SafeR-CLIP: Mitigating NSFW Content in Vision-Language Models While Preserving Pre-Trained Knowledge

The recent introduction of the Semantic-enhanced CLIP (SeeCLIP) framework addresses the challenges of Open-Set Domain Generalization (OSDG), particularly the risks associated with distinguishing known and unknown classes in vision-language models. SeeCLIP enhances semantic understanding by decomposing images into detailed semantic tokens, improving model performance in recognizing novel object categories amidst domain shifts.

A novel framework named ReCoVAD has been proposed for video anomaly detection (VAD), inspired by the human nervous system's dual pathways. This framework allows for selective frame processing, significantly reducing computational costs associated with dense frame-level inference. The approach leverages large pre-trained models, enhancing VAD's efficiency in applications such as security surveillance and autonomous driving.

SpatialGeo has been introduced as a novel vision encoder that enhances the spatial reasoning capabilities of multimodal large language models (MLLMs) by integrating geometry and semantics features. This advancement addresses the limitations of existing MLLMs, particularly in interpreting spatial arrangements in three-dimensional space, which has been a significant challenge in the field.

A new method called Augmentation-Based Test-Time Adversarial Correction (ATAC) has been proposed to enhance the robustness of the CLIP model against adversarial perturbations in images. This approach operates in the embedding space of CLIP, utilizing augmentation-induced drift vectors to correct embeddings based on angular consistency. The method has shown to outperform previous state-of-the-art techniques by nearly 50% in robustness across various benchmarks.

A new framework named MindShot has been introduced to enhance brain decoding by reconstructing visual stimuli from brain signals, addressing challenges like individual differences and high data collection costs. This two-stage framework includes a Multi-Subject Pretraining (MSP) stage and a Fourier-based cross-subject Knowledge Distillation (FKD) stage, aiming to improve adaptability for clinical applications.