Towards Effective and Efficient Long Video Understanding of Multimodal Large Language Models via One-shot Clip Retrieval

A recent study highlights a critical flaw in Multimodal Large Language Models (MLLMs) that stems from the Pre-Norm architecture, which creates a significant norm disparity between high-norm visual tokens and low-norm text tokens. This imbalance leads to slower semantic transformations of visual tokens compared to text, resulting in visual information loss during cross-modal feature fusion.

Recent advancements in Multimodal Large Language Models (MLLMs) have led to the development of See-Control, a framework designed for smartphone interaction with a robotic arm. This framework introduces the Embodied Smartphone Operation (ESO) task, allowing for platform-agnostic smartphone operation through direct physical interaction, bypassing the limitations of the Android Debug Bridge (ADB). See-Control includes an ESO benchmark, an MLLM-based agent, and a dataset of operation episodes.

A recent study has introduced a method called nlg2choice, aimed at enhancing the capabilities of Multimodal Large Language Models (MLLMs) in Fine-Grained Visual Classification (FGVC). This approach addresses the challenges of evaluating free-form responses in auto-regressive models, particularly in settings with extensive multiple-choice options where traditional methods fall short.

A recent study explores sound symbolism, revealing how Multimodal Large Language Models (MLLMs) interpret auditory information in human languages. The research introduces LEX-ICON, a dataset comprising 8,052 words and 2,930 pseudo-words across four languages, examining MLLMs' phonetic iconicity through phoneme-level attention scores.

SimSUM has been introduced as a benchmark dataset comprising 10,000 simulated patient records that connect unstructured clinical notes with structured background variables, specifically in the context of respiratory diseases. The dataset aims to enhance clinical information extraction by incorporating tabular data generated from a Bayesian network, with clinical notes produced by a large language model, GPT-4o.

MiniGPT-5 has been introduced as a novel interleaved vision-and-language generation model that utilizes generative vokens to enhance the coherence of image-text outputs. This model employs a two-stage training strategy that allows for description-free multimodal generation, significantly improving performance on datasets like MMDialog and VIST.

A new framework named Weaver has been introduced to enhance the performance of language model verifiers by combining multiple weak verifiers into a stronger ensemble. This approach addresses the existing performance gap between general-purpose verifiers and oracle verifiers, which have perfect accuracy. Weaver utilizes weak supervision to estimate the accuracy of each verifier, allowing for a more reliable scoring of generated responses.

Recent research highlights significant shortcomings in Multimodal Large Language Models (MLLMs) regarding their ability to interpret diagrams, which are crucial for understanding abstract concepts and relationships. The study reveals that MLLMs struggle with basic perceptual tasks, exhibiting near-zero accuracy in fine-grained grounding and object identification.