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Found 29 papers, 12 papers with code
TripletGAN: Training Generative Model with Triplet Loss
As an effective way of metric learning, triplet loss has been widely used in many deep learning tasks, including face recognition and person-ReID, leading to many states of the arts.
GASNet: Weakly-supervised Framework for COVID-19 Lesion Segmentation
Segmentation of infected areas in chest CT volumes is of great significance for further diagnosis and treatment of COVID-19 patients.
One-sample Guided Object Representation Disassembling
In this paper, we introduce the One-sample Guided Object Representation Disassembling (One-GORD) method, which only requires one annotated sample for each object category to learn disassembled object representation from unannotated images.
Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging
We describe the architecture and algorithms of the Adaptive Charging Network (ACN), which was first deployed on the Caltech campus in early 2016 and is currently operating at over 100 other sites in the United States.
Document Layout Analysis with Aesthetic-Guided Image Augmentation
To measure the proposed image layer modeling method, we propose a manually-labeled non-Manhattan layout fine-grained segmentation dataset named FPD.
Safe Distillation Box
In other words, all users may employ a model in SDB for inference, but only authorized users get access to KD from the model.
Attention-based Transformation from Latent Features to Point Clouds
The points generated by AXform do not have the strong 2-manifold constraint, which improves the generation of non-smooth surfaces.
Generate Point Clouds with Multiscale Details from Graph-Represented Structures
As details are missing in most representations of structures, the lack of controllability to details is one of the major weaknesses in structure-based controllable point cloud generation.
SRPCN: Structure Retrieval based Point Completion Network
Besides, the missing patterns are diverse in reality, but existing methods can only handle fixed ones, which means a poor generalization ability.
SIT: A Bionic and Non-Linear Neuron for Spiking Neural Network
Spiking Neural Networks (SNNs) have piqued researchers' interest because of their capacity to process temporal information and low power consumption.
Progressive Scene Text Erasing with Self-Supervision
Scene text erasing seeks to erase text contents from scene images and current state-of-the-art text erasing models are trained on large-scale synthetic data.
Aggregated Text Transformer for Scene Text Detection
We present the Aggregated Text TRansformer(ATTR), which is designed to represent texts in scene images with a multi-scale self-attention mechanism.
Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions
Thus, label-efficient deep learning methods are developed to make comprehensive use of the labeled data as well as the abundance of unlabeled and weak-labeled data.
DDT: Dual-branch Deformable Transformer for Image Denoising
Transformer is beneficial for image denoising tasks since it can model long-range dependencies to overcome the limitations presented by inductive convolutional biases.
WYTIWYR: A User Intent-Aware Framework with Multi-modal Inputs for Visualization Retrieval
Retrieving charts from a large corpus is a fundamental task that can benefit numerous applications such as visualization recommendations. The retrieved results are expected to conform to both explicit visual attributes (e. g., chart type, colormap) and implicit user intents (e. g., design style, context information) that vary upon application scenarios.
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
Linear Speedup of Incremental Aggregated Gradient Methods on Streaming Data
This paper considers a type of incremental aggregated gradient (IAG) method for large-scale distributed optimization.
ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction
To comprehensively understand their intrinsic semantics, in this paper, we obtain prototype representations for each type of event relation and propose a Prototype-Enhanced Matching (ProtoEM) framework for the joint extraction of multiple kinds of event relations.
Progressive Evidence Refinement for Open-domain Multimodal Retrieval Question Answering
Firstly, utilizing compressed evidence features as input to the model results in the loss of fine-grained information within the evidence.
DCQA: Document-Level Chart Question Answering towards Complex Reasoning and Common-Sense Understanding
Our DCQA dataset is expected to foster research on understanding visualizations in documents, especially for scenarios that require complex reasoning for charts in the visually-rich document.
Enhancing Object Coherence in Layout-to-Image Synthesis
Layout-to-image synthesis is an emerging technique in conditional image generation.
High-fidelity Person-centric Subject-to-Image Synthesis
Specifically, we first develop two specialized pre-trained diffusion models, i. e., Text-driven Diffusion Model (TDM) and Subject-augmented Diffusion Model (SDM), for scene and person generation, respectively.
UMAAF: Unveiling Aesthetics via Multifarious Attributes of Images
Then, to better utilize image attributes in aesthetic assessment, we propose the Unified Multi-attribute Aesthetic Assessment Framework (UMAAF) to model both absolute and relative attributes of images.
Shapley Values-enabled Progressive Pseudo Bag Augmentation for Whole Slide Image Classification
While most of the conventional MIL methods use attention scores to estimate instance importance scores (IIS) which contribute to the prediction of the slide labels, these often lead to skewed attention distributions and inaccuracies in identifying crucial instances.
Point Cloud Part Editing: Segmentation, Generation, Assembly, and Selection
Based on this process, we introduce SGAS, a model for part editing that employs two strategies: feature disentanglement and constraint.
EPA: Neural Collapse Inspired Robust Out-of-Distribution Detector
Out-of-distribution (OOD) detection plays a crucial role in ensuring the security of neural networks.
Out-of-Distribution Detection using Neural Activation Prior
Our neural activation prior is based on a key observation that, for a channel before the global pooling layer of a fully trained neural network, the probability of a few neurons being activated with a large response by an in-distribution (ID) sample is significantly higher than that by an OOD sample.
PrimeComposer: Faster Progressively Combined Diffusion for Image Composition with Attention Steering
This prior information is encoded into the attention weights, which are then integrated into the self-attention layers of the generator to guide the synthesis process.
Efficient scene text image super-resolution with semantic guidance
Scene text image super-resolution has significantly improved the accuracy of scene text recognition.
