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Found 11 papers, 6 papers with code
Mora: Enabling Generalist Video Generation via A Multi-Agent Framework
Sora is the first large-scale generalist video generation model that garnered significant attention across society.
Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models
Sora is a text-to-video generative AI model, released by OpenAI in February 2024.
MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark
Multimodal Large Language Models (MLLMs) have gained significant attention recently, showing remarkable potential in artificial general intelligence.
GUARD: Role-playing to Generate Natural-language Jailbreakings to Test Guideline Adherence of Large Language Models
Our system of different roles will leverage this knowledge graph to generate new jailbreaks, which have proved effective in inducing LLMs to generate unethical or guideline-violating responses.
AdvCheck: Characterizing Adversarial Examples via Local Gradient Checking
To address the issues, we introduce the concept of local gradient, and reveal that adversarial examples have a quite larger bound of local gradient than the benign ones.
Is Multi-Modal Necessarily Better? Robustness Evaluation of Multi-modal Fake News Detection
The proliferation of fake news and its serious negative social influence push fake news detection methods to become necessary tools for web managers.
Unleashing the Potential of Adjacent Snippets for Weakly-supervised Temporal Action Localization
C$^3$BN consists of two key ingredients: a micro data augmentation strategy that increases the diversity in-between adjacent snippets by convex combination of adjacent snippets, and a macro-micro consistency regularization that enforces the model to be invariant to the transformations~\textit{w. r. t.}
Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable Neurons
By maximizing the number of excitable neurons concerning various wrong behaviors of models, DeepSensor can generate testing examples that effectively trigger more errors due to adversarial inputs, polluted data and incomplete training.
NIP: Neuron-level Inverse Perturbation Against Adversarial Attacks
From the perspective of image feature space, some of them cannot reach satisfying results due to the shift of features.
CatchBackdoor: Backdoor Testing by Critical Trojan Neural Path Identification via Differential Fuzzing
Existing DNN testing methods are mainly designed to find incorrect corner case behaviors in adversarial settings but fail to discover the backdoors crafted by strong trojan attacks.
Salient Feature Extractor for Adversarial Defense on Deep Neural Networks
Motivated by the observation that adversarial examples are due to the non-robust feature learned from the original dataset by models, we propose the concepts of salient feature(SF) and trivial feature(TF).
