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Mohammad Hossein Rohban

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15 papers

2 author rows

TMLR Journal 2026 Journal Article

CARINOX: Inference-time Scaling with Category-Aware Reward-based Initial Noise Optimization and Exploration

Seyed Amir Kasaei
Ali Aghayari
Arash Marioriyad
Niki Sepasian
Shayan Baghayi Nejad
MohammadAmin Fazli
Mahdieh Soleymani Baghshah
Mohammad Hossein Rohban

Text-to-image diffusion models, such as Stable Diffusion, can produce high-quality and diverse images but often fail to achieve compositional alignment, particularly when prompts describe complex object relationships, attributes, or spatial arrangements. Recent inference-time approaches address this by optimizing or exploring the initial noise under the guidance of reward functions that score text–image alignment—without requiring model fine-tuning. While promising, each strategy has intrinsic limitations when used alone: optimization can stall due to poor initialization or unfavorable search trajectories, whereas exploration may require a prohibitively large number of samples to locate a satisfactory output. Our analysis further shows that neither single reward metrics nor ad-hoc combinations reliably capture all aspects of compositionality, leading to weak or inconsistent guidance. To overcome these challenges, we present Category-Aware Reward-based Initial Noise Optimization and EXploration (CARINOX), a unified framework that combines noise optimization and exploration with a principled reward selection procedure grounded in correlation with human judgments. Evaluations on two complementary benchmarks—covering diverse compositional challenges—show that CARINOX raises average alignment scores by +16% onT2I-CompBench++ and +11% on the HRS benchmark, consistently outperforming state-of-the-art optimization and exploration-based methods across all major categories, while preserving image quality and diversity.

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TMLR Journal 2026 Journal Article

Large Language Models for Scientific Idea Generation: A Creativity-Centered Survey

Fatemeh Shahhosseini
Arash Marioriyad
Ali Momen
Mahdieh Soleymani Baghshah
Mohammad Hossein Rohban
Shaghayegh Haghjooy Javanmard

Scientific idea generation is central to discovery, requiring the joint satisfaction of novelty and scientific soundness. Unlike standard reasoning or general creative generation, scientific ideation is inherently open-ended and multi-objective, making its automation particularly challenging. Recent advances in large language models (LLMs) have enabled the generation of coherent and plausible scientific ideas, yet the nature and limits of their creative capabilities remain poorly understood. This survey provides a structured synthesis of methods for LLM-driven scientific ideation, focusing on how different approaches trade off novelty and scientific validity. We organize existing methods into five complementary families: External knowledge augmentation, Prompt-based distributional steering, Inference-time scaling, Multi-agent collaboration, and Parameter-level adaptation. To interpret their contributions, we adopt two complementary creativity frameworks: Boden’s taxonomy to characterize the expected level of creative novelty, and Rhodes’ 4Ps framework to analyze the aspects or sources of creativity emphasized by each method. By aligning methodological developments with cognitive creativity frameworks, this survey clarifies the evaluation landscape and identifies key challenges and directions for reliable and systematic LLM-based scientific discovery.

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ICLR Conference 2025 Conference Paper

Adversarially Robust Anomaly Detection through Spurious Negative Pair Mitigation

Hossein Mirzaei
Mojtaba Nafez
Jafar Habibi
Mohammad Sabokrou
Mohammad Hossein Rohban

Despite significant progress in Anomaly Detection (AD), the robustness of existing detection methods against adversarial attacks remains a challenge, compromising their reliability in critical real-world applications such as autonomous driving. This issue primarily arises from the AD setup, which assumes that training data is limited to a group of unlabeled normal samples, making the detectors vulnerable to adversarial anomaly samples during testing. Additionally, implementing adversarial training as a safeguard encounters difficulties, such as formulating an effective objective function without access to labels. An ideal objective function for adversarial training in AD should promote strong perturbations both within and between the normal and anomaly groups to maximize margin between normal and anomaly distribution. To address these issues, we first propose crafting a pseudo-anomaly group derived from normal group samples. Then, we demonstrate that adversarial training with contrastive loss could serve as an ideal objective function, as it creates both inter- and intra-group perturbations. However, we notice that spurious negative pairs compromise the conventional contrastive loss for achieving robust AD. Spurious negative pairs are those that should be mapped closely but are erroneously separated. These pairs introduce noise and misguide the direction of inter-group adversarial perturbations. To overcome the effect of spurious negative pairs, we define opposite pairs and adversarially pull them apart to strengthen inter-group perturbations. Experimental results demonstrate our superior performance in both clean and adversarial scenarios, with a 26.1% improvement in robust detection across various challenging benchmark datasets.

