Search Results for author:
Found 23 papers, 9 papers with code
A Clinical-oriented Multi-level Contrastive Learning Method for Disease Diagnosis in Low-quality Medical Images
Nevertheless, the effectiveness of CL is highly dependent on the quality of the positive and negative sample pairs.
Enhancing Texture Generation with High-Fidelity Using Advanced Texture Priors
Moreover, background noise frequently arises in high-resolution texture synthesis, limiting the practical application of these generation technologies. In this work, we propose a high-resolution and high-fidelity texture restoration technique that uses the rough texture as the initial input to enhance the consistency between the synthetic texture and the initial texture, thereby overcoming the issues of aliasing and blurring caused by the user's structure simplification operations.
Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation
Hence, both modules establish a learnable connection to solve the semantic gaps between the encoder and the decoder, which leads to a high-performance segmentation model for medical images.
Efficient LLM inference solution on Intel GPU
A customized Scaled-Dot-Product-Attention kernel is designed to match our fusion policy based on the segment KV cache solution.
MetaDreamer: Efficient Text-to-3D Creation With Disentangling Geometry and Texture
In the second stage, we concentrate on fine-tuning the geometry and optimizing the texture, thereby achieving a more refined 3D object.
Physics-constrained Attack against Convolution-based Human Motion Prediction
Specifically, we introduce a novel adaptable scheme that facilitates the attack to suit the scale of the target pose and two physical constraints to enhance the naturalness of the adversarial example.
Self-supervised Domain Adaptation for Breaking the Limits of Low-quality Fundus Image Quality Enhancement
Most of the existing image enhancement methods mainly focus on improving the image quality by leveraging the guidance of high-quality images, which is difficult to be collected in medical applications.
Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation
Combining the strengths of UMIX with CMT, UCMT can retain model disagreement and enhance the quality of pseudo labels for the co-training segmentation.
Learning Constrained Dynamic Correlations in Spatiotemporal Graphs for Motion Prediction
The key of DSTD-GC is constrained dynamic correlation modeling, which explicitly parameterizes the common static constraints as a spatial/temporal vanilla adjacency matrix shared by all frames/joints and dynamically extracts correspondence variances for each frame/joint with an adjustment modeling function.
Rich Action-semantic Consistent Knowledge for Early Action Prediction
Next, we build a bi-directional semantic graph for the teacher network and a single-directional semantic graph for the student network to model rich ASCK among partial videos.
Mobile Augmented Reality: User Interfaces, Frameworks, and Intelligence
Mobile Augmented Reality (MAR) integrates computer-generated virtual objects with physical environments for mobile devices.
Cluster Contrast for Unsupervised Person Re-Identification
Thus, our method can solve the problem of cluster inconsistency and be applicable to larger data sets.
Ranked #1 on
Unsupervised Person Re-Identification
on PersonX
A robust low data solution: dimension prediction of semiconductor nanorods
Deep neural network is further applied to develop regression model which demonstrated the well performed prediction on both the original and generated data with a similar distribution.
SDMTL: Semi-Decoupled Multi-grained Trajectory Learning for 3D human motion prediction
Predicting future human motion is critical for intelligent robots to interact with humans in the real world, and human motion has the nature of multi-granularity.
DeepSSM: Deep State-Space Model for 3D Human Motion Prediction
In contrast to prior works, we improve the multi-order modeling ability of human motion systems for more accurate predictions by building a deep state-space model (DeepSSM).
AIBench Scenario: Scenario-distilling AI Benchmarking
We formalize a real-world application scenario as a Directed Acyclic Graph-based model and propose the rules to distill it into a permutation of essential AI and non-AI tasks, which we call a scenario benchmark.
Interpretable Machine Learning Model for Early Prediction of Mortality in Elderly Patients with Multiple Organ Dysfunction Syndrome (MODS): a Multicenter Retrospective Study and Cross Validation
This study aims to develop an interpretable and generalizable model for early mortality prediction in elderly patients with MODS.
Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis
Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field.
TrajectoryNet: a new spatio-temporal feature learning network for human motion prediction
And the global temporal co-occurrence features represent the co-occurrence relationship that different subsequences in a complex motion sequence are appeared simultaneously, which can be obtained automatically with our proposed TrajectoryNet by reorganizing the temporal information as the depth dimension of the input tensor.
PISEP^2: Pseudo Image Sequence Evolution based 3D Pose Prediction
Specifically, a skeletal representation is proposed by transforming the joint coordinate sequence into an image sequence, which can model the different correlations of different joints.
Ranked #1 on
Pose Prediction
on Filtered NTU RGB+D
Two-block vs. Multi-block ADMM: An empirical evaluation of convergence
Alternating Direction Method of Multipliers (ADMM) has become a widely used optimization method for convex problems, particularly in the context of data mining in which large optimization problems are often encountered.
HIGHLY EFFICIENT 8-BIT LOW PRECISION INFERENCE OF CONVOLUTIONAL NEURAL NETWORKS
High throughput and low latency inference of deep neural networks are critical for the deployment of deep learning applications.
Highly Efficient 8-bit Low Precision Inference of Convolutional Neural Networks with IntelCaffe
High throughput and low latency inference of deep neural networks are critical for the deployment of deep learning applications.
