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Found 37 papers, 17 papers with code
Cosmos World Foundation Model Platform for Physical AI
We position a world foundation model as a general-purpose world model that can be fine-tuned into customized world models for downstream applications.
Decoding the Flow: CauseMotion for Emotional Causality Analysis in Long-form Conversations
To address the limitations of large-scale language models (e. g., GPT-4) in capturing intricate emotional causality within extended dialogues, we propose CauseMotion, a long-sequence emotional causal reasoning framework grounded in Retrieval-Augmented Generation (RAG) and multimodal fusion.
Beyond Words: AuralLLM and SignMST-C for Precise Sign Language Production and Bidirectional Accessibility
To improve the accuracy and applicability of sign language systems, we propose the AuraLLM and SignMST-C models.
CausalTAD: Causal Implicit Generative Model for Debiased Online Trajectory Anomaly Detection
Existing solutions directly train a generative model for observed trajectories and calculate the conditional generative probability $P({T}|{C})$ as the anomaly risk, where ${T}$ and ${C}$ represent the trajectory and SD pair respectively.
Comparison of Tiny Machine Learning Techniques for Embedded Acoustic Emission Analysis
These classifications can be performed based on the entire AE waveform or specific features that have been extracted from it.
Edge-Enhanced Dilated Residual Attention Network for Multimodal Medical Image Fusion
Multimodal medical image fusion is a crucial task that combines complementary information from different imaging modalities into a unified representation, thereby enhancing diagnostic accuracy and treatment planning.
On-device Anomaly Detection in Conveyor Belt Operations
This study proposes two distinctive pattern recognition approaches for real-time anomaly detection in the operational cycles of mining conveyor belts, combining feature extraction, threshold-based cycle detection, and tiny machine-learning classification.
DLP-LoRA: Efficient Task-Specific LoRA Fusion with a Dynamic, Lightweight Plugin for Large Language Models
This approach reduces inference time to less than twice that of single LoRA inference by leveraging parallel computation.
RmGPT: Rotating Machinery Generative Pretrained Model
Extensive experiments demonstrate that RmGPT significantly outperforms state-of-the-art algorithms, achieving near-perfect accuracy in diagnosis tasks and exceptionally low errors in prognosis tasks.
Scalable quantum dynamics compilation via quantum machine learning
In this work, we explore the potential of a VQC scheme by making use of out-of-distribution generalization results in quantum machine learning (QML): By learning the action of a given many-body dynamics on a small data set of product states, we can obtain a unitary circuit that generalizes to highly entangled states such as the Haar random states.
GeoFormer: Learning Point Cloud Completion with Tri-Plane Integrated Transformer
In this paper, we introduce a GeoFormer that simultaneously enhances the global geometric structure of the points and improves the local details.
CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer
We present CogVideoX, a large-scale text-to-video generation model based on diffusion transformer, which can generate 10-second continuous videos aligned with text prompt, with a frame rate of 16 fps and resolution of 768 * 1360 pixels.
Stable-Hair: Real-World Hair Transfer via Diffusion Model
In the second stage, we specifically designed a Hair Extractor and a Latent IdentityNet to transfer the target hairstyle with highly detailed and high-fidelity to the bald image.
EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model
Surprisingly, we observe that: (1) multimodal prompts and (2) vision-language models with early fusion (e. g., BEIT-3) are beneficial for prompting SAM for accurate referring segmentation.
Ranked #3 on
Referring Expression Segmentation
on RefCOCO testA
Cost-efficient Active Illumination Camera For Hyper-spectral Reconstruction
In addition, the ability to reconstruct hyperspectral data from multi-spectral input makes our device compatible to models and algorithms developed for hyperspectral applications with no modifications required.
ProcessPainter: Learn Painting Process from Sequence Data
The painting process of artists is inherently stepwise and varies significantly among different painters and styles.
Solution for Point Tracking Task of ICCV 1st Perception Test Challenge 2023
To address this issue, we propose a simple yet effective approach called TAP with confident static points (TAPIR+), which focuses on rectifying the tracking of the static point in the videos shot by a static camera.
Fast Personalized Text-to-Image Syntheses With Attention Injection
Currently, personalized image generation methods mostly require considerable time to finetune and often overfit the concept resulting in generated images that are similar to custom concepts but difficult to edit by prompts.
