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Found 59 papers, 15 papers with code
Calibrated Multi-Preference Optimization for Aligning Diffusion Models
To address this, we present Calibrated Preference Optimization (CaPO), a novel method to align T2I diffusion models by incorporating the general preference from multiple reward models without human annotated data.
Focus-N-Fix: Region-Aware Fine-Tuning for Text-to-Image Generation
Text-to-image (T2I) generation has made significant advances in recent years, but challenges still remain in the generation of perceptual artifacts, misalignment with complex prompts, and safety.
HV-BEV: Decoupling Horizontal and Vertical Feature Sampling for Multi-View 3D Object Detection
Additionally, instead of relying on uniform sampling within a fixed height range, we introduce a height-aware module that incorporates historical information, enabling the reference points to adaptively focus on the varying heights at which objects appear in different scenes.
LVMark: Robust Watermark for latent video diffusion models
A key component of LVMark is a selective weight modulation strategy that efficiently embeds watermark messages into the video diffusion model while preserving the quality of the generated videos.
Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks and Diffusion Model
Motion artifacts present in magnetic resonance imaging (MRI) can seriously interfere with clinical diagnosis.
FastTrackTr:Towards Fast Multi-Object Tracking with Transformers
Transformer-based multi-object tracking (MOT) methods have captured the attention of many researchers in recent years.
Multi-object Tracking by Detection and Query: an efficient end-to-end manner
Multi-object tracking is advancing through two dominant paradigms: traditional tracking by detection and newly emerging tracking by query.
3D-GSW: 3D Gaussian Splatting for Robust Watermarking
In this paper, we introduce a robust watermarking method for 3D-GS that secures ownership of both the model and its rendered images.
Towards Student Actions in Classroom Scenes: New Dataset and Baseline
In this paper, we present a new multi-label student action video (SAV) dataset for complex classroom scenes.
Cropper: Vision-Language Model for Image Cropping through In-Context Learning
The goal of image cropping is to identify visually appealing crops within an image.
ArtVLM: Attribute Recognition Through Vision-Based Prefix Language Modeling
Recognizing and disentangling visual attributes from objects is a foundation to many computer vision applications.
Fluid-Antenna Enhanced ISAC: Joint Antenna Positioning and Dual-Functional Beamforming Design under Perfect and Imperfect CSI
Integrated sensing and communication (ISAC) emerges as an essential technique for overcoming spectrum congestion.
Optical Diffusion Models for Image Generation
Diffusion models generate new samples by progressively decreasing the noise from the initially provided random distribution.
Fluid-Antenna Enhanced Integrated Sensing and Communication: Joint Antenna Positioning and Beamforming Design
This paper investigates a fluid antenna (FA) enhanced integrated sensing and communication (ISAC) system consisting of a base station (BS), multiple single-antenna communication users, and one point target, where the BS is equipped with FAs to enhance both the communication and sensing performance.
A Survey of Deep Learning Based Radar and Vision Fusion for 3D Object Detection in Autonomous Driving
This paper focuses on a comprehensive survey of radar-vision (RV) fusion based on deep learning methods for 3D object detection in autonomous driving.
MonoDETRNext: Next-Generation Accurate and Efficient Monocular 3D Object Detector
Monocular 3D object detection has vast application potential across various fields.
WateRF: Robust Watermarks in Radiance Fields for Protection of Copyrights
The advances in the Neural Radiance Fields (NeRF) research offer extensive applications in diverse domains, but protecting their copyrights has not yet been researched in depth.
Parrot: Pareto-optimal Multi-Reward Reinforcement Learning Framework for Text-to-Image Generation
We use the novel multi-reward optimization algorithm to jointly optimize the T2I model and a prompt expansion network, resulting in significant improvement of image quality and also allow to control the trade-off of different rewards using a reward related prompt during inference.
Gemini: A Family of Highly Capable Multimodal Models
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding.
Ranked #1 on
Visual Question Answering (VQA)
on InfographicVQA
Rich Human Feedback for Text-to-Image Generation
We show that the predicted rich human feedback can be leveraged to improve image generation, for example, by selecting high-quality training data to finetune and improve the generative models, or by creating masks with predicted heatmaps to inpaint the problematic regions.
