Search Results for author:
Found 10 papers, 7 papers with code
CoIN: A Benchmark of Continual Instruction tuNing for Multimodel Large Language Model
To this end, we introduce MoELoRA to MLLMs which is effective to retain the previous instruction alignment.
MotionZero:Exploiting Motion Priors for Zero-shot Text-to-Video Generation
To tackle the two issues, we propose a prompt-adaptive and disentangled motion control strategy coined as MotionZero, which derives motion priors from prompts of different objects by Large-Language-Models and accordingly applies motion control of different objects to corresponding regions in disentanglement.
BatchNorm-based Weakly Supervised Video Anomaly Detection
In the proposed BN-WVAD, we leverage the Divergence of Feature from Mean vector (DFM) of BatchNorm as a reliable abnormality criterion to discern potential abnormal snippets in abnormal videos.
Anomaly Detection In Surveillance Videos
Video Anomaly Detection
CUCL: Codebook for Unsupervised Continual Learning
To address this issue, after analyzing the phenomenon and identifying the lack of diversity as a vital factor, we propose a method named Codebook for Unsupervised Continual Learning (CUCL) which promotes the model to learn discriminative features to complete the class boundary.
ACT-Diffusion: Efficient Adversarial Consistency Training for One-step Diffusion Models
Though diffusion models excel in image generation, their step-by-step denoising leads to slow generation speeds.
From Global to Local: Multi-scale Out-of-distribution Detection
Out-of-distribution (OOD) detection aims to detect "unknown" data whose labels have not been seen during the in-distribution (ID) training process.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
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ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognition
To overcome this bottleneck, we leverage class priors to restrict the generalization scope of the class-agnostic SAM and propose a class-aware smoothness optimization algorithm named Imbalanced-SAM (ImbSAM).
Semi-supervised Anomaly Detection
Supervised Anomaly Detection
An Efficient Membership Inference Attack for the Diffusion Model by Proximal Initialization
Therefore, we also explore the robustness of diffusion models to MIA in the text-to-speech (TTS) task, which is an audio generation task.
DETA: Denoised Task Adaptation for Few-Shot Learning
Test-time task adaptation in few-shot learning aims to adapt a pre-trained task-agnostic model for capturing taskspecific knowledge of the test task, rely only on few-labeled support samples.
Revisiting Multi-Codebook Quantization
However, finding the optimal solution to MCQ is proved to be NP-hard due to its encoding process, \textit{i. e.}, converting an input vector to a binary code.