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TMLR Journal 2025 Journal Article

Attention Overlap Is Responsible for The Entity Missing Problem in Text-to-image Diffusion Models!

Arash Mari Oriyad
Mohammadali Banayeeanzade
Reza Abbasi
Mohammad Hossein Rohban
Mahdieh Soleymani Baghshah

Text-to-image diffusion models such as Stable Diffusion and DALL-E have exhibited impressive capabilities in producing high-quality, diverse, and realistic images based on textual prompts. Nevertheless, a common issue arises where these models encounter difficulties in faithfully generating every entity specified in the prompt, leading to a recognized challenge known as entity missing in visual compositional generation. While previous studies indicated that actively adjusting cross-attention maps during inference could potentially resolve the issue, there has been a lack of systematic investigation into the specific objective function required for this task. In this work, we thoroughly investigate three potential causes of entity missing from the perspective of cross-attention maps: insufficient attention intensity, excessive attention spread, and significant overlap between attention maps of different entities. Through comprehensive empirical analysis, we found that optimizing metrics that quantify the overlap between attention maps of entities is highly effective at mitigating entity missing. We hypothesize that during the denoising process, entity-related tokens engage in a form of competition for attention toward specific regions through the cross-attention mechanism. This competition may result in the attention of a spatial location being divided among multiple tokens, leading to difficulties in accurately generating the entities associated with those tokens. Building on this insight, we propose four overlap-based loss functions that can be used to implicitly manipulate the latent embeddings of the diffusion model during inference: Intersection over union (IoU), center-of-mass (CoM) distance, Kullback–Leibler (KL) divergence, and clustering compactness (CC). Extensive experiments on a diverse set of prompts demonstrate that our proposed training-free methods substantially outperform previous approaches on a range of compositional alignment metrics, including visual question-answering, captioning score, CLIP similarity, and human evaluation. Notably, our method outperforms the best baseline by $9\%$ in human evaluation.

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NeurIPS Conference 2025 Conference Paper

FrameShield: Adversarially Robust Video Anomaly Detection

Mojtaba Nafez
Mobina Poulaei
Nikan Vasei
Bardia moakhar
Mohammad Sabokrou
Mohammad Hossein Rohban

Weakly Supervised Video Anomaly Detection (WSVAD) has achieved notable advancements, yet existing models remain vulnerable to adversarial attacks, limiting their reliability. Due to the inherent constraints of weak supervision—where only video-level labels are provided despite the need for frame-level predictions—traditional adversarial defense mechanisms, such as adversarial training, are not effective since video-level adversarial perturbations are typically weak and inadequate. To address this limitation, pseudo-labels generated directly from the model can enable frame-level adversarial training; however, these pseudo-labels are inherently noisy, significantly degrading performance. We therefore introduce a novel Pseudo-Anomaly Generation method called Spatiotemporal Region Distortion (SRD), which creates synthetic anomalies by applying severe augmentations to localized regions in normal videos while preserving temporal consistency. Integrating these precisely annotated synthetic anomalies with the noisy pseudo-labels substantially reduces label noise, enabling effective adversarial training. Extensive experiments demonstrate that our method significantly enhances the robustness of WSVAD models against adversarial attacks, outperforming state-of-the-art methods by an average of 71. 0\% in overall AUROC performance across multiple benchmarks. The implementation and code are publicly available at FrameShield (GitHub).

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NeurIPS Conference 2025 Conference Paper

Navigating the MIL Trade-Off: Flexible Pooling for Whole Slide Image Classification

Hossein Jafarinia
Danial Hamdi
Amirhossein Alamdar
Elahe Zahiri
Soroush Vafaie Tabar
Alireza Alipanah
Nahal Mirzaie
Saeed Razavi

Multiple Instance Learning (MIL) is a standard weakly supervised approach for Whole Slide Image (WSI) classification, where performance hinges on both feature representation and MIL pooling strategies. Recent research has predominantly focused on Transformer-based architectures adapted for WSIs. However, we argue that this trend faces a fundamental limitation: data scarcity. In typical settings, Transformer models yield only marginal gains without access to large-scale datasets—resources that are virtually inaccessible to all but a few well-funded research labs. Motivated by this, we revisit simple, non-attention MIL with unsupervised slide features and analyze temperature-$\beta$-controlled log-sum-exp (LSE) pooling. For slides partitioned into $N$ patches, we theoretically show that LSE has a smooth transition at a critical $\beta_{\mathrm{crit}}=\mathcal{O}(\log N)$ threshold, interpolating between mean-like aggregation (stable, better generalization but less sensitive) and max-like aggregation (more sensitive but looser generalization bounds). Grounded in this analysis, we introduce Maxsoft—a novel MIL pooling function that enables flexible control over this trade-off, allowing adaptation to specific tasks and datasets. To further tackle real-world deployment challenges such as specimen heterogeneity, we propose PerPatch augmentation—a simple yet effective technique that enhances model robustness. Empirically, Maxsoft achieves state-of-the-art performance in low-data regimes across four major benchmarks (CAMELYON16, CAMELYON17, TCGA-Lung, and SICAP-MIL), often matching or surpassing large-scale foundation models. When combined with PerPatch augmentation, this performance is further improved through increased robustness. Code is available at \href{https: //github. com/jafarinia/maxsoft}{\texttt{https: //github. com/jafarinia/maxsoft}}

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NeurIPS Conference 2025 Conference Paper

Spurious-Aware Prototype Refinement for Reliable Out-of-Distribution Detection

Reihaneh Zohrabi
Hosein Hasani
Mahdieh Baghshah
Anna Rohrbach
Marcus Rohrbach
Mohammad Hossein Rohban

Out-of-distribution (OOD) detection is crucial for ensuring the reliability and safety of machine learning models in real-world applications, where they frequently face data distributions unseen during training. Despite progress, existing methods are often vulnerable to spurious correlations that mislead models and compromise robustness. To address this, we propose SPROD, a novel prototype-based OOD detection approach that explicitly addresses the challenge posed by unknown spurious correlations. Our post-hoc method refines class prototypes to mitigate bias from spurious features without additional data or hyperparameter tuning, and is broadly applicable across diverse backbones and OOD detection settings. We conduct a comprehensive spurious correlation OOD detection benchmarking, comparing our method against existing approaches and demonstrating its superior performance across challenging OOD datasets, such as CelebA, Waterbirds, UrbanCars, Spurious Imagenet, and the newly introduced Animals MetaCoCo. On average, SPROD improves AUROC by 4. 8% and FPR@95 by 9. 4% over the second best.

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TMLR Journal 2025 Journal Article

Why Settle for Mid: A Probabilistic Viewpoint to Spatial Relationship Alignment in Text-to-image Models

Parham Rezaei
Arash Marioriyad
Mahdieh Soleymani Baghshah
Mohammad Hossein Rohban

Despite the ability of text-to-image models to generate high-quality, realistic, and diverse images, they face challenges in compositional generation, often struggling to accurately represent details specified in the input prompt. A prevalent issue in compositional generation is the misalignment of spatial relationships, as models often fail to faithfully generate images that reflect the spatial configurations specified between objects in the input prompts. To address this challenge, we propose a novel probabilistic framework for modeling the relative spatial positioning of objects in a scene, leveraging the concept of Probability of Superiority (PoS). Building on this insight, we make two key contributions. First, we introduce a novel evaluation metric, PoS-based Evaluation (PSE), designed to assess the alignment of 2D and 3D spatial relationships between text and image, with improved adherence to human judgment. Second, we propose PoS-based Generation (PSG), an inference-time method that improves the alignment of 2D and 3D spatial relationships in T2I models without requiring fine-tuning. PSG employs a PoS-based reward function that can be utilized in two distinct ways: (1) as a gradient-based guidance mechanism applied to the cross-attention maps during the denoising steps, or (2) as a search-based strategy that evaluates a set of initial noise vectors to select the best one. Extensive experiments demonstrate that the PSE metric exhibits stronger alignment with human judgment compared to traditional center-based metrics, providing a more nuanced and reliable measure of complex spatial relationship accuracy in text-image alignment. Furthermore, PSG significantly enhances the ability of text-to-image models to generate images with specified spatial configurations, outperforming state-of-the-art methods across multiple evaluation metrics and benchmarks.

