Search Results for author:
Found 48 papers, 22 papers with code
Accurate Tracking of Arabidopsis Root Cortex Cell Nuclei in 3D Time-Lapse Microscopy Images Based on Genetic Algorithm
To the best of our knowledge, this research represents the first successful attempt to address a long-standing problem in the field of time-lapse microscopy in the root meristem by proposing an accurate tracking method for Arabidopsis root nuclei.
IAP: Improving Continual Learning of Vision-Language Models via Instance-Aware Prompting
Recent pre-trained vision-language models (PT-VLMs) often face a Multi-Domain Class-Incremental Learning (MCIL) scenario in practice, where several classes and domains of multi-modal tasks are incrementally arrived.
Video SimpleQA: Towards Factuality Evaluation in Large Video Language Models
Recent advancements in Large Video Language Models (LVLMs) have highlighted their potential for multi-modal understanding, yet evaluating their factual grounding in video contexts remains a critical unsolved challenge.
From System 1 to System 2: A Survey of Reasoning Large Language Models
Achieving human-level intelligence requires refining the transition from the fast, intuitive System 1 to the slower, more deliberate System 2 reasoning.
CE-SDWV: Effective and Efficient Concept Erasure for Text-to-Image Diffusion Models via a Semantic-Driven Word Vocabulary
Large-scale text-to-image (T2I) diffusion models have achieved remarkable generative performance about various concepts.
Resource-Constrained Federated Continual Learning: What Does Matter?
We revisit this problem with a large-scale benchmark and analyze the performance of state-of-the-art FCL approaches under different resource-constrained settings.
SimC3D: A Simple Contrastive 3D Pretraining Framework Using RGB Images
To this end, we propose SimC3D, a simple but effective 3D contrastive learning framework, for the first time, pretraining 3D backbones from pure RGB image data.
Federated Incremental Named Entity Recognition
In a more realistic scenario, local clients receive new entity types continuously, while new local clients collecting novel data may irregularly join the global FNER training.
Continual LLaVA: Continual Instruction Tuning in Large Vision-Language Models
In terms of methodology, we propose Continual LLaVA, a rehearsal-free method tailored for continual instruction tuning in LVLMs.
How to Continually Adapt Text-to-Image Diffusion Models for Flexible Customization?
Moreover, they heavily suffer from catastrophic forgetting and concept neglect on old personalized concepts when continually learning a series of new concepts.
Deep Self-Cleansing for Medical Image Segmentation with Noisy Labels
To achieve this, we devise a gaussian mixture model-based label filtering module that distinguishes noisy labels from clean labels.
PECTP: Parameter-Efficient Cross-Task Prompts for Incremental Vision Transformer
Prompt-fixed methods only learn a single set of prompts on one of the incremental tasks and can not handle all the incremental tasks effectively.
Dual Hyperspectral Mamba for Efficient Spectral Compressive Imaging
Experiments verify the benefits of our DHM for HSI reconstruction.
Rehearsal-free Federated Domain-incremental Learning
A key feature of RefFiL is the generation of local fine-grained prompts by our domain adaptive prompt generator, which effectively learns from local domain knowledge while maintaining distinctive boundaries on a global scale.
MuseumMaker: Continual Style Customization without Catastrophic Forgetting
To deal with catastrophic forgetting amongst past learned styles, we devise a dual regularization for shared-LoRA module to optimize the direction of model update, which could regularize the diffusion model from both weight and feature aspects, respectively.
Never-Ending Behavior-Cloning Agent for Robotic Manipulation
It can continually learn observation knowledge of novel 3D scene semantics and robot manipulation skills from skill-shared and skill-specific attributes, respectively.
Federated Learning with New Knowledge: Fundamentals, Advances, and Futures
Federated Learning (FL) is a privacy-preserving distributed learning approach that is rapidly developing in an era where privacy protection is increasingly valued.
ViCA-NeRF: View-Consistency-Aware 3D Editing of Neural Radiance Fields
In addition to the implicit neural radiance field (NeRF) modeling, our key insight is to exploit two sources of regularization that explicitly propagate the editing information across different views, thus ensuring multi-view consistency.
MM-LLMs: Recent Advances in MultiModal Large Language Models
In the past year, MultiModal Large Language Models (MM-LLMs) have undergone substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs via cost-effective training strategies.
Federated Continual Novel Class Learning
In a privacy-focused era, Federated Learning (FL) has emerged as a promising machine learning technique.
Continual Named Entity Recognition without Catastrophic Forgetting
This issue is intensified in CNER due to the consolidation of old entity types from previous steps into the non-entity type at each step, leading to what is known as the semantic shift problem of the non-entity type.
Continual Named Entity Recognition
named-entity-recognition
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Create Your World: Lifelong Text-to-Image Diffusion
Text-to-image generative models can produce diverse high-quality images of concepts with a text prompt, which have demonstrated excellent ability in image generation, image translation, etc.
I3DOD: Towards Incremental 3D Object Detection via Prompting
Meanwhile, the current class-incremental 3D object detection methods neglect the relationships between the object localization information and category semantic information and assume all the knowledge of old model is reliable.
Task Relation Distillation and Prototypical Pseudo Label for Incremental Named Entity Recognition
However, INER faces the challenge of catastrophic forgetting specific for incremental learning, further aggravated by background shift (i. e., old and future entity types are labeled as the non-entity type in the current task).
Heterogeneous Forgetting Compensation for Class-Incremental Learning
To surmount the above challenges, we develop a novel Heterogeneous Forgetting Compensation (HFC) model, which can resolve heterogeneous forgetting of easy-to-forget and hard-to-forget old categories from both representation and gradient aspects.
Self-paced Weight Consolidation for Continual Learning
Continual learning algorithms which keep the parameters of new tasks close to that of previous tasks, are popular in preventing catastrophic forgetting in sequential task learning settings.
Gradient-Semantic Compensation for Incremental Semantic Segmentation
To tackle the above challenges, in this paper, we propose a Gradient-Semantic Compensation (GSC) model, which surmounts incremental semantic segmentation from both gradient and semantic perspectives.
Towards AI-Architecture Liberty: A Comprehensive Survey on Design and Generation of Virtual Architecture by Deep Learning
3D shape generation techniques leveraging deep learning have garnered significant interest from both the computer vision and architectural design communities, promising to enrich the content in the virtual environment.
Tailored Multi-Organ Segmentation with Model Adaptation and Ensemble
Multi-organ segmentation, which identifies and separates different organs in medical images, is a fundamental task in medical image analysis.
Federated Incremental Semantic Segmentation
Moreover, new clients collecting novel classes may join in the global training of FSS, which further exacerbates catastrophic forgetting.
Xformer: Hybrid X-Shaped Transformer for Image Denoising
The channel-wise Transformer block performs direct global context interactions across tokens defined by channel dimension.
InOR-Net: Incremental 3D Object Recognition Network for Point Cloud Representation
Moreover, they cannot explore which 3D geometric characteristics are essential to alleviate the catastrophic forgetting on old classes of 3D objects.
No One Left Behind: Real-World Federated Class-Incremental Learning
These issues render global model to undergo catastrophic forgetting on old categories, when local clients receive new categories consecutively under limited memory of storing old categories.
Is Out-of-Distribution Detection Learnable?
Based on this observation, we next give several necessary and sufficient conditions to characterize the learnability of OOD detection in some practical scenarios.
BoW3D: Bag of Words for Real-Time Loop Closing in 3D LiDAR SLAM
To address this limitation, we present a novel Bag of Words for real-time loop closing in 3D LiDAR SLAM, called BoW3D.
LinK3D: Linear Keypoints Representation for 3D LiDAR Point Cloud
Feature extraction and matching are the basic parts of many robotic vision tasks, such as 2D or 3D object detection, recognition, and registration.
Federated Class-Incremental Learning
It makes the global model suffer from significant catastrophic forgetting on old classes in real-world scenarios, where local clients often collect new classes continuously and have very limited storage memory to store old classes.
Exploring the Equivalence of Siamese Self-Supervised Learning via A Unified Gradient Framework
These methods appear to be quite different in the designed loss functions from various motivations.
Confident Anchor-Induced Multi-Source Free Domain Adaptation
To address these challenges, we develop a novel Confident-Anchor-induced multi-source-free Domain Adaptation (CAiDA) model, which is a pioneer exploration of knowledge adaptation from multiple source domains to the unlabeled target domain without any source data, but with only pre-trained source models.
Unsupervised Dense Deformation Embedding Network for Template-Free Shape Correspondence
Shape correspondence from 3D deformation learning has attracted appealing academy interests recently.
Generative Partial Visual-Tactile Fused Object Clustering
More specifically, we first do partial visual and tactile features extraction from the partial visual and tactile data, respectively, and encode the extracted features in modality-specific feature subspaces.
I3DOL: Incremental 3D Object Learning without Catastrophic Forgetting
Moreover, the performance of advanced approaches degrades dramatically for past learned classes (i. e., catastrophic forgetting), due to the irregular and redundant geometric structures of 3D point cloud data.
Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation
Weakly-supervised learning has attracted growing research attention on medical lesions segmentation due to significant saving in pixel-level annotation cost.
CSCL: Critical Semantic-Consistent Learning for Unsupervised Domain Adaptation
Unsupervised domain adaptation without consuming annotation process for unlabeled target data attracts appealing interests in semantic segmentation.
Evolving Metric Learning for Incremental and Decremental Features
Online metric learning has been widely exploited for large-scale data classification due to the low computational cost.
What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation
Unsupervised domain adaptation has attracted growing research attention on semantic segmentation.
Data Poisoning Attacks on Federated Machine Learning
To the end, experimental results on real-world datasets show that federated multi-task learning model is very sensitive to poisoning attacks, when the attackers either directly poison the target nodes or indirectly poison the related nodes by exploiting the communication protocol.
Semantic-Transferable Weakly-Supervised Endoscopic Lesions Segmentation
To better utilize these dependencies, we present a new semantic lesions representation transfer model for weakly-supervised endoscopic lesions segmentation, which can exploit useful knowledge from relevant fully-labeled diseases segmentation task to enhance the performance of target weakly-labeled lesions segmentation task.
