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Found 18 papers, 4 papers with code
Nonparametric Automatic Differentiation Variational Inference with Spline Approximation
Compared with widely-used nonparametric variational inference methods, the proposed method is easy to implement and adaptive to various data structures.
VQPy: An Object-Oriented Approach to Modern Video Analytics
Video analytics is widely used in contemporary systems and services.
AnyOKP: One-Shot and Instance-Aware Object Keypoint Extraction with Pretrained ViT
Towards flexible object-centric visual perception, we propose a one-shot instance-aware object keypoint (OKP) extraction approach, AnyOKP, which leverages the powerful representation ability of pretrained vision transformer (ViT), and can obtain keypoints on multiple object instances of arbitrary category after learning from a support image.
Ethosight: A Reasoning-Guided Iterative Learning System for Nuanced Perception based on Joint-Embedding & Contextual Label Affinity
Traditional computer vision models often necessitate extensive data acquisition, annotation, and validation.
Out-of-distribution forgetting: vulnerability of continual learning to intra-class distribution shift
In this work, we reported a special form of catastrophic forgetting raised by the OOD problem in continual learning settings, and we named it out-of-distribution forgetting (OODF).
Emergence of Symbols in Neural Networks for Semantic Understanding and Communication
The capacity to generate meaningful symbols and effectively employ them for advanced cognitive processes, such as communication, reasoning, and planning, constitutes a fundamental and distinctive aspect of human intelligence.
Adaptive Data Augmentation for Contrastive Learning
In computer vision, contrastive learning is the most advanced unsupervised learning framework.
TQ-Net: Mixed Contrastive Representation Learning For Heterogeneous Test Questions
Finally, supervised contrastive learning was conducted on relevance prediction-related downstream tasks, which helped the model to learn the representation of questions effectively.
AI of Brain and Cognitive Sciences: From the Perspective of First Principles
Nowadays, we have witnessed the great success of AI in various applications, including image classification, game playing, protein structure analysis, language translation, and content generation.
Effective Decision Boundary Learning for Class Incremental Learning
Rehearsal approaches in class incremental learning (CIL) suffer from decision boundary overfitting to new classes, which is mainly caused by two factors: insufficiency of old classes data for knowledge distillation and imbalanced data learning between the learned and new classes because of the limited storage memory.
BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster
To address this challenge, we have open sourced BigDL 2. 0 at https://github. com/intel-analytics/BigDL/ under Apache 2. 0 license (combining the original BigDL and Analytics Zoo projects); using BigDL 2. 0, users can simply build conventional Python notebooks on their laptops (with possible AutoML support), which can then be transparently accelerated on a single node (with up-to 9. 6x speedup in our experiments), and seamlessly scaled out to a large cluster (across several hundreds servers in real-world use cases).
Recursive Least-Squares Estimator-Aided Online Learning for Visual Tracking
In this paper, we propose a simple yet effective recursive least-squares estimator-aided online learning approach for few-shot online adaptation without requiring offline training.
Intra-Class Uncertainty Loss Function for Classification
To address this issue, we propose a loss function with intra-class uncertainty following Gaussian distribution.
EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks
Test samples with the backdoor key will then be classified into the target class specified by the attacker.
Progressive Relation Learning for Group Activity Recognition
Another relation-gating (RG) agent in continuous action space adjusts the high-level semantic graph to pay more attention to group-relevant relations.
Skeleton-Based Action Recognition with Synchronous Local and Non-local Spatio-temporal Learning and Frequency Attention
Benefiting from its succinctness and robustness, skeleton-based action recognition has recently attracted much attention.
Continual Learning of Context-dependent Processing in Neural Networks
Deep neural networks (DNNs) are powerful tools in learning sophisticated but fixed mapping rules between inputs and outputs, thereby limiting their application in more complex and dynamic situations in which the mapping rules are not kept the same but changing according to different contexts.
Ranked #5 on
Continual Learning
on ASC (19 tasks)
Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics
Disorders of consciousness are a heterogeneous mixture of different diseases or injuries.
Neurons and Cognition
