UniSER: A Foundation Model for Unified Soft Effects Removal

A new study introduces a scene graph-guided generative AI framework aimed at synthesizing realistic images of industrial hazard scenarios. This framework addresses the challenge of acquiring datasets for workplace hazards, which are difficult to capture in real-time. By analyzing historical Occupational Safety and Health Administration (OSHA) accident reports with GPT-4o, the study extracts structured hazard reasoning and creates object-level scene graphs. These graphs are utilized to guide a text-to-image diffusion model, generating accurate hazard scenes for evaluation.

This study evaluates the effectiveness of various large language models (LLMs) in restoring diacritics in Romanian texts, a crucial task for text processing in languages with rich diacritical marks. The models tested include OpenAI's GPT-3.5, GPT-4, Google's Gemini 1.0 Pro, and Meta's Llama family, among others. Results indicate that GPT-4o achieves high accuracy in diacritic restoration, outperforming a neutral baseline, while other models show variability. The findings emphasize the importance of model architecture, training data, and prompt design in enhancing natural language processing to…

This study explores the use of Large Language Models (LLMs), specifically GPT-4o, for evaluating short-answer quizzes and project reports in an undergraduate Computational Linguistics course. The research involved approximately 50 students and 14 project teams, comparing LLM-generated scores with human evaluations from teaching assistants. Results indicated a strong correlation between LLM and human scores, achieving up to 0.98 correlation and exact score agreement in 55% of quiz cases, while showing variability in scoring open-ended responses.

CAR-Scenes is a frame-level dataset designed for autonomous driving, facilitating the training and evaluation of vision-language models (VLMs) for scene-level understanding. The dataset comprises 5,192 annotated images from sources like Argoverse, Cityscapes, KITTI, and nuScenes, utilizing a comprehensive 28-key category/sub-category knowledge base. The annotations are generated through a GPT-4o-assisted pipeline with human verification, providing detailed attributes and supporting semantic retrieval and risk-aware scenario mining.

Multimodal Large Language Models (MLLMs) have demonstrated significant cross-modal capabilities but continue to struggle with hallucinations. To address this issue, VBackChecker has been introduced as a reference-free hallucination detection framework. This framework verifies the consistency of MLLM-generated responses with visual inputs using a pixel-level Grounding LLM that incorporates reasoning and segmentation capabilities. Additionally, a new pipeline for generating instruction-tuning data, R-Instruct, has been developed, enhancing interpretability and handling rich-context scenarios eff…

Visual Language Models (VLMs) have shown significant advancements, yet their reliability in response to minor, non-altering input changes is not well understood. A comprehensive study reveals that modern VLMs, including models like GPT-4o and Gemini 2.0 Flash, exhibit high sensitivity to small visual and textual perturbations. These perturbations include pixel-level shifts, geometric transformations, and paraphrasing that maintain the original semantics. The findings indicate that a notable portion of the samples alters their predicted answers due to these minor modifications.