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Found 10 papers, 2 papers with code
Large Motion Model for Unified Multi-Modal Motion Generation
In this work, we present Large Motion Model (LMM), a motion-centric, multi-modal framework that unifies mainstream motion generation tasks into a generalist model.
Conditional Logical Message Passing Transformer for Complex Query Answering
In this paper, we propose Conditional Logical Message Passing Transformer (CLMPT), which considers the difference between constants and variables in the case of using pre-trained neural link predictors and performs message passing conditionally on the node type.
Balancing the Causal Effects in Class-Incremental Learning
In this way, the model is encouraged to adapt to all classes with causal effects from both new and old data and thus alleviates the causal imbalance problem.
Class Incremental Learning
Continual Named Entity Recognition
+6
Multi-scale 2D Temporal Map Diffusion Models for Natural Language Video Localization
Our method involves the direct generation of a global 2D temporal map via a conditional denoising diffusion process, based on the input video and language query.
Delving Deep into the Generalization of Vision Transformers under Distribution Shifts
By comprehensively investigating these GE-ViTs and comparing with their corresponding CNN models, we observe: 1) For the enhanced model, larger ViTs still benefit more for the OOD generalization.
Towards Overcoming False Positives in Visual Relationship Detection
We observe that during training, the relationship proposal distribution is highly imbalanced: most of the negative relationship proposals are easy to identify, e. g., the inaccurate object detection, which leads to the under-fitting of low-frequency difficult proposals.
Bias-based Universal Adversarial Patch Attack for Automatic Check-out
To address the problem, this paper proposes a bias-based framework to generate class-agnostic universal adversarial patches with strong generalization ability, which exploits both the perceptual and semantic bias of models.
Training Robust Deep Neural Networks via Adversarial Noise Propagation
Various adversarial defense methods have accordingly been developed to improve adversarial robustness for deep models.
Interpreting and Improving Adversarial Robustness of Deep Neural Networks with Neuron Sensitivity
In this paper, we first draw the close connection between adversarial robustness and neuron sensitivities, as sensitive neurons make the most non-trivial contributions to model predictions in the adversarial setting.
PDA: Progressive Data Augmentation for General Robustness of Deep Neural Networks
In other words, DNNs trained with PDA are able to obtain more robustness against both adversarial attacks as well as common corruptions than the recent state-of-the-art methods.
