Elman, Dive into the Latest Advancements in Multimodal Image and Text Foundation Models
This newsletter explores recent breakthroughs and challenges in the rapidly evolving field of multimodal image and text foundation models. We'll delve into new benchmarks designed to assess these models' capabilities in complex real-world scenarios, novel training approaches inspired by human cognitive development, and frameworks for tackling the inherent uncertainties in multimodal robot planning. We'll also examine how these models grapple with fundamental cognitive challenges like the binding problem and explore innovative techniques for enhancing their understanding of lengthy, visually-rich documents.
Evaluating LMMs in Real-World Engineering with EEE-Bench
EEE-Bench: A Comprehensive Multimodal Electrical And Electronics Engineering Benchmark by Ming Li, Jike Zhong, Tianle Chen, Yuxiang Lai, Konstantinos Psounis https://arxiv.org/abs/2411.01492
Caption: This radar chart visualizes the performance of various large language and multimodal models (LLMs and LMMs) across 10 electrical and electronics engineering (EEE) subdomains within the EEE-Bench benchmark. The results highlight the models' struggles with visually complex EEE problems, with even the top performer, GPT40, achieving less than 50% accuracy overall. This underscores the need for improved visual understanding and reasoning capabilities in LMMs for real-world engineering applications.
Large language models (LLMs) and their multimodal counterparts (LMMs) have demonstrated impressive capabilities across various domains. However, their proficiency in complex, real-world engineering tasks remained largely uncharted territory. This paper introduces EEE-Bench, a comprehensive multimodal benchmark specifically designed to evaluate these models' reasoning capabilities within the challenging field of electrical and electronics engineering (EEE).
EEE-Bench comprises 2,860 meticulously curated multiple-choice and free-form questions spanning 10 essential EEE subdomains, ranging from digital logic circuits to electromagnetics. Unlike benchmarks in other fields, EEE-Bench emphasizes visually complex and versatile problems that often have less deterministic solutions. This characteristic demands a deeper integration of visual and textual information for successful problem-solving, making it an ideal testing ground for LMMs.
The researchers evaluated 17 widely used LLMs and LMMs, both open-source and closed-source, on EEE-Bench. The results revealed significant shortcomings. Average performance ranged from a mere 19.48% to 46.78% accuracy, with GPT-40 achieving the highest overall score. While closed-source models generally outperformed their open-source counterparts, even the best-performing models struggled with subjects involving complex visual diagrams, such as circuit theory. This suggests that current LMMs, despite proficiency in numerical computation, lack the necessary visual understanding and reasoning skills for complex engineering tasks.
Further investigation uncovered a surprising "laziness" phenomenon: when presented with spurious captions contradicting the accompanying images, the models often disregarded the visual information and relied solely on the misleading text. This over-reliance on text, even when flagged as potentially misleading, highlights a critical area for improvement in LMM development. A detailed error analysis, using GPT-40 as a case study, attributed over 50% of errors to reasoning issues and 26.5% to image perception errors. EEE-Bench provides a valuable resource for future research, driving progress towards LMMs capable of tackling real-world engineering challenges.
TaxaBind: Unifying Ecological Data with Multimodal Embeddings
TaxaBind: A Unified Embedding Space for Ecological Applications by Srikumar Sastry, Subash Khanal, Aayush Dhakal, Adeel Ahmad, Nathan Jacobs https://arxiv.org/abs/2411.00683
Caption: This diagram illustrates the TaxaBind framework, which integrates six ecological data modalities (ground-level imagery, geographic location, satellite imagery, taxonomic text, audio, and environmental features) into a unified embedding space. Using ground-level imagery as a binding modality and a novel multimodal patching technique, TaxaBind enables cross-modal knowledge transfer and facilitates various ecological applications, such as species distribution mapping and audio classification.
Ecologists frequently face the complex tasks of fine-grained species classification and distribution mapping, often requiring separate frameworks and datasets. This paper introduces TaxaBind, a unified embedding space integrating six modalities – ground-level images, geographic location, satellite imagery, text, audio, and environmental features – to address these ecological challenges. Using ground-level images as a binding modality, TaxaBind learns a joint representation space, aligning all available modalities and potentially unlocking information previously siloed in disparate data sources.
Key to TaxaBind's architecture is multimodal patching, a novel technique distilling knowledge from various modalities into the binding modality. Extending existing patching techniques beyond two modalities, it addresses limitations of frameworks like ImageBind. Multimodal patching involves three steps: locked tuning of a modality-specific encoder using the binding modality encoder as a teacher, full fine-tuning of both encoders, and patching by linearly interpolating the weights between the locked and fine-tuned versions of the modality-specific encoder. The interpolation weights are optimized based on a patching task, such as zero-shot classification with text.
The researchers constructed two large datasets, iSatNat (species images paired with satellite imagery) and iSoundNat (species images paired with audio), along with TaxaBench-8k, a diverse multimodal dataset with six paired modalities for benchmarking. Experiments demonstrated TaxaBind's strong zero-shot capabilities and emergent properties across several tasks. In zero-shot image classification, TaxaBind outperformed baselines on four out of five datasets. It also excelled in cross-modal retrieval tasks, surpassing both random baselines and ImageBind. TaxaBind's audio encoder proved effective in bird species audio classification, while the location encoder demonstrated its ability to reason about ecological traits. The satellite image encoder also showed strong performance in predicting bird species encounter rates. These results highlight TaxaBind's potential as a powerful tool for various ecological applications.
Exploring Analogical Reasoning in Multimodal LLMs
Can Multimodal Large Language Model Think Analogically? by Diandian Guo, Cong Cao, Fangfang Yuan, Dakui Wang, Wei Ma, Yanbing Liu, Jianhui Fu https://arxiv.org/abs/2411.01307
Caption: This image illustrates the framework for evaluating multimodal analogical reasoning in MLLMs. On the left, example analogy questions with image and text inputs are shown. These are transformed into unified prompts for MPT models (center), which are then processed by an MLLM "explainer" to generate textual descriptions of the images and their relationships, ultimately predicting the missing element.
Analogical reasoning, the ability to connect disparate concepts based on relational similarities, is fundamental to human cognition. This research investigates the capacity of Multimodal Large Language Models (MLLMs) to perform analogical reasoning across modalities like images and text, exploring two perspectives: MLLM as an explainer, augmenting existing models, and MLLM as a predictor, directly solving problems.
The core task involves predicting a missing entity (e<sub>a</sub>) given an analogy example (e<sub>h</sub>, e<sub>t</sub>) and a question-answer entity pair (e<sub>q</sub>, ?), formalized as (e<sub>h</sub>, e<sub>t</sub>): (e<sub>q</sub>, ?). The authors introduce a unified prompt template T = I<sub>1</sub> I<sub>2</sub> [CLS] Then T<sub>r</sub>[R] T<sub>t<sub>et</sub></sub> [SEP] || T<sub>q<sub>eq</sub></sub> T<sub>r</sub>[R] [MASK] [SEP] for MPT models, where I represents images, T represents text, and [R] denotes the relation. The MLLM reconstructs more accurate descriptions for entities and relations. A two-step fine-tuning approach is used for the predictor framework: training on triplet information from a knowledge graph and then on the specific format of multimodal analogical reasoning tasks.
Experiments on the MARS dataset yielded promising results. The Explainer method, combined with existing MPT models like MKGformer and FLAVA, showed significant performance improvements. The Predictor framework also achieved state-of-the-art performance. Zero-shot experiments on the MBARD dataset, focusing on verb-noun analogies, indicated promising MLLM capabilities. Ablation studies confirmed the importance of each component, and error analysis revealed that some incorrect predictions were reasonable from a human perspective. This work provides compelling evidence for the inherent analogical reasoning abilities of MLLMs.
