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Found 27 papers, 10 papers with code
CFTrack: Enhancing Lightweight Visual Tracking through Contrastive Learning and Feature Matching
Achieving both efficiency and strong discriminative ability in lightweight visual tracking is a challenge, especially on mobile and edge devices with limited computational resources.
A Comprehensive Survey on the Trustworthiness of Large Language Models in Healthcare
However, the trustworthiness of LLMs in healthcare remains underexplored, lacking a systematic review that provides a comprehensive understanding and future insights into this area.
Generalizable Cervical Cancer Screening via Large-scale Pretraining and Test-Time Adaptation
In retrospective cohorts, Smart-CCS achieved an overall area under the curve (AUC) value of 0. 965 and sensitivity of 0. 913 for cancer screening on 11 internal test datasets.
HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image Classification
Fine-grained classification of whole slide images (WSIs) is essential in precision oncology, enabling precise cancer diagnosis and personalized treatment strategies.
2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less Attenuation Correction
To address these challenges, we developed a novel 2. 5D Multi-view Averaging Diffusion Model (MADM) for 3D image-to-image translation with application on NAC-LDPET to AC-SDPET translation.
Cascaded Multi-path Shortcut Diffusion Model for Medical Image Translation
To reduce the required number of iterations and ensure robust performance, our method first obtains a conditional GAN-generated prior image that will be used for the efficient reverse translation with a DM in the subsequent step.
SceneX: Procedural Controllable Large-scale Scene Generation
Extensive experiments demonstrated the capability of our method in controllable large-scale scene generation, including nature scenes and unbounded cities, as well as scene editing such as asset placement and season translation.
DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning
Federated learning (FL) has emerged as a powerful paradigm for learning from decentralized data, and federated domain generalization further considers the test dataset (target domain) is absent from the decentralized training data (source domains).
POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation
Low-dose PET offers a valuable means of minimizing radiation exposure in PET imaging.
Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators
Theoretically, we show the convergence guarantee of the dual regulators.
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET Denoising
While previous federated learning (FL) algorithms enable multi-institution collaborative training without the need of aggregating local data, addressing the large domain shift in the application of multi-institutional low-count PET denoising remains a challenge and is still highly under-explored.
Scale-Prior Deformable Convolution for Exemplar-Guided Class-Agnostic Counting
Here we propose a scale-sensitive generalized loss to tackle this problem.
Ranked #10 on
Object Counting
on FSC147
An Empirical Study of Pseudo-Labeling for Image-based 3D Object Detection
We hope this work can provide insights for the image-based 3D detection community under a semi-supervised setting.
StyleFlow For Content-Fixed Image to Image Translation
We evaluate our model in several I2I translation benchmarks, and the results show that the proposed model has advantages over previous methods in both strongly constrained and normally constrained tasks.
Ranked #1 on
Style Transfer
on WikiArt
Probing Visual-Audio Representation for Video Highlight Detection via Hard-Pairs Guided Contrastive Learning
Our proposed method mainly leverages the intra-modality encoding and cross-modality co-occurrence encoding for fully representation modeling.
Pyramid Region-based Slot Attention Network for Temporal Action Proposal Generation
Based on PRSlot modules, we present a novel Pyramid Region-based Slot Attention Network termed PRSA-Net to learn a unified visual representation with rich temporal and semantic context for better proposal generation.
Better Teacher Better Student: Dynamic Prior Knowledge for Knowledge Distillation
Besides, our DPK makes the performance of the student model positively correlated with that of the teacher model, which means that we can further boost the accuracy of students by applying larger teachers.
Understanding CNNs from excitations
To organize these excitations into final saliency maps, we introduce a double-chain backpropagation procedure.
Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention
A key feature of the multi-label setting is that images often have multiple labels, which typically refer to different regions of the image.
GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer
It captures spatial-temporal contextual information jointly to augment the individual and group representations effectively with a clustered spatial-temporal transformer.
Learning Geometry-Guided Depth via Projective Modeling for Monocular 3D Object Detection
In this paper, we propose to learn geometry-guided depth estimation with projective modeling to advance monocular 3D object detection.
Ranked #11 on
Monocular 3D Object Detection
on KITTI Cars Moderate
Video Crowd Localization with Multi-focus Gaussian Neighborhood Attention and a Large-Scale Benchmark
Video crowd localization is a crucial yet challenging task, which aims to estimate exact locations of human heads in the given crowded videos.
A Cloud-Based Energy Management Strategy for Hybrid Electric City Bus Considering Real-Time Passenger Load Prediction
The Gradient Boost Decision Tree model is selected due to its best accuracy and high stability.
Learning Time Reduction Using Warm Start Methods for a Reinforcement Learning Based Supervisory Control in Hybrid Electric Vehicle Applications
This study aims to reduce the learning iterations of Q-learning in HEV application and improve fuel consumption in initial learning phases utilizing warm start methods.
A Non-Intrusive Correction Algorithm for Classification Problems with Corrupted Data
A novel correction algorithm is proposed for multi-class classification problems with corrupted training data.
GTC: Guided Training of CTC Towards Efficient and Accurate Scene Text Recognition
Extensive experiments on standard benchmarks demonstrate that our end-to-end model achieves a new state-of-the-art for regular and irregular scene text recognition and needs 6 times shorter inference time than attentionbased methods.
Generation of Virtual Dual Energy Images from Standard Single-Shot Radiographs using Multi-scale and Conditional Adversarial Network
Dual-energy (DE) chest radiographs provide greater diagnostic information than standard radiographs by separating the image into bone and soft tissue, revealing suspicious lesions which may otherwise be obstructed from view.