Stable-Makeup: When Real-World Makeup Transfer Meets Diffusion Model
Current makeup transfer methods are limited to simple makeup styles, making them difficult to apply in real-world scenarios.
SSR-Encoder: Encoding Selective Subject Representation for Subject-Driven Generation
Recent advancements in subject-driven image generation have led to zero-shot generation, yet precise selection and focus on crucial subject representations remain challenging.
CogAgent: A Visual Language Model for GUI Agents
People are spending an enormous amount of time on digital devices through graphical user interfaces (GUIs), e. g., computer or smartphone screens.
Ranked #4 on
on
FGNet: Towards Filling the Intra-class and Inter-class Gaps for Few-shot Segmentation
In this paper, we propose a uniform network to fill both the gaps, termed FGNet.
DPP-based Client Selection for Federated Learning with Non-IID Data
This paper proposes a client selection (CS) method to tackle the communication bottleneck of federated learning (FL) while concurrently coping with FL's data heterogeneity issue.
Video4MRI: An Empirical Study on Brain Magnetic Resonance Image Analytics with CNN-based Video Classification Frameworks
Due to the high similarity between MRI data and videos, we conduct extensive empirical studies on video recognition techniques for MRI classification to answer the questions: (1) can we directly use video recognition models for MRI classification, (2) which model is more appropriate for MRI, (3) are the common tricks like data augmentation in video recognition still useful for MRI classification?
Shakes on a Plane: Unsupervised Depth Estimation from Unstabilized Photography
Modern mobile burst photography pipelines capture and merge a short sequence of frames to recover an enhanced image, but often disregard the 3D nature of the scene they capture, treating pixel motion between images as a 2D aggregation problem.
Neural Volume Super-Resolution
This allows us to super-resolve the 3D scene representation by applying 2D convolutional networks on the 2D feature planes.
An Attention-based Multi-Scale Feature Learning Network for Multimodal Medical Image Fusion
Furthermore, we propose a novel fixed fusion strategy termed Softmax-based weighted strategy based on the Softmax weights and matrix nuclear norm.
An Edge Alignment-based Orientation Selection Method for Neutron Tomography
Neutron computed tomography (nCT) is a 3D characterization technique used to image the internal morphology or chemical composition of samples in biology and materials sciences.
All You Need is RAW: Defending Against Adversarial Attacks with Camera Image Pipelines
Various defense methods have proposed image-to-image mapping methods, either including these perturbations in the training process or removing them in a preprocessing denoising step.
The Implicit Values of A Good Hand Shake: Handheld Multi-Frame Neural Depth Refinement
Modern smartphones can continuously stream multi-megapixel RGB images at 60Hz, synchronized with high-quality 3D pose information and low-resolution LiDAR-driven depth estimates.
CelebHair: A New Large-Scale Dataset for Hairstyle Recommendation based on CelebA
In this paper, we present a new large-scale dataset for hairstyle recommendation, CelebHair, based on the celebrity facial attributes dataset, CelebA.
DatasetGAN: Efficient Labeled Data Factory with Minimal Human Effort
To showcase the power of our approach, we generated datasets for 7 image segmentation tasks which include pixel-level labels for 34 human face parts, and 32 car parts.
Adaptive Radar Detection and Classification Algorithms for Multiple Coherent Signals
In this paper, we address the problem of target detection in the presence of coherent (or fully correlated) signals, which can be due to multipath propagation effects or electronic attacks by smart jammers.
A prognostic dynamic model applicable to infectious diseases providing easily visualized guides -- A case study of COVID-19 in the UK
A reasonable prediction of infectious diseases transmission process under different disease control strategies is an important reference point for policy makers.
Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering
Key to our approach is to exploit GANs as a multi-view data generator to train an inverse graphics network using an off-the-shelf differentiable renderer, and the trained inverse graphics network as a teacher to disentangle the GAN's latent code into interpretable 3D properties.
A Combined Data-driven and Physics-driven Method for Steady Heat Conduction Prediction using Deep Convolutional Neural Networks
For the data-driven based method, the introduction of physical equation not only is able to speed up the convergence, but also produces physically more consistent solutions.
Deep Neural Network Fingerprinting by Conferrable Adversarial Examples
We propose a fingerprinting method for deep neural network classifiers that extracts a set of inputs from the source model so that only surrogates agree with the source model on the classification of such inputs.