Managing Uncertainty in Multimodal Robot Planning
Know Where You're Uncertain When Planning with Multimodal Foundation Models: A Formal Framework by Neel P. Bhatt, Yunhao Yang, Rohan Siva, Daniel Milan, Ufuk Topcu, Zhangyang Wang https://arxiv.org/abs/2411.01639
Caption: This diagram illustrates a framework for robot planning under uncertainty, disentangling perception and decision uncertainties. It shows how active sensing improves visual inputs based on perception uncertainty (u<sub>p</sub>), while a foundation model generates plans evaluated for decision uncertainty (u<sub>d</sub>) before execution. This process leads to more robust and adaptable robot behavior in navigation tasks.
Multimodal foundation models are transforming robotic perception and planning. However, uncertainty in both perception (interpreting sensory inputs) and decision-making (generating plans) poses a significant challenge. This research introduces a framework to disentangle, quantify, and mitigate these two forms of uncertainty, aiming for more robust autonomous systems.
The framework separates perception uncertainty, arising from limitations in visual understanding, from decision uncertainty, related to plan robustness. Conformal prediction calibrates visual confidence, quantifying perception uncertainty. Formal-Methods-Driven Prediction (FMDP) leverages formal verification to assess the likelihood of plans satisfying task requirements, quantifying decision uncertainty as u<sub>d</sub> = ∫<sub>0</sub><sup>c<sub>n+1</sub></sup> f<sub>nc</sub>(x)dx, where c<sub>n+1</sub> is the plan's confidence score and f<sub>nc</sub> is the probability density function of nonconformity scores.
Two targeted intervention mechanisms are implemented: active sensing dynamically re-observes scenes with high perception uncertainty, and automated refinement fine-tunes the model on high-certainty data. Empirical validation in simulated and real-world robotic navigation tasks demonstrated the framework's effectiveness, reducing variability and increasing task success rates. These improvements highlight the importance of disentangling uncertainty sources for targeted interventions, enhancing robustness and reliability.
The Binding Problem: Understanding VLM Limitations
Understanding the Limits of Vision Language Models Through the Lens of the Binding Problem by Declan Campbell, Sunayana Rane, Tyler Giallanza, Nicolò De Sabbata, Kia Ghods, Amogh Joshi, Alexander Ku, Steven M. Frankland, Thomas L. Griffiths, Jonathan D. Cohen, Taylor W. Webb https://arxiv.org/abs/2411.00238
Caption: This image illustrates visual search experiments with 2D sprites and 3D objects, testing disjunctive (target has a unique feature) and conjunctive (target shares features with distractors) search. The accompanying graphs show the performance of VLMs, demonstrating a decline in accuracy in conjunctive search as the number of objects increases, suggesting a struggle with the binding problem.
Vision Language Models (VLMs) exhibit surprising failures in basic multi-object reasoning despite their impressive capabilities. This paper examines these limitations through the binding problem—the challenge of associating object features without interference. The hypothesis is that VLMs, lacking robust serial processing, suffer from representational interference.
Evaluating state-of-the-art VLMs on cognitive tasks revealed telling patterns. VLMs excelled in disjunctive visual search but struggled in conjunctive search, with accuracy decreasing as object numbers increased. In numerical estimation, VLMs displayed human-like capacity limits, struggling with larger sets, especially with low feature variability (high interference). A scene description task further confirmed that errors increased with the number of feature triplets – indicating interference. VLMs performed better in visual analogy tasks when images were decomposed, suggesting difficulty stems from processing multi-object scenes rather than understanding relations. These findings suggest VLMs struggle with the binding problem due to over-reliance on parallel processing. The presence of binding errors implies compositional representations, beneficial for generalization but creating interference potential. This tension between compositionality and interference is a fundamental challenge for both biological and artificial cognitive systems.
