Sparse Reasoning is Enough: Biological-Inspired Framework for Video Anomaly Detection with Large Pre-trained Models

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.

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.

The introduction of SaFeR-CLIP marks a significant advancement in enhancing the safety of vision-language models like CLIP by employing a proximity-aware approach to redirect unsafe concepts to semantically similar safe alternatives. This method minimizes representational changes while improving zero-shot accuracy by up to 8.0% compared to previous techniques.