FairJudge: MLLM Judging for Social Attributes and Prompt Image Alignment

The paper presents an automated framework for detecting visual attribute reliance in trained vision models. It introduces a self-reflective agent that generates and tests hypotheses about the visual attributes influencing model predictions. This iterative process allows the agent to refine its hypotheses based on experimental results and assess the accuracy of its findings, ensuring model robustness and preventing overfitting.

The paper presents an Unbiased Semantic Decoding (USD) strategy integrated with the Segment Anything Model (SAM) for few-shot segmentation tasks. This approach aims to enhance the model's generalization ability by extracting target information from both support and query sets simultaneously, addressing the limitations of previous methods that relied heavily on explicit prompts. The study highlights the potential of USD in improving segmentation accuracy across unknown classes.

Data-Free Quantization (DFQ) presents a solution for model compression without needing real data, which is beneficial in privacy-sensitive contexts. While DFQ has been effective for unimodal models, its application to Vision-Language Models like CLIP has not been thoroughly investigated. This study introduces D4C, a DFQ framework specifically designed for CLIP, addressing challenges such as semantic content and intra-image diversity in synthesized samples.

The article discusses the limitations of the CLIP model in capturing fine-grained details in remote sensing (RS) data. It highlights two main issues: the underutilization of object-level supervision in RS image-text datasets and the performance degradation of region-text alignment methods when applied to RS data. To address these challenges, the authors introduce the MGRS-200k dataset, which provides rich object-level textual supervision for improved RS region-category alignment.

The paper presents HASTEN (Hierarchical Semantic Tree Anchoring), a novel approach for Class-Incremental Learning (CIL) that integrates hierarchical information to mitigate catastrophic forgetting. It leverages external knowledge graphs to enhance the learning of visual and textual features, addressing the limitations of existing CLIP-based CIL methods that fail to capture inherent hierarchies in visual and linguistic concepts.

The paper presents a hybrid multimodal deep learning framework designed for intelligent fashion recommendation, addressing outfit compatibility prediction and complementary item retrieval. Utilizing the CLIP architecture, the model integrates visual and textual encoders to create joint representations of fashion items. It achieves a high AUC of 0.95 for compatibility prediction on the Polyvore dataset and an accuracy of 69.24% for retrieving compatible items based on a target item description.

The paper titled 'HV-Attack: Hierarchical Visual Attack for Multimodal Retrieval Augmented Generation' discusses a new method to exploit vulnerabilities in multimodal Retrieval-Augmented Generation (MRAG) systems. It highlights how imperceptible perturbations to image inputs can misalign and disrupt the generation process, posing significant safety concerns for Large Multimodal Models (LMMs). This research addresses the challenge of robustness in MRAG systems against such visual attacks.

A recent study examines the gender and racial biases present in multilingual vision-language models (VLMs) such as M-CLIP, NLLB-CLIP, CAPIVARA-CLIP, and SigLIP-2. Despite the expectation that multilinguality would reduce bias, findings reveal that all models exhibit stronger gender skew compared to English-only baselines. Notably, CAPIVARA-CLIP shows significant biases in low-resource languages, while NLLB-CLIP and SigLIP-2 transfer English stereotypes into gender-neutral languages.