FMM-Attack: A Flow-based Multi-modal Adversarial Attack on Video-based LLMs

Despite the remarkable performance of video-based large language models (LLMs), their adversarial threat remains unexplored. To fill this gap, we propose the first adversarial attack tailored for video-based LLMs by crafting flow-based multi-modal adversarial perturbations on a small fraction of frames within a video, dubbed FMM-Attack. Extensive experiments show that our attack can effectively induce video-based LLMs to generate incorrect answers when videos are added with imperceptible adversarial perturbations. Intriguingly, our FMM-Attack can also induce garbling in the model output, prompting video-based LLMs to hallucinate. Overall, our observations inspire a further understanding of multi-modal robustness and safety-related feature alignment across different modalities, which is of great importance for various large multi-modal models. Our code is available at https://github.com/THU-Kingmin/FMM-Attack.

MambaIR: A Simple Baseline for Image Restoration with State-Space Model

Recent years have witnessed great progress in image restoration thanks to the advancements in modern deep neural networks e.g. Convolutional Neural Network and Transformer. However, existing restoration backbones are usually limited due to the inherent local reductive bias or quadratic computational complexity. Recently, Selective Structured State Space Model e.g., Mamba, has shown great potential for long-range dependencies modeling with linear complexity, but it is still under-explored in low-level computer vision. In this work, we introduce a simple but strong benchmark model, named MambaIR, for image restoration. In detail, we propose the Residual State Space Block as the core component, which employs convolution and channel attention to enhance the capabilities of the vanilla Mamba. In this way, our MambaIR takes advantage of local patch recurrence prior as well as channel interaction to produce restoration-specific feature representation. Extensive experiments demonstrate the superiority of our method, for example, MambaIR outperforms Transformer-based baseline SwinIR by up to 0.36dB, using similar computational cost but with a global receptive field. Code is available at \url{https://github.com/csguoh/MambaIR}.

Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders

Learning 3D representation plays a critical role in masked autoencoder (MAE) based pre-training methods for point cloud, including single-modal and cross-modal based MAE. Specifically, although cross-modal MAE methods learn strong 3D representations via the auxiliary of other modal knowledge, they often suffer from heavy computational burdens and heavily rely on massive cross-modal data pairs that are often unavailable, which hinders their applications in practice. Instead, single-modal methods with solely point clouds as input are preferred in real applications due to their simplicity and efficiency. However, such methods easily suffer from limited 3D representations with global random mask input. To learn compact 3D representations, we propose a simple yet effective Point Feature Enhancement Masked Autoencoders (Point-FEMAE), which mainly consists of a global branch and a local branch to capture latent semantic features. Specifically, to learn more compact features, a share-parameter Transformer encoder is introduced to extract point features from the global and local unmasked patches obtained by global random and local block mask strategies, followed by a specific decoder to reconstruct. Meanwhile, to further enhance features in the local branch, we propose a Local Enhancement Module with local patch convolution to perceive fine-grained local context at larger scales. Our method significantly improves the pre-training efficiency compared to cross-modal alternatives, and extensive downstream experiments underscore the state-of-the-art effectiveness, particularly outperforming our baseline (Point-MAE) by 5.16%, 5.00%, and 5.04% in three variants of ScanObjectNN, respectively. The code is available at https://github.com/zyh16143998882/AAAI24-PointFEMAE.