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Found 20 papers, 8 papers with code
Prompt-Agnostic Adversarial Perturbation for Customized Diffusion Models
In this paper, we introduce a Prompt-Agnostic Adversarial Perturbation (PAP) method for customized diffusion models.
Overview of AI-Debater 2023: The Challenges of Argument Generation Tasks
In this paper we present the results of the AI-Debater 2023 Challenge held by the Chinese Conference on Affect Computing (CCAC 2023), and introduce the related datasets.
Beyond Prompt Learning: Continual Adapter for Efficient Rehearsal-Free Continual Learning
Thus, in this paper, we propose a beyond prompt learning approach to the RFCL task, called Continual Adapter (C-ADA).
Projecting Points to Axes: Oriented Object Detection via Point-Axis Representation
This paper introduces the point-axis representation for oriented object detection, emphasizing its flexibility and geometrically intuitive nature with two key components: points and axes.
SPEAR: Receiver-to-Receiver Acoustic Neural Warping Field
We present SPEAR, a continuous receiver-to-receiver acoustic neural warping field for spatial acoustic effects prediction in an acoustic 3D space with a single stationary audio source.
Continual Novel Class Discovery via Feature Enhancement and Adaptation
Continual Novel Class Discovery (CNCD) aims to continually discover novel classes without labels while maintaining the recognition capability for previously learned classes.
I2CANSAY:Inter-Class Analogical Augmentation and Intra-Class Significance Analysis for Non-Exemplar Online Task-Free Continual Learning
Online task-free continual learning (OTFCL) is a more challenging variant of continual learning which emphasizes the gradual shift of task boundaries and learns in an online mode.
CEAT: Continual Expansion and Absorption Transformer for Non-Exemplar Class-Incremental Learning
Besides, to address the classifier bias towards the new classes, we propose a novel approach to generate the pseudo-features to correct the classifier.
Emerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendations
With the boom of e-commerce and web applications, recommender systems have become an important part of our daily lives, providing personalized recommendations based on the user's preferences.
4D-Rotor Gaussian Splatting: Towards Efficient Novel View Synthesis for Dynamic Scenes
We consider the problem of novel-view synthesis (NVS) for dynamic scenes.
DYSON: Dynamic Feature Space Self-Organization for Online Task-Free Class Incremental Learning
Then we propose a novel Dynamic feature space Self-Organization (DYSON) method containing three major components including 1) a feature extractor 2) a Dynamic Feature-Geometry Alignment (DFGA) module aligning the feature space to the optimal geometry computed by DNC and 3) a training-free class-incremental classifier derived from the DNC geometry.
Multi-body SE(3) Equivariance for Unsupervised Rigid Segmentation and Motion Estimation
A truly generalizable approach to rigid segmentation and motion estimation is fundamental to 3D understanding of articulated objects and moving scenes.
Bayesian inference and neural estimation of acoustic wave propagation
In this work, we introduce a novel framework which combines physics and machine learning methods to analyse acoustic signals.
Knowledge Restore and Transfer for Multi-label Class-Incremental Learning
Current class-incremental learning research mainly focuses on single-label classification tasks while multi-label class-incremental learning (MLCIL) with more practical application scenarios is rarely studied.
DKT: Diverse Knowledge Transfer Transformer for Class Incremental Learning
Deep neural networks suffer from catastrophic forgetting in class incremental learning, where the classification accuracy of old classes drastically deteriorates when the networks learn the knowledge of new classes.
Learning 3D Semantics from Pose-Noisy 2D Images with Hierarchical Full Attention Network
This motivates us to conduct a "task transfer" paradigm so that 3D semantic segmentation benefits from aggregating 2D semantic cues, albeit pose noises are contained in 2D image observations.
DeepAoANet: Learning Angle of Arrival from Software Defined Radios with Deep Neural Networks
To demonstrate the utility of our approach we have collected IQ (In-phase and Quadrature components) samples from a four-element Universal Linear Array (ULA) in various Light-of-Sight (LOS) and Non-Line-of-Sight (NLOS) environments, and published the dataset.
Sound Source Detection from Raw Waveforms with Multi-Scale Synperiodic Filterbanks
Convolution of the proposed filterbanks with the raw waveform helps to achieve multi-scale perception in the time domain.
SoundDet: Polyphonic Moving Sound Event Detection and Localization from Raw Waveform
Specifically, SoundDet consists of a backbone neural network and two parallel heads for temporal detection and spatial localization, respectively.
Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration
In this paper, we focus on the multi-object tracking (MOT) problem of automatic driving and robot navigation.
