacks by combining conventional machine learning (ML) models and Deep Learning models.
Accelerating Learnt Video Codecs with Gradient Decay and Layer-wise Distillation
In recent years, end-to-end learnt video codecs have demonstrated their potential to compete with conventional coding algorithms in term of compression efficiency.
How does spatial structure affect psychological restoration? A method based on Graph Neural Networks and Street View Imagery
In this work, a spatial-dependent graph neural networks (GNNs) approach is proposed to reveal the relation between spatial structure and restoration quality on an urban scale.
VBench: Comprehensive Benchmark Suite for Video Generative Models
We will open-source VBench, including all prompts, evaluation methods, generated videos, and human preference annotations, and also include more video generation models in VBench to drive forward the field of video generation.
A Novel Deep Clustering Framework for Fine-Scale Parcellation of Amygdala Using dMRI Tractography
The amygdala plays a vital role in emotional processing and exhibits structural diversity that necessitates fine-scale parcellation for a comprehensive understanding of its anatomico-functional correlations.
Cross-Domain Dual-Functional OFDM Waveform Design for Accurate Sensing/Positioning
For the communication-centric design, to maximize the achievable data rate, a fraction of REs are optimally allocated for communications according to prior knowledge of the communication channel.
CapsFusion: Rethinking Image-Text Data at Scale
To provide higher-quality and more scalable multimodal pretraining data, we propose CapsFusion, an advanced framework that leverages large language models to consolidate and refine information from both web-based image-text pairs and synthetic captions.
Multi-task deep learning for large-scale building detail extraction from high-resolution satellite imagery
Understanding urban dynamics and promoting sustainable development requires comprehensive insights about buildings.
Recent Methodological Advances in Federated Learning for Healthcare
We give detailed recommendations to help improve the quality of the methodology development for federated learning in healthcare.
Neural-Singular-Hessian: Implicit Neural Representation of Unoriented Point Clouds by Enforcing Singular Hessian
Our approach enforces the Hessian of the neural implicit function to have a zero determinant for points near the surface.
Planning with Logical Graph-based Language Model for Instruction Generation
Despite the superior performance of large language models to generate natural language texts, it is hard to generate texts with correct logic according to a given task, due to the difficulties for neural models to capture implied rules from free-form texts.
Project Aria: A New Tool for Egocentric Multi-Modal AI Research
Egocentric, multi-modal data as available on future augmented reality (AR) devices provides unique challenges and opportunities for machine perception.
Ear-Keeper: Real-time Diagnosis of Ear Lesions Utilizing Ultralight-Ultrafast ConvNet and Large-scale Ear Endoscopic Dataset
Deep learning-based ear disease diagnosis technology has proven effective and affordable.
MDCS: More Diverse Experts with Consistency Self-distillation for Long-tailed Recognition
In the analysis and ablation study, we demonstrate that our method compared with previous work can effectively increase the diversity of experts, significantly reduce the variance of the model, and improve recognition accuracy.
Ranked #5 on
Long-tail Learning
on CIFAR-10-LT (ρ=50)
Audio is all in one: speech-driven gesture synthetics using WavLM pre-trained model
Firstly, considering that speech audio not only contains acoustic and semantic features but also conveys personality traits, emotions, and more subtle information related to accompanying gestures, we pioneer the adaptation of WavLM, a large-scale pre-trained model, to extract low-level and high-level audio information.
Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge
The best-performing algorithms successfully generalized to holdout external validation sets, achieving a median DSC of 89. 5\%, 90. 9\%, and 88. 3\% on North American, European, and Asian cohorts, respectively.
Deep neural networks from the perspective of ergodic theory
The design of deep neural networks remains somewhat of an art rather than precise science.
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs.
TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representation
TractCloud achieves efficient and consistent whole-brain white matter parcellation across the lifespan (from neonates to elderly subjects, including brain tumor patients) without the need for registration.
Data-Driven Optimal Control of Tethered Space Robot Deployment with Learning Based Koopman Operator
To avoid complex constraints of the traditional nonlinear method for tethered space robot (TSR) deployment, this paper proposes a data-driven optimal control framework with an improved deep learning based Koopman operator that could be applied to complex environments.
ATWM: Defense against adversarial malware based on adversarial training
In order to defend against malware attacks, researchers have proposed many Windows malware detection models based on deep learning.
Emu: Generative Pretraining in Multimodality
We present Emu, a Transformer-based multimodal foundation model, which can seamlessly generate images and texts in multimodal context.
Ranked #1 on
Visual Question Answering
on VizWiz
TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance
We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project.
Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference
Alzheimer's disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition.