RelationVLM: Making Large Vision-Language Models Understand Visual Relations
- URL: http://arxiv.org/abs/2403.12801v1
- Date: Tue, 19 Mar 2024 15:01:19 GMT
- Title: RelationVLM: Making Large Vision-Language Models Understand Visual Relations
- Authors: Zhipeng Huang, Zhizheng Zhang, Zheng-Jun Zha, Yan Lu, Baining Guo,
- Abstract summary: We present RelationVLM, a large vision-language model capable of comprehending various levels and types of relations whether across multiple images or within a video.
Specifically, we devise a multi-stage relation-aware training scheme and a series of corresponding data configuration strategies to bestow RelationVLM with the capabilities of understanding semantic relations.
- Score: 66.70252936043688
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The development of Large Vision-Language Models (LVLMs) is striving to catch up with the success of Large Language Models (LLMs), yet it faces more challenges to be resolved. Very recent works enable LVLMs to localize object-level visual contents and ground text to them. Nonetheless, current LVLMs still struggle to precisely understand visual relations due to the lack of relevant data. In this work, we present RelationVLM, a large vision-language model capable of comprehending various levels and types of relations whether across multiple images or within a video. Specifically, we devise a multi-stage relation-aware training scheme and a series of corresponding data configuration strategies to bestow RelationVLM with the capabilities of understanding semantic relations, temporal associations and geometric transforms. Extensive case studies and quantitative evaluations show RelationVLM has strong capability in understanding such relations and emerges impressive in-context capability of reasoning from few-shot examples by comparison. This work fosters the advancements of LVLMs by enabling them to support a wider range of downstream applications toward artificial general intelligence.
Related papers
- In-Context Learning Improves Compositional Understanding of Vision-Language Models [2.762909189433944]
compositional image understanding remains a rather difficult task due to the object bias present in training data.
We compare contrastive models with generative ones and analyze their differences in architecture, pre-training data, and training tasks and losses.
Our proposed approach outperforms baseline models across multiple compositional understanding datasets.
arXiv Detail & Related papers (2024-07-22T09:03:29Z) - Large Vision-Language Models as Emotion Recognizers in Context Awareness [14.85890824622433]
Context-aware emotion recognition (CAER) is a complex and significant task that requires perceiving emotions from various contextual cues.
Previous approaches primarily focus on designing sophisticated architectures to extract emotional cues from images.
This paper systematically explore the potential of leveraging Large Vision-Language Models (LVLMs) to empower the CAER task.
arXiv Detail & Related papers (2024-07-16T01:28:06Z) - Rethinking Visual Prompting for Multimodal Large Language Models with External Knowledge [76.45868419402265]
multimodal large language models (MLLMs) have made significant strides by training on vast high-quality image-text datasets.
However, the inherent difficulty in explicitly conveying fine-grained or spatially dense information in text, such as masks, poses a challenge for MLLMs.
This paper proposes a new visual prompt approach to integrate fine-grained external knowledge, gleaned from specialized vision models, into MLLMs.
arXiv Detail & Related papers (2024-07-05T17:43:30Z) - Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language Models [81.71651422951074]
Chain-of-Spot (CoS) method is a novel approach that enhances feature extraction by focusing on key regions of interest.
This technique allows LVLMs to access more detailed visual information without altering the original image resolution.
Our empirical findings demonstrate a significant improvement in LVLMs' ability to understand and reason about visual content.
arXiv Detail & Related papers (2024-03-19T17:59:52Z) - CODIS: Benchmarking Context-Dependent Visual Comprehension for Multimodal Large Language Models [58.95889895912716]
We introduce a new benchmark, named as CODIS, designed to assess the ability of models to use context provided in free-form text to enhance visual comprehension.
Our findings indicate that MLLMs consistently fall short of human performance on this benchmark.
This underscores the pressing need to enhance the ability of MLLMs to comprehend visuals in a context-dependent manner.
arXiv Detail & Related papers (2024-02-21T08:21:12Z) - Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions [11.786387517781328]
Vision-Language Models (VLMs) are advanced models that can tackle more intricate tasks such as image captioning and visual question answering.
Our classification organizes VLMs into three distinct categories: models dedicated to vision-language understanding, models that process multimodal inputs to generate unimodal (textual) outputs and models that both accept and produce multimodal inputs and outputs.
We meticulously dissect each model, offering an extensive analysis of its foundational architecture, training data sources, as well as its strengths and limitations wherever possible.
arXiv Detail & Related papers (2024-02-20T18:57:34Z) - CoVLM: Composing Visual Entities and Relationships in Large Language
Models Via Communicative Decoding [66.52659447360104]
CoVLM can guide the LLM to explicitly compose visual entities and relationships among the text.
We propose CoVLM, which can guide the LLM to explicitly compose visual entities and relationships among the text.
arXiv Detail & Related papers (2023-11-06T18:59:44Z) - Large Language Models are Visual Reasoning Coordinators [144.67558375045755]
We propose a novel paradigm that coordinates multiple vision-language models for visual reasoning.
We show that our instruction tuning variant, Cola-FT, achieves state-of-the-art performance on visual question answering.
We also show that our in-context learning variant, Cola-Zero, exhibits competitive performance in zero and few-shot settings.
arXiv Detail & Related papers (2023-10-23T17:59:31Z) - Link-Context Learning for Multimodal LLMs [40.923816691928536]
Link-context learning (LCL) emphasizes "reasoning from cause and effect" to augment the learning capabilities of MLLMs.
LCL guides the model to discern not only the analogy but also the underlying causal associations between data points.
To facilitate the evaluation of this novel approach, we introduce the ISEKAI dataset.
arXiv Detail & Related papers (2023-08-15T17:33:24Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.