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ECAI Conference 2024 Conference Paper

GABInsight: Exploring Gender-Activity Binding Bias in Vision-Language Models

Ali Abdollahi
Mahdi Ghaznavi
Mohammad Reza Karimi Nejad
Arash Mari Oriyad
Reza Abbasi
Ali Salesi
Melika Behjati
Mohammad Hossein Rohban

Vision-language models (VLMs) are intensively used in many downstream tasks, including those requiring assessments of individuals appearing in the images. While VLMs perform well in simple single-person scenarios, in real-world applications, we often face complex situations in which there are persons of different genders doing different activities. We show that in such cases, VLMs are biased towards identifying the individual with the expected gender (according to ingrained gender stereotypes in the model or other forms of sample selection bias) as the performer of the activity. We refer to this bias in associating an activity with the gender of its actual performer in an image or text as the Gender-Activity Binding (GAB) bias and analyze how this bias is internalized in VLMs. To assess this bias, we have introduced the GAB dataset with approximately 5500 AI-generated images that represent a variety of activities, addressing the scarcity of real-world images for some scenarios. To have extensive quality control, the generated images are evaluated for their diversity, quality, and realism. We have tested 12 renowned pre-trained VLMs on this dataset in the context of text-to-image and image-to-text retrieval to measure the effect of this bias on their predictions. Additionally, we have carried out supplementary experiments to quantify the bias in VLMs’ text encoders and to evaluate VLMs’ capability to recognize activities. Our experiments indicate that VLMs experience an average performance decline of about 13. 2% when confronted with gender-activity binding bias.

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ICML Conference 2024 Conference Paper

RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples

Hossein Mirzaei
Mohammad Jafari
Hamid Reza Dehbashi
Ali Ansari 0001
Sepehr Ghobadi
Masoud Hadi
Arshia Soltani Moakhar
Mohammad Azizmalayeri

In recent years, there have been significant improvements in various forms of image outlier detection. However, outlier detection performance under adversarial settings lags far behind that in standard settings. This is due to the lack of effective exposure to adversarial scenarios during training, especially on unseen outliers, leading detection models failing to learn robust features. To bridge this gap, we introduce RODEO, a data-centric approach that generates effective outliers for robust outlier detection. More specifically, we show that incorporating outlier exposure (OE) and adversarial training could be an effective strategy for this purpose, as long as the exposed training outliers meet certain characteristics, including diversity, and both conceptual differentiability and analogy to the inlier samples. We leverage a text-to-image model to achieve this goal. We demonstrate both quantitatively and qualitatively that our adaptive OE method effectively generates ”diverse” and ”near-distribution” outliers, leveraging information from both text and image domains. Moreover, our experimental results show that utilizing our synthesized outliers significantly enhances the performance of the outlier detector, particularly in adversarial settings.

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ICLR Conference 2023 Conference Paper

Fake It Until You Make It: Towards Accurate Near-Distribution Novelty Detection

Hossein Mirzaei
Mohammadreza Salehi
Sajjad Shahabi
Efstratios Gavves
Cees G. M. Snoek
Mohammad Sabokrou
Mohammad Hossein Rohban

We aim for image-based novelty detection. Despite considerable progress, existing models either fail or face dramatic drop under the so-called ``near-distribution" setup, where the differences between normal and anomalous samples are subtle. We first demonstrate existing methods could experience up to 20\% decrease in their AUCs in the near-distribution setting. Next, we propose to exploit a score-based generative model to produce synthetic near-distribution anomalous data. Our model is then fine-tuned to distinguish such data from the normal samples. We make quantitative as well as qualitative evaluation of this strategy, and compare the results with a variety of GAN-based models. Effectiveness of our method for both near-distribution and standard novelty detection is assessed through extensive experiments on datasets in diverse applications such as medical images, object classification, and quality control. This reveals that our method significantly improves upon existing models, and consistently decreases the gap between the near-distribution and standard novelty detection AUCs by a considerable amount.

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TMLR Journal 2022 Journal Article

A Unified Survey on Anomaly, Novelty, Open-Set, and Out of-Distribution Detection: Solutions and Future Challenges

Mohammadreza Salehi
Hossein Mirzaei
Dan Hendrycks
Yixuan Li
Mohammad Hossein Rohban
Mohammad Sabokrou

Machine learning models often encounter samples that are diverged from the training distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently assign that sample to an in-class label, significantly compromises the reliability of a model. The problem has gained significant attention due to its importance for safety deploying models in open-world settings. Detecting OOD samples is challenging due to the intractability of modeling all possible unknown distributions. To date, several research domains tackle the problem of detecting unfamiliar samples, including anomaly detection, novelty detection, one-class learning, open set recognition, and out-of-distribution detection. Despite having similar and shared concepts, out-of-distribution, open-set, and anomaly detection have been investigated independently. Accordingly, these research avenues have not crosspollinated, creating research barriers. While some surveys intend to provide an overview of these approaches, they seem to only focus on a specific domain without examining the relationship between different domains. This survey aims to provide a cross-domain and comprehensive review of numerous eminent works in respective areas while identifying their commonalities. Researchers can benefit from the overview of research advances in different fields and develop future methodology synergistically. Furthermore, to the best of our knowledge, while there are surveys in anomaly detection or one-class learning, there is no comprehensive or up-to-date survey on out-of-distribution detection, which this survey covers extensively. Finally, having a unified cross-domain perspective, this study discusses and sheds light on future lines of research, intending to bring these fields closer together.

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NeurIPS Conference 2022 Conference Paper

Your Out-of-Distribution Detection Method is Not Robust!

Mohammad Azizmalayeri
Arshia Soltani Moakhar
Arman Zarei
Reihaneh Zohrabi
Mohammad Manzuri
Mohammad Hossein Rohban

Out-of-distribution (OOD) detection has recently gained substantial attention due to the importance of identifying out-of-domain samples in reliability and safety. Although OOD detection methods have advanced by a great deal, they are still susceptible to adversarial examples, which is a violation of their purpose. To mitigate this issue, several defenses have recently been proposed. Nevertheless, these efforts remained ineffective, as their evaluations are based on either small perturbation sizes, or weak attacks. In this work, we re-examine these defenses against an end-to-end PGD attack on in/out data with larger perturbation sizes, e. g. up to commonly used $\epsilon=8/255$ for the CIFAR-10 dataset. Surprisingly, almost all of these defenses perform worse than a random detection under the adversarial setting. Next, we aim to provide a robust OOD detection method. In an ideal defense, the training should expose the model to almost all possible adversarial perturbations, which can be achieved through adversarial training. That is, such training perturbations should based on both in- and out-of-distribution samples. Therefore, unlike OOD detection in the standard setting, access to OOD, as well as in-distribution, samples sounds necessary in the adversarial training setup. These tips lead us to adopt generative OOD detection methods, such as OpenGAN, as a baseline. We subsequently propose the Adversarially Trained Discriminator (ATD), which utilizes a pre-trained robust model to extract robust features, and a generator model to create OOD samples. We noted that, for the sake of training stability, in the adversarial training of the discriminator, one should attack real in-distribution as well as real outliers, but not generated outliers. Using ATD with CIFAR-10 and CIFAR-100 as the in-distribution data, we could significantly outperform all previous methods in the robust AUROC while maintaining high standard AUROC and classification accuracy. The code repository is available at https: //github. com/rohban-lab/ATD.

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ICML Conference 2013 Conference Paper

Topic Discovery through Data Dependent and Random Projections

Weicong Ding
Mohammad Hossein Rohban
Prakash Ishwar
Venkatesh Saligrama

We present algorithms for topic modeling based on the geometry of cross-document word-frequency patterns. This perspective gains significance under the so called separability condition. This is a condition on existence of novel-words that are unique to each topic. We present a suite of highly efficient algorithms with provable guarantees based on data-dependent and random projections to identify novel words and associated topics. Our key insight here is that the maximum and minimum values of cross-document frequency patterns projected along any direction are associated with novel words. While our sample complexity bounds for topic recovery are similar to the state-of-art, the computational complexity of our random projection scheme scales linearly with the number of documents and the number of words per document. We present several experiments on synthetic and realworld datasets to demonstrate qualitative and quantitative merits of our scheme.

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AAAI Conference 2012 Conference Paper

A Bayesian Approach to the Data Description Problem

Alireza Ghasemi
Hamid R. Rabiee
Mohammad Taghi Manzuri
Mohammad Hossein Rohban

In this paper, we address the problem of data description using a Bayesian framework. The goal of data description is to draw a boundary around objects of a certain class of interest to discriminate that class from the rest of the feature space. Data description is also known as one-class learning and has a wide range of applications. The proposed approach uses a Bayesian framework to precisely compute the class boundary and therefore can utilize domain information in form of prior knowledge in the framework. It can also operate in the kernel space and therefore recognize arbitrary boