Publications

CTRL-O: Language-Controllable Object-Centric Visual Representation Learning
Aniket Rajiv Didolkar
Andrii Zadaianchuk
Rabiul Awal
Maximilian Seitzer
Efstratios Gavves
Aishwarya Agrawal
Object-centric representation learning aims to decompose visual scenes into fixed-size vectors called "slots" or "object files", where each … (see more)slot captures a distinct object. Current state-of-the-art models have shown remarkable success in object discovery, particularly in complex real-world scenes, while also generalizing well to unseen domains. However, these models suffer from a key limitation: they lack controllability. Specifically, current object-centric models learn representations based on their preconceived understanding of objects and parts, without allowing user input to guide or modify which objects are represented. Introducing controllability into object-centric models could unlock a range of useful capabilities, such as enabling models to represent scenes at variable levels of granularity based on user specification. In this work, we propose a novel approach that conditions slot representations through guided decomposition, paired with a novel contrastive learning objective, to enable user-directed control over which objects are represented. Our method achieves such controllability without any mask supervision and successfully binds to user-specified objects in complex real-world scenes.
2025-06-10
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (published)
doi.org
openreview.net
CulturalFrames: Assessing Cultural Expectation Alignment in Text-to-Image Models and Evaluation Metrics
Shravan Nayak
Mehar Bhatia
Xiaofeng Zhang
Verena Rieser
Lisa Anne Hendricks
Sjoerd van Steenkiste
Yash Goyal
Karolina Stanczak
Aishwarya Agrawal
The increasing ubiquity of text-to-image (T2I) models as tools for visual content generation raises concerns about their ability to accurate… (see more)ly represent diverse cultural contexts. In this work, we present the first study to systematically quantify the alignment of T2I models and evaluation metrics with respect to both explicit as well as implicit cultural expectations. To this end, we introduce CulturalFrames, a novel benchmark designed for rigorous human evaluation of cultural representation in visual generations. Spanning 10 countries and 5 socio-cultural domains, CulturalFrames comprises 983 prompts, 3637 corresponding images generated by 4 state-of-the-art T2I models, and over 10k detailed human annotations. We find that T2I models not only fail to meet the more challenging implicit expectations but also the less challenging explicit expectations. Across models and countries, cultural expectations are missed an average of 44% of the time. Among these failures, explicit expectations are missed at a surprisingly high average rate of 68%, while implicit expectation failures are also significant, averaging 49%. Furthermore, we demonstrate that existing T2I evaluation metrics correlate poorly with human judgments of cultural alignment, irrespective of their internal reasoning. Collectively, our findings expose critical gaps, providing actionable directions for developing more culturally informed T2I models and evaluation methodologies.
2025-06-10
ICML.cc/2025/Workshop/MoFA (poster)
doi.org
openreview.net
CulturalFrames: Assessing Cultural Expectation Alignment in Text-to-Image Models and Evaluation Metrics
Shravan Nayak
Mehar Bhatia
Xiaofeng Zhang
Verena Rieser
Lisa Anne Hendricks
Sjoerd van Steenkiste
Yash Goyal
Karolina Stanczak
Aishwarya Agrawal
The increasing ubiquity of text-to-image (T2I) models as tools for visual content generation raises concerns about their ability to accurate… (see more)ly represent diverse cultural contexts. In this work, we present the first study to systematically quantify the alignment of T2I models and evaluation metrics with respect to both explicit as well as implicit cultural expectations. To this end, we introduce CulturalFrames, a novel benchmark designed for rigorous human evaluation of cultural representation in visual generations. Spanning 10 countries and 5 socio-cultural domains, CulturalFrames comprises 983 prompts, 3637 corresponding images generated by 4 state-of-the-art T2I models, and over 10k detailed human annotations. We find that T2I models not only fail to meet the more challenging implicit expectations but also the less challenging explicit expectations. Across models and countries, cultural expectations are missed an average of 44% of the time. Among these failures, explicit expectations are missed at a surprisingly high average rate of 68%, while implicit expectation failures are also significant, averaging 49%. Furthermore, we demonstrate that existing T2I evaluation metrics correlate poorly with human judgments of cultural alignment, irrespective of their internal reasoning. Collectively, our findings expose critical gaps, providing actionable directions for developing more culturally informed T2I models and evaluation methodologies.