LoCAL: Enhancing LMMs for Long Document Understanding
LoRA-Contextualizing Adaptation of Large Multimodal Models for Long Document Understanding by Jian Chen, Ruiyi Zhang, Yufan Zhou, Tong Yu, Franck Dernoncourt, Jiuxiang Gu, Ryan A. Rossi, Changyou Chen, Tong Sun https://arxiv.org/abs/2411.01106
Caption: This diagram illustrates the LoCAL framework for understanding multi-page, visually-rich documents. It shows how an LMM encoder processes both the question and the document pages, performs similarity-based retrieval to identify the relevant evidence page, and then uses another LMM to generate the answer based on the retrieved evidence. This approach allows the LMM to efficiently handle lengthy documents without the need for external parsers.
Large multimodal models (LMMs) face challenges with complex, multi-page documents. LoCAL (LoRA-Contextualizing Adaptation of Large multimodal models) addresses this by using the LMM itself as a multimodal retriever, fetching relevant pages to answer user questions.
LoCAL employs two LMM-based modules: one for evidence retrieval and another for question answering. The retrieval module generates feature sequences for the image and question, calculating a contextualized late interaction (Col) score, SLI(Eq, Ev), to measure relevance: $SLI(Eq, Ev) = \sum_{i=1}^{n} max_{j\in{1,..,m}} e_{q_i}^T e_{v_j}$, where Eq and Ev are the feature sequences for the question and image, respectively. LoCAL uses dual LoRA adapters within a single LLM for parameter sharing. The authors also introduce LOCAL-bench, a new visually-rich document QA dataset.
Empirical evaluations demonstrated LoCAL's effectiveness in both retrieval and question answering tasks, outperforming baselines and rivaling larger proprietary models. Ablation studies highlighted the importance of hidden state layer selection for efficient retrieval. LoCAL represents a significant advancement in long-document understanding, leveraging LMMs' inherent retrieval capabilities and offering an efficient solution through its dual-adapter design.
Dreaming Out Loud: A Developmentally Inspired Approach to Training VLMs
Dreaming Out Loud: A Self-Synthesis Approach For Training Vision-Language Models With Developmentally Plausible Data by Badr AlKhamissi, Yingtian Tang, Abdülkadir Gökce, Johannes Mehrer, Martin Schrimpf https://arxiv.org/abs/2411.00828
Caption: This diagram illustrates the four-phase self-synthesis training framework for vision-language models. Each phase builds upon the previous one, starting with language skills, incorporating vision, generating synthetic data through captioning, and finally refining cognitive abilities through multi-task training. The dashed line between Phase 1 and Phase 4 represents the optional inclusion of the initial text corpus in the final training phase.
Large language models (LLMs) require massive training data. This research introduces a "self-synthesis" approach inspired by human cognitive development, training vision-language models with a developmentally plausible amount of data. The framework consists of four phases: Phase 1 establishes fundamental language skills; Phase 2 integrates vision using a frozen DINOv2Large vision encoder; Phase 3 generates synthetic captions for unlabeled images to further train the language model; and Phase 4 refines cognitive abilities through multi-task training. The training objective in Phase 2 is represented by the following formula:
max θ,φ ∑(n=1)^N ∑(s=1)^tn log p_(θ,φ)(t_(n,s+1) | [f(i_n); t_(n,1:s)])
where p_(θ,φ)(·) is the probability distribution, f(i_n) are the projected image embeddings, t_n are text tokens, N is the number of examples, and |t_n| is the length of the n-th text sequence. Evaluations showed improvements on language-only tasks and mixed results on vision-language tasks. While promising for data-efficient training, further research is needed to enhance learning efficiency. This approach represents a significant step towards building more data-efficient vision-language models.
Conclusion
This newsletter highlighted key advancements and challenges in multimodal image and text foundation models. From benchmarks like EEE-Bench revealing limitations in real-world engineering applications to innovative frameworks like TaxaBind unifying diverse ecological data, the field is rapidly progressing. The exploration of analogical reasoning capabilities, the development of robust uncertainty management in robotic planning, and the investigation of the binding problem provide valuable insights into the cognitive strengths and weaknesses of these models. Novel training approaches like the self-synthesis framework offer promising directions for data-efficient learning. While challenges remain, the research presented in this newsletter underscores the significant strides being made towards more robust, adaptable, and intelligent multimodal models.