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Found 13 papers, 5 papers with code
LGSDF: Continual Global Learning of Signed Distance Fields Aided by Local Updating
Implicit reconstruction of ESDF (Euclidean Signed Distance Field) involves training a neural network to regress the signed distance from any point to the nearest obstacle, which has the advantages of lightweight storage and continuous querying.
Context-Semantic Quality Awareness Network for Fine-Grained Visual Categorization
To tackle this challenge, we propose a weakly supervised Context-Semantic Quality Awareness Network (CSQA-Net) for FGVC.
Ranked #4 on
Fine-Grained Image Classification
on NABirds
Fine-Grained Image Classification
Fine-Grained Visual Categorization
OpenGraph: Open-Vocabulary Hierarchical 3D Graph Representation in Large-Scale Outdoor Environments
In this work, we propose OpenGraph, the first open-vocabulary hierarchical graph representation designed for large-scale outdoor environments.
Robust Transductive Few-shot Learning via Joint Message Passing and Prototype-based Soft-label Propagation
Label propagation methods try to propagate the labels of support samples on the constructed graph encoding the relationships between both support and query samples.
Density Distribution-based Learning Framework for Addressing Online Continual Learning Challenges
CL, especially the Class Incremental Learning, enables adaptation to new test distributions while continuously learning from a single-pass training data stream, which is more in line with the practical application requirements of real-world scenarios.
DFB: A Data-Free, Low-Budget, and High-Efficacy Clean-Label Backdoor Attack
DFB is unique in its independence from training data access, requiring solely the knowledge of a specific target class.
Stabilization with Prescribed Instant via Lyapunov Method
In anothor word, the settling time under the presented controller is independent of the initial conditions and equals the prescribed time instant.
Few-Shot Point Cloud Semantic Segmentation via Contrastive Self-Supervision and Multi-Resolution Attention
Moreover, a case study on practical CAM/CAD segmentation is presented to demonstrate the effectiveness of our approach for real-world applications.
Fine-Grained Visual Classification via Internal Ensemble Learning Transformer
The proposed IELT involves three main modules: multi-head voting (MHV) module, cross-layer refinement (CLR) module, and dynamic selection (DS) module.
Masked Self-Supervision for Remaining Useful Lifetime Prediction in Machine Tools
With the availability of deep learning approaches, the great potential and prospect of utilizing these for RUL prediction have resulted in several models which are designed driven by operation data of manufacturing machines.
CAM/CAD Point Cloud Part Segmentation via Few-Shot Learning
However, the disadvantage is that the resulting models from the fully-supervised learning methodology are highly reliant on the completeness of the available dataset, and its generalization ability is relatively poor to new unknown segmentation types (i. e. further additional novel classes).
Cross-Enhancement Transformer for Action Segmentation
Temporal convolutions have been the paradigm of choice in action segmentation, which enhances long-term receptive fields by increasing convolution layers.
Ranked #5 on
Action Segmentation
on Breakfast
Schrödinger's FP: Dynamic Adaptation of Floating-Point Containers for Deep Learning Training
We introduce a software-hardware co-design approach to reduce memory traffic and footprint during training with BFloat16 or FP32 boosting energy efficiency and execution time performance.