2025-06-10
ArXiv (preprint)
arxiv.org
arxiv.org
Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCereal
Christina Butsko
Kristof Van Tricht
Gabriel Tseng
Giorgia Milli
David Rolnick
Ruben Cartuyvels
Inbal Becker Reshef
Zoltan Szantoi
Hannah Kerner
The increasing availability of geospatial foundation models has the potential to transform remote sensing applications such as land cover cl… (see more)assification, environmental monitoring, and change detection. Despite promising benchmark results, the deployment of these models in operational settings is challenging and rare. Standardized evaluation tasks often fail to capture real-world complexities relevant for end-user adoption such as data heterogeneity, resource constraints, and application-specific requirements. This paper presents a structured approach to integrate geospatial foundation models into operational mapping systems. Our protocol has three key steps: defining application requirements, adapting the model to domain-specific data and conducting rigorous empirical testing. Using the Presto model in a case study for crop mapping, we demonstrate that fine-tuning a pre-trained model significantly improves performance over conventional supervised methods. Our results highlight the model’s strong spatial and temporal generalization capabilities. Our protocol provides a replicable blueprint for practitioners and lays the groundwork for future research to operationalize foundation models in diverse remote sensing applications. Application of the protocol to the WorldCereal global crop-mapping system showcases the framework’s scalability.
2025-06-10
ICML.cc/2025/Workshop/TerraBytes (published)
openreview.net
openreview.net
Did I Faithfully Say What I Thought? Bridging the Gap Between Neural Activity and Self-Explanations in Large Language Models
Milan Bhan
Jean-Noël Vittaut
Nicolas Chesneau
Sarath Chandar
Marie-Jeanne Lesot
Large Language Models (LLM) have demonstrated the capability of generating free text self Natural Language Explanation (self-NLE) to justify… (see more) their answers. Despite their logical appearance, self-NLE do not necessarily reflect the LLM actual decision-making process, making such explanations unfaithful. While existing methods for measuring self-NLE faithfulness mostly rely on behavioral tests or computational block identification, none of them examines the neural activity underlying the model's reasoning. This work introduces a novel flexible framework for quantitatively measuring the faithfulness of LLM-generated self-NLE by directly comparing the latter with interpretations of the model's internal hidden states. The proposed framework is versatile and provides deep insights into self-NLE faithfulness by establishing a direct connection between self-NLE and model reasoning. This approach advances the understanding of self-NLE faithfulness and provides building blocks for generating more faithful self-NLE.
2025-06-10
ArXiv (preprint)
arxiv.org
arxiv.org
Did I Faithfully Say What I Thought? Bridging the Gap Between Neural Activity and Self-Explanations in Large Language Models
Milan Bhan
Jean-Noël Vittaut
Nicolas Chesneau
Sarath Chandar
Marie-Jeanne Lesot
Large Language Models (LLM) have demonstrated the capability of generating free text self Natural Language Explanation (self-NLE) to justify… (see more) their answers. Despite their logical appearance, self-NLE do not necessarily reflect the LLM actual decision-making process, making such explanations unfaithful. While existing methods for measuring self-NLE faithfulness mostly rely on behavioral tests or computational block identification, none of them examines the neural activity underlying the model's reasoning. This work introduces a novel flexible framework for quantitatively measuring the faithfulness of LLM-generated self-NLE by directly comparing the latter with interpretations of the model's internal hidden states. The proposed framework is versatile and provides deep insights into self-NLE faithfulness by establishing a direct connection between self-NLE and model reasoning. This approach advances the understanding of self-NLE faithfulness and provides building blocks for generating more faithful self-NLE.
2025-06-10
ArXiv (preprint)
doi.org
arxiv.org
Diffusion Tree Sampling: Scalable inference‑time alignment of diffusion models
Vineet Jain
Kusha Sareen
Mohammad Pedramfar
Siamak Ravanbakhsh
Adapting a pretrained diffusion model to new objectives at inference time remains an open problem in generative modeling. Existing steering … (see more)methods suffer from inaccurate value estimation, especially at high noise levels, which biases guidance. Moreover, information from past runs is not reused to improve sample quality, leading to inefficient use of compute. Inspired by the success of Monte Carlo Tree Search, we address these limitations by casting inference-time alignment as a search problem that reuses past computations. We introduce a tree-based approach that _samples_ from the reward-aligned target density by propagating terminal rewards back through the diffusion chain and iteratively refining value estimates with each additional generation. Our proposed method, Diffusion Tree Sampling (DTS), produces asymptotically exact samples from the target distribution in the limit of infinite rollouts, and its greedy variant Diffusion Tree Search (DTS*) performs a robust search for high reward samples. On MNIST and CIFAR-10 class-conditional generation, DTS matches the FID of the best-performing baseline with up to
2025-06-10
ICML.cc/2025/Workshop/PUT (poster)
openreview.net
openreview.net
Diffusion Tree Sampling: Scalable inference-time alignment of diffusion models
Vineet Jain
Kusha Sareen
Mohammad Pedramfar
Siamak Ravanbakhsh
Adapting a pretrained diffusion model to new objectives at inference time remains an open problem in generative modeling. Existing steering … (see more)methods suffer from inaccurate value estimation, especially at high noise levels, which biases guidance. Moreover, information from past runs is not reused to improve sample quality, resulting in inefficient use of compute. Inspired by the success of Monte Carlo Tree Search, we address these limitations by casting inference-time alignment as a search problem that reuses past computations. We introduce a tree-based approach that samples from the reward-aligned target density by propagating terminal rewards back through the diffusion chain and iteratively refining value estimates with each additional generation. Our proposed method, Diffusion Tree Sampling (DTS), produces asymptotically exact samples from the target distribution in the limit of infinite rollouts, and its greedy variant, Diffusion Tree Search (DTS
2025-06-10
ICML.cc/2025/Workshop/PUT (poster)
doi.org
openreview.net
Emergent brain-like representations in a goal-directed neural network model of visual search
Motahareh Pourrahimi
Pouya Bashivan
2025-06-10
bioRxiv (preprint)
doi.org
Geometry-Aware Preference Learning for 3D Texture Generation
AmirHossein Zamani
Tianhao Xie
Amir Aghdam
Tiberiu Popa
Eugene Belilovsky
Recent advances in 3D generative models have achieved impressive results but 3D contents generated by these models may not align with subjec… (see more)tive human preferences or task-specific criteria. Moreover, a core challenge in the 3D texture generation domain remains: most existing approaches rely on repeated calls to 2D text-to-image generative models, which lack an inherent understanding of the 3D structure of the input 3D mesh object. To address this, we propose an end-to-end differentiable preference learning framework that back-propagates human preferences, represented by differentiable reward functions, through the entire 3D generative pipeline, making the process inherently geometry-aware. We demonstrate the effectiveness of our framework using four proposed novel geometry-aware reward functions, offering a more controllable and interpretable pathway for high-quality 3D content creation from natural language.
2025-06-10
ICML.cc/2025/Workshop/MoFA (poster)
openreview.net
openreview.net
Language models’ activations linearly encode training-order recency
Dmitrii Krasheninnikov
Richard E. Turner
David Scott Krueger
2025-06-10
ICML.cc/2025/Workshop/MemFM (published)
openreview.net
openreview.net
Low-Rank Adaptation Secretly Imitates Differentially Private SGD
Saber Malekmohammadi
Golnoosh Farnadi
As pre-trained language models grow in size, full fine-tuning their parameters on task adaptation data becomes increasingly impractical. To … (see more)address this challenge, some methods for low-rank adaptation of language models have been proposed, e.g. LoRA, which incorporates trainable low-rank decomposition matrices into only some parameters of the pre-trained model, called adapters. This approach significantly reduces the number of trainable parameters compared to fine-tuning all parameters or adapters. In this work, we look at low-rank adaptation method from the lens of data privacy. We show theoretically that the low-rank adaptation used in LoRA is equivalent to fine-tuning adapters with noisy batch gradients - just like what DPSGD algorithm does. We also quantify the variance of the injected noise as a decreasing function of adaptation rank. By establishing a Berry-Esseen type bound on the total variation distance between the injected noise distribution and a Gaussian noise distribution with the same variance, we show that the dynamics of low-rank adaptation is very close to when DPSGD is performed w.r.t the adapters. Following our theoretical findings and approved by our experimental results, we show that low-rank adaptation provides robustness to membership inference attacks w.r.t the fine-tuning data.
2025-06-10
ICML.cc/2025/Workshop/MemFM (published)
openreview.net
openreview.net