"I Can See Forever!": Evaluating Real-time VideoLLMs for Assisting Individuals with Visual Impairments

The introduction of Test-Time Reinforcement Learning (TTRV) aims to enhance vision language models by adapting them during inference without relying on labeled data. This method builds upon the Group Relative Policy Optimization (GRPO) framework, optimizing rewards based on output frequency and controlling output diversity through low entropy rewards. The approach has shown significant improvements in object recognition and visual question answering, with gains of up to 52.4% and 29.8%, respectively.

A study evaluated the effectiveness of AI-generated images in aiding self-expression of mental distress among twenty Chinese international students in the UK. Participants described their experiences, which were then transformed into images using GPT-4o, and assessed the images' helpfulness in expressing their feelings. The dataset created includes 100 descriptions and 400 generated images.

A novel self-supervised symbolic auto-encoder has been introduced, enabling symbolic computer vision to interpret diagrams through structured representations and logical rules. This approach contrasts with traditional pixel-based visual models by parsing diagrams into geometric primitives, enhancing machine vision's interpretability.

A comparative study has been conducted on the object counting capabilities of two multi-modal large language models, GPT-4o and GPT-5, focusing on their performance in zero-shot scenarios using only textual prompts. The evaluation was carried out on the FSC-147 and CARPK datasets, revealing that both models achieved results comparable to state-of-the-art methods, with some instances exceeding them.

A new framework called 'Look, Recite, Then Answer' has been proposed to enhance the performance of Vision-Language Models (VLMs) by generating self-generated knowledge hints. This approach aims to address the limitations of VLMs in specialized fields like precision agriculture, where reasoning-driven hallucination can hinder accurate visual perception.

The introduction of DIQ-H marks a significant advancement in evaluating the robustness of Vision-Language Models (VLMs) under conditions of temporal visual degradation, addressing critical failure modes such as hallucination persistence. This benchmark applies various physics-based corruptions to assess how VLMs recover from errors across multiple frames in dynamic environments.

A new method for Scene Graph Anticipation (SGA) has been introduced, termed Linguistic Scene Graph Anticipation (LSGA), which utilizes a language-driven framework to enhance the prediction of future scene graphs from video clips. This approach aims to improve the understanding of dynamic scenes by integrating semantic dynamics and commonsense temporal regularities, which are often difficult to extract from visual features alone.

The introduction of SpatialReasoner marks a significant advancement in spatial reasoning for large-scale 3D environments, addressing challenges faced by existing vision-language models that are limited to smaller, room-scale scenarios. This framework utilizes the H$^2$U3D dataset, which encompasses multi-floor environments and generates diverse question-answer pairs to enhance 3D scene understanding.