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Found 11 papers, 5 papers with code
Rethinking Target Label Conditioning in Adversarial Attacks: A 2D Tensor-Guided Generative Approach
Compared to single-target adversarial attacks, multi-target attacks have garnered significant attention due to their ability to generate adversarial images for multiple target classes simultaneously.
Analyzable Chain-of-Musical-Thought Prompting for High-Fidelity Music Generation
However, the conventional next-token prediction paradigm in AR models does not align with the human creative process in music composition, potentially compromising the musicality of generated samples.
Enhancing Adversarial Transferability via Component-Wise Transformation
This limitation arises because, while models of the same architecture may focus on different regions of the object, the variation is even more pronounced across different architectures.
A Hybrid Defense Strategy for Boosting Adversarial Robustness in Vision-Language Models
The neural network model achieved a high accuracy of 98% in these challenging classification tasks, while the XGBoost model reached a success rate of 85. 26% in prediction tasks.
An End-to-End Approach for Chord-Conditioned Song Generation
The Song Generation task aims to synthesize music composed of vocals and accompaniment from given lyrics.
SongCreator: Lyrics-based Universal Song Generation
While various aspects of song generation have been explored by previous works, such as singing voice, vocal composition and instrumental arrangement, etc., generating songs with both vocals and accompaniment given lyrics remains a significant challenge, hindering the application of music generation models in the real world.
WebCanvas: Benchmarking Web Agents in Online Environments
To bridge this gap, we introduce WebCanvas, an innovative online evaluation framework for web agents that effectively addresses the dynamic nature of web interactions.
Uncovering What, Why and How: A Comprehensive Benchmark for Causation Understanding of Video Anomaly
In pursuit of these answers, we present a comprehensive benchmark for Causation Understanding of Video Anomaly (CUVA).
Uncovering What Why and How: A Comprehensive Benchmark for Causation Understanding of Video Anomaly
In addition we also introduce MMEval a novel evaluation metric designed to better align with human preferences for CUVA facilitating the measurement of existing LLMs in comprehending the underlying cause and corresponding effect of video anomalies.
Understanding Generalized Label Smoothing when Learning with Noisy Labels
It was shown that LS serves as a regularizer for training data with hard labels and therefore improves the generalization of the model.
Ranked #16 on
Learning with noisy labels
on CIFAR-10N-Worst
To Smooth or Not? When Label Smoothing Meets Noisy Labels
We provide understandings for the properties of LS and NLS when learning with noisy labels.
Ranked #10 on
Learning with noisy labels
on CIFAR-10N-Random3