A Surrogate-Assisted Extended Generative Adversarial Network for Parameter Optimization in Free-Form Metasurface Design
Metasurfaces have widespread applications in fifth-generation (5G) microwave communication.
Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric Chest X-ray images
Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications.
Semantically Redundant Training Data Removal and Deep Model Classification Performance: A Study with Chest X-rays
Deep learning (DL) has demonstrated its innate capacity to independently learn hierarchical features from complex and multi-dimensional data.
Re-mine, Learn and Reason: Exploring the Cross-modal Semantic Correlations for Language-guided HOI detection
Human-Object Interaction (HOI) detection is a challenging computer vision task that requires visual models to address the complex interactive relationship between humans and objects and predict HOI triplets.
Forward-Forward Training of an Optical Neural Network
Neural networks (NN) have demonstrated remarkable capabilities in various tasks, but their computation-intensive nature demands faster and more energy-efficient hardware implementations.
Segmentation of Aortic Vessel Tree in CT Scans with Deep Fully Convolutional Networks
Automatic and accurate segmentation of aortic vessel tree (AVT) in computed tomography (CT) scans is crucial for early detection, diagnosis and prognosis of aortic diseases, such as aneurysms, dissections and stenosis.
Soundini: Sound-Guided Diffusion for Natural Video Editing
We propose a method for adding sound-guided visual effects to specific regions of videos with a zero-shot setting.
VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining
Our results show that our pretrained aesthetic vision-language model outperforms prior works on image aesthetic captioning over the AVA-Captions dataset, and it has powerful zero-shot capability for aesthetic tasks such as zero-shot style classification and zero-shot IAA, surpassing many supervised baselines.
Ranked #46 on
Video Quality Assessment
on MSU SR-QA Dataset
SVDiff: Compact Parameter Space for Diffusion Fine-Tuning
Diffusion models have achieved remarkable success in text-to-image generation, enabling the creation of high-quality images from text prompts or other modalities.
MRET: Multi-resolution Transformer for Video Quality Assessment
No-reference video quality assessment (NR-VQA) for user generated content (UGC) is crucial for understanding and improving visual experience.
CSDN: Combing Shallow and Deep Networks for Accurate Real-time Segmentation of High-definition Intravascular Ultrasound Images
In this paper, we propose a two-stream framework for efficient segmentation of 60 MHz high resolution IVUS images.
Does image resolution impact chest X-ray based fine-grained Tuberculosis-consistent lesion segmentation?
Literature is sparse in discussing the optimal image resolution to train these models for segmenting the Tuberculosis (TB)-consistent lesions in CXRs.
Generalizability of Deep Adult Lung Segmentation Models to the Pediatric Population: A Retrospective Study
In this work, our goal is to (i) analyze the generalizability of deep adult lung segmentation models to the pediatric population and (ii) improve performance through a stage-wise, systematic approach consisting of CXR modality-specific weight initializations, stacked ensembles, and an ensemble of stacked ensembles.
Video object tracking based on YOLOv7 and DeepSORT
Among the current popular MOT methods based on deep learning, Detection Based Tracking (DBT) is the most widely used in industry, and the performance of them depend on their object detection network.
Deep ensemble learning for segmenting tuberculosis-consistent manifestations in chest radiographs
We observed that the stacking ensemble demonstrated superior segmentation performance (Dice score: 0. 5743, 95% confidence interval: (0. 4055, 0. 7431)) compared to the individual constituent models and other ensemble methods.
MaxViT: Multi-Axis Vision Transformer
We also show that our proposed model expresses strong generative modeling capability on ImageNet, demonstrating the superior potential of MaxViT blocks as a universal vision module.
Ranked #1 on
Object Detection
on COCO 2017
Contrastive Embedding Distribution Refinement and Entropy-Aware Attention for 3D Point Cloud Classification
Learning a powerful representation from point clouds is a fundamental and challenging problem in the field of computer vision.
MAXIM: Multi-Axis MLP for Image Processing
In this work, we present a multi-axis MLP based architecture called MAXIM, that can serve as an efficient and flexible general-purpose vision backbone for image processing tasks.
Ranked #1 on
Deblurring
on HIDE (trained on GOPRO)
MUSIQ: Multi-scale Image Quality Transformer
To accommodate this, the input images are usually resized and cropped to a fixed shape, causing image quality degradation.
