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Found 31 papers, 14 papers with code
FFA Sora, video generation as fundus fluorescein angiography simulator
Fundus fluorescein angiography (FFA) is critical for diagnosing retinal vascular diseases, but beginners often struggle with image interpretation.
BEOL Electro-Biological Interface for 1024-Channel TFT Neurostimulator with Cultured DRG Neurons
The demand for high-quality neurostimulation, driven by the development of brain-computer interfaces, has outpaced the capabilities of passive microelectrode-arrays, which are limited by channel-count and biocompatibility.
EyeDiff: text-to-image diffusion model improves rare eye disease diagnosis
EyeDiff effectively tackles the issue of data imbalance and insufficiency typically encountered in rare diseases and addresses the challenges of collecting large-scale annotated images, offering a transformative solution to enhance the development of expert-level diseases diagnosis models in ophthalmic field.
Subgraph Aggregation for Out-of-Distribution Generalization on Graphs
To address these challenges, we propose a novel framework, SubGraph Aggregation (SuGAr), designed to learn a diverse set of subgraphs that are crucial for OOD generalization on graphs.
Molecular Property Prediction
Out-of-Distribution Generalization
+1
CTA-Net: A CNN-Transformer Aggregation Network for Improving Multi-Scale Feature Extraction
Convolutional neural networks (CNNs) and vision transformers (ViTs) have become essential in computer vision for local and global feature extraction.
Edge Unlearning is Not "on Edge"! An Adaptive Exact Unlearning System on Resource-Constrained Devices
Another approach, exact unlearning, tackles this issue by discarding the data and retraining the model from scratch, but at the cost of considerable computational and memory resources.
Fine-grained Classification of Port Wine Stains Using Optical Coherence Tomography Angiography
The five PWS types present significant differences across all metrics compared to the conventional subtypes.
ADRS-CNet: An adaptive dimensionality reduction selection and classification network for DNA storage clustering algorithms
Currently, clustering and comparison of sequenced sequences are employed to recover the original sequence information as much as possible.
YOLO-pdd: A Novel Multi-scale PCB Defect Detection Method Using Deep Representations with Sequential Images
This work advances computer vision inspection for PCB defect detection, providing a reliable solution for high-precision, robust, real-time, and domain-adaptive defect detection in the PCB manufacturing industry.
Diffusion-Aided Joint Source Channel Coding For High Realism Wireless Image Transmission
In the following diffusion step, DiffJSCC uses the derived multimodal features, together with channel state information such as the signal-to-noise ratio (SNR), as conditions to guide the denoising diffusion process, which converts the initial random noise to the final reconstruction.
VisionCLIP: An Med-AIGC based Ethical Language-Image Foundation Model for Generalizable Retina Image Analysis
Generalist foundation model has ushered in newfound capabilities in medical domain.
Category-Agnostic Pose Estimation for Point Clouds
The goal of object pose estimation is to visually determine the pose of a specific object in the RGB-D input.
Quantum-Inspired Machine Learning for Molecular Docking
Traditional docking by searching for possible binding sites and conformations is computationally complex and results poorly under blind docking.
VCISR: Blind Single Image Super-Resolution with Video Compression Synthetic Data
In this work, we for the first time, present a video compression-based degradation model to synthesize low-resolution image data in the blind SISR task.
TrTr: A Versatile Pre-Trained Large Traffic Model based on Transformer for Capturing Trajectory Diversity in Vehicle Population
In this study, we apply the Transformer architecture to traffic tasks, aiming to learn the diversity of trajectories within vehicle populations.
MMVC: Learned Multi-Mode Video Compression with Block-based Prediction Mode Selection and Density-Adaptive Entropy Coding
In this paper, we propose multi-mode video compression (MMVC), a block wise mode ensemble deep video compression framework that selects the optimal mode for feature domain prediction adapting to different motion patterns.
Regression modeling on DNA encoded libraries
DNA encoded libraries (DELs) are pooled, combinatorial compound collections where each member is tagged with its own unique DNA barcode.
Unified Signal Compression Using a GAN with Iterative Latent Representation Optimization
We propose a unified signal compression framework that uses a generative adversarial network (GAN) to compress heterogeneous signals.
Capture Uncertainties in Deep Neural Networks for Safe Operation of Autonomous Driving Vehicles
Uncertainties in Deep Neural Network (DNN)-based perception and vehicle's motion pose challenges to the development of safe autonomous driving vehicles.
Deep Learning in Latent Space for Video Prediction and Compression
The proposed method first learns the efficient lower-dimensional latent space representation of each video frame and then performs inter-frame prediction in that latent domain.
Uncertainty-aware Joint Salient Object and Camouflaged Object Detection
Visual salient object detection (SOD) aims at finding the salient object(s) that attract human attention, while camouflaged object detection (COD) on the contrary intends to discover the camouflaged object(s) that hidden in the surrounding.
Simultaneously Localize, Segment and Rank the Camouflaged Objects
With the above understanding about camouflaged objects, we present the first ranking based COD network (Rank-Net) to simultaneously localize, segment and rank camouflaged objects.
Ranked #7 on
Camouflaged Object Segmentation
on PCOD_1200
A First Look into DeFi Oracles
In this paper, we present the first study of DeFi oracles deployed in practice.
Cryptography and Security
Open Graph Benchmark: Datasets for Machine Learning on Graphs
We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research.
Ranked #1 on
Link Property Prediction
on ogbl-citation2
Weakly-Supervised Salient Object Detection via Scribble Annotations
In this paper, we propose a weakly-supervised salient object detection model to learn saliency from such annotations.
Probabilistic K-means Clustering via Nonlinear Programming
In theory, we can solve the model by active gradient projection, while inefficiently.
Unified Signal Compression Using Generative Adversarial Networks
We propose a unified compression framework that uses generative adversarial networks (GAN) to compress image and speech signals.
Strategies for Pre-training Graph Neural Networks
Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training.
Ranked #4 on
Molecular Property Prediction
on QM9
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research.
Retrosynthetic reaction prediction using neural sequence-to-sequence models
We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem.
Ranked #33 on
Single-step retrosynthesis
on USPTO-50k
