Yonatan Bitton
Yonatan Bitton is a Research Scientist at Google Tel Aviv, working on vision-and-language generalization and multimodal consistency.
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RefVNLI: Towards Scalable Evaluation of Subject-driven Text-to-image Generation
Aviv Slobodkin
Hagai Taitelbaum
Brian Gordon
Michal Sokolik
Almog Gueta
Royi Rassin
Dani Lischinski
2025
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Subject-driven text-to-image (T2I) generation aims to produce images that align with a given textual description, while preserving the visual identity from a referenced subject image. Despite its broad downstream applicability - ranging from enhanced personalization in image generation to consistent character representation in video rendering - progress in this field is limited by the lack of reliable automatic evaluation. Existing methods either assess only one aspect of the task (i.e., textual alignment or subject preservation), misalign with human judgments, or rely on costly API-based evaluation. To address this gap, we introduce RefVNLI, a cost-effective metric that evaluates both textual alignment and subject preservation in a single run. Trained on a large-scale dataset derived from video-reasoning benchmarks and image perturbations, RefVNLI outperforms or statistically matches existing baselines across multiple benchmarks and subject categories (e.g., Animal, Object), achieving up to 6.4-point gains in textual alignment and 5.9-point gains in subject preservation.
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Large-scale video generative models, capable of creating realistic videos of diverse visual concepts, are strong candidates for general-purpose physical world simulators. However, their adherence to physical commonsense across real-world actions remains unclear (e.g., playing tennis, backflip). Existing benchmarks suffer from limitations such as limited size, lack of human evaluation, sim-to-real gaps, and absence of fine-grained physical rule analysis. To address this, we introduce VideoPhy-2, an action-centric dataset for evaluating physical commonsense in generated videos. We curate 200 diverse actions and detailed prompts for video synthesis from modern generative models. We perform human evaluation that assesses semantic adherence, physical commonsense, and grounding of physical rules in the generated videos. Our findings reveal major shortcomings, with even the best model achieving only 22% joint performance (i.e., high semantic and physical commonsense adherence) on the hard subset of VideoPhy-2. We find that the models particularly struggle with conservation laws like mass and momentum. Finally, we also train VideoPhy-AutoEval, an automatic evaluator for fast, reliable assessment on our dataset. Overall, VideoPhy-2 serves as a rigorous benchmark, exposing critical gaps in video generative models and guiding future research in physically-grounded video generation. The data and code is available at https://videophy2.github.io/
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A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains
Alon Jacovi
Or Honovich
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (2024), pp. 4615–4634
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Prompting language models to provide step-by-step answers (e.g., “Chain-of-Thought”) is the prominent approach for complex reasoning tasks, where more accurate reasoning chains typically improve downstream task performance. Recent literature discusses automatic methods to verify reasoning to evaluate and improve their correctness. However, no fine-grained step-level datasets are available to enable thorough evaluation of such verification methods, hindering progress in this direction. We introduce REVEAL: Reasoning Verification Evaluation, a dataset to benchmark automatic verifiers of complex Chain-of-Thought reasoning in open-domain question-answering settings. REVEAL includes comprehensive labels for the relevance, attribution to evidence passages, and logical correctness of each reasoning step in a language model’s answer, across a variety of datasets and state-of-the-art language models. Evaluation on REVEAL shows that verifiers struggle at verifying reasoning chains — in particular, verifying logical correctness and detecting contradictions. Available at https://reveal-dataset.github.io/.
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DOCCI: Descriptions of Connected and Contrasting Images
Garrett Tanzer
Jaemin Cho
Su Wang
Sunayana Rane
Zack Berger
Zarana Parekh
(2024)
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Despite recent advancements, text-to-image (T2I) models still exhibit critical limitations, such as errors in understanding spatial relationships, object counting, text rendering, and more. One challenge in overcoming these failure modes is the lack of resources; the majority of existing image-text datasets provide only brief captions that do not offer sufficient detail to discrepancies between images and their descriptions. To advance the development of T2I models further, we introduce \textbf{Descriptions of Connected and Contrasting Images (DOCCI)}, a dataset of 15k images taken by a single person with detailed human-annotated descriptions in English. We meticulously annotated detailed and coherent descriptions, averaging 136 words, which sufficiently differentiate images from related or similar ones. We intentionally curated images that showcase a diverse range of visual properties, including entities with their attributes, various orientations, and lighting effects, many of which are related to each other. We thoroughly analyze the quality and characteristics of the image-description pairs, and assess the performance of the latest T2I and I2T models. The experimental results indicate that the current state-of-the-art T2I models still struggle with the aforementioned challenges, and even the SOTA models have not fully addressed them. DOCCI is publicly available, and we believe that this dataset will be a valuable benchmark for vision-language research.
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ImageInWords: Unlocking Hyper-Detailed Image Descriptions
Andrew Bunner
Ranjay Krishna
(2024)
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Despite the longstanding adage "an image is worth a thousand words," creating accurate and hyper-detailed image descriptions for training Vision-Language models remains challenging.
Current datasets typically have web-scraped descriptions that are short, low-granularity, and often contain details unrelated to the visual content. As a result, models trained on such data generate descriptions replete with missing information, visual inconsistencies, and hallucinations. To address these issues, we introduce ImageInWords (IIW), a carefully designed human-in-the-loop annotation framework for curating hyper-detailed image descriptions and a new dataset resulting from this process.
We validate the framework through evaluations focused on the quality of the dataset and its utility for fine-tuning with considerations for readability, comprehensiveness, specificity, hallucinations, and human-likeness. Our dataset significantly improves across these dimensions compared to recently released datasets (+66%) and GPT-4V outputs (+48%). Furthermore, models fine-tuned with IIW data excel by +31% against prior work along the same human evaluation dimensions. Given our fine-tuned models, we also evaluate text-to-image generation and vision-language reasoning. Our model's descriptions can generate images closest to the original, as judged by both automated and human metrics. We also find our model produces more compositionally rich descriptions, outperforming the best baseline by up to 6% on ARO, SVO-Probes, and Winoground datasets.
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One of the exciting capabilities of recent language models for dialog is their ability to independently search for relevant information to ground a given dialog response. However, obtaining training data to teach models how to issue search queries is time and resource consuming.
In this work, we propose q2d: an automatic data generation pipeline that generates information-seeking dialogs from questions. We prompt a large language model (PaLM) to create conversational versions of question answering datasets, and use it to improve query generation models that communicate with external search APIs to ground dialog responses. Unlike previous approaches which relied on human written dialogs with search queries, our method allows to automatically generate query-based grounded dialogs with better control and scale.
Our experiments demonstrate that: (1) For query generation on the QReCC dataset, models trained on our synthetically-generated data achieve 90%--97% of the performance of models trained on the human-generated data; (2) We can successfully generate data for training dialog models in new domains without any existing dialog data as demonstrated on the multi-hop MuSiQue and Bamboogle QA datasets. (3) We perform a thorough analysis of the generated dialogs showing that humans find them of high quality and struggle to distinguish them from human-written dialogs.
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Mismatch Quest: Visual and Textual Feedback for Image-Text Misalignment
Brian Gordon
Dani Lischinski
Daniel Cohen-Or
Xi Chen
arXiv (2023)
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While existing image/text alignment models reach high quality binary assessments, they fall short of pinpointing the exact source of misalignment.
In this paper, we present a method to provide detailed textual and visual explanation of detected misalignments between text/image pairs.
We leverage large language models to automatically construct a training set that holds plausible misaligned captions for a given image and corresponding textual explanations and visual indicators. We also introduce a new human curated test set comprising ground-truth textual and visual misalignment annotations. Empirical results show that fine-tuning vision language models on our training set enables them to articulate misalignments and visually indicate them within images, outperforming strong baselines both on the binary alignment classification and the explanation generation tasks.
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q2d: Automatic Dialog Generation to Improve Models' Query Generation
Enav Weinreb
Ido Hakimi
Shlomi Cohen-Ganor
Yoad Lewenberg
EMNLP 2023 (2023)
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We propose q2d: an automatic data generation pipeline that generates information-seeking dialogues based on questions. We apply our method to create conversational versions of questions answering datasets, which we release as a new dataset. We use this data to improve query generation models, which communicate with an external search APIs to generate factual responses. Unlike previous approaches, which relied on human annotators, our method allows to automatically generate labeled dialogues with better control and scale.
In experiments, we demonstrate that: (1) Models trained on our synthetic data produce results comparable to those trained on natural data; (2) Our generated datasets are effective as a benchmark and as a training signal that generalizes to human-annotated test sets.
We also provide an extensive analysis of the quality and factuality of the generated datasets. Our studies indicate that our automatic dialogue generation pipeline is effective at improving query generation and factuality.
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What You See is What You Read? Improving Text-Image Alignment Evaluation
Michal Yarom
Eran Ofek
arXiv (2023)
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Automatically determining whether a text and a corresponding image are semantically aligned is a significant challenge for vision-language models, with applications in generative text-to-image and image-to-text tasks. In this work, we study methods for automatic image-text alignment evaluation. We first introduce a comprehensive evaluation set spanning multiple datasets from both text-to-image and image-to-text generation tasks, with human judgements for whether a given text-image pair is semantically aligned. We then describe two automatic methods to determine alignment: the first involving a pipeline based on question generation and visual question answering models, and the second employing an end-to-end classification approach based on synthetic data generation. Both methods surpass prior approaches in various text-image alignment tasks, with our analysis showing significant improvements in challenging cases that involve complex composition or unnatural images. Finally, we demonstrate how our approaches can localize specific misalignments between an image and a given text, and how they can be used to automatically re-rank candidates in text-to-image generation.
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The alignment of diverse data modalities, especially video and text, is a significant challenge in AI. This study introduces VideoCon, a novel dataset for robust video-language alignment evaluation. It provides contrast captions for originally matched video-captions, complemented with natural language explanations (NLEs) that delineate the differences between the video and the contrast captions. Notably, VideoCon emphasizes temporally challenging scenarios to enhance the robustness of evaluations. To address misalignments observed in previous models, we propose AlignVideo, a video-language model trained on VideoCon that demonstrates enhanced alignment capabilities. Experiments reveal that AlignVideo surpasses existing baselines in video-text alignment and generates more precise NLEs. Moreover, it showcases state-of-the-art performance in zero-shot downstream tasks, emphasizing complex video understanding, such as action recognition and temporal event sequencing. Our work paves the way for advancements in video-text alignment evaluation and model development.
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