Ranked #3 on
Image Quality Assessment
on MSU NR VQA Database
An Improved Algorithm of Robot Path Planning in Complex Environment Based on Double DQN
Deep Q Network (DQN) has several limitations when applied in planning a path in environment with a number of dilemmas according to our experiment.
Rich Features for Perceptual Quality Assessment of UGC Videos
Besides the subjective ratings and content labels of the dataset, we also propose a DNN-based framework to thoroughly analyze importance of content, technical quality, and compression level in perceptual quality.
Adversarially Adaptive Normalization for Single Domain Generalization
As a generic tool, the improvement introduced by ASR-Norm is agnostic to the choice of ADA methods.
COMISR: Compression-Informed Video Super-Resolution
Most video super-resolution methods focus on restoring high-resolution video frames from low-resolution videos without taking into account compression.
Deep 3D-to-2D Watermarking: Embedding Messages in 3D Meshes and Extracting Them from 2D Renderings
Digital watermarking is widely used for copyright protection.
Deep Perceptual Image Quality Assessment for Compression
In this paper we propose the largest image compression quality dataset to date with human perceptual preferences, enabling the use of deep learning, and we develop a full reference perceptual quality assessment metric for lossy image compression that outperforms the existing state-of-the-art methods.
Multi-path Neural Networks for On-device Multi-domain Visual Classification
This approach effectively reduces the total number of parameters and FLOPS, encouraging positive knowledge transfer while mitigating negative interference across domains.
The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study
As a case study, we focus on the design of the quantization tables in the JPEG compression standard.
GIFnets: Differentiable GIF Encoding Framework
DitherNet manipulates the input image to reduce color banding artifacts and provides an alternative to traditional dithering.
Two-Phase Multi-Party Computation Enabled Privacy-Preserving Federated Learning
Countries across the globe have been pushing strict regulations on the protection of personal or private data collected.
Distributed, Parallel, and Cluster Computing
Super-Resolving Commercial Satellite Imagery Using Realistic Training Data
We propose a realistic training data generation model for commercial satellite imagery products, which includes not only the imaging process on satellites but also the post-process on the ground.
Better Compression with Deep Pre-Editing
In this work we aim to break the unholy connection between bit-rate and image quality, and propose a way to circumvent compression artifacts by pre-editing the incoming image and modifying its content to fit the given bits.
Distortion Agnostic Deep Watermarking
Watermarking is the process of embedding information into an image that can survive under distortions, while requiring the encoded image to have little or no perceptual difference from the original image.
CNN-Based Invertible Wavelet Scattering for the Investigation of Diffusion Properties of the In Vivo Human Heart in Diffusion Tensor Imaging
The results on both simulated and acquired in vivo cardiac DW images showed that the proposed WSCNN method effectively compensates for motion-induced signal loss and produces in vivo cardiac DW images with better quality and more coherent fiber structures with respect to existing methods, which makes it an interesting method for measuring correctly the diffusion properties of the in vivo human heart in DTI under free breathing.
Global Fitting of the Response Surface via Estimating Multiple Contours of a Simulator
One fundamental issue in the study of computer simulators is known as experimental design, that is, how to select the input settings where the computer simulator is run and the corresponding response is collected.
A Multi-stage Framework with Context Information Fusion Structure for Skin Lesion Segmentation
The computer-aided diagnosis (CAD) systems can highly improve the reliability and efficiency of melanoma recognition.
A Comparison of 1-D and 2-D Deep Convolutional Neural Networks in ECG Classification
Then, in order to alleviate the overfitting problem in two-dimensional network, we initialize AlexNet-like network with weights trained on ImageNet, to fit the training ECG images and fine-tune the model, and to further improve the accuracy and robustness of ECG classification.
Learning the Synthesizability of Dynamic Texture Samples
In this paper, we investigate the synthesizability of dynamic texture samples: {\em given a dynamic texture sample, how synthesizable it is by using EDTS, and which EDTS method is the most suitable to synthesize it?}
McKernel: A Library for Approximate Kernel Expansions in Log-linear Time
The algorithm requires to compute the product of Walsh Hadamard Transform (WHT) matrices.
Image tag completion by local learning
The problem of tag completion is to learn the missing tags of an image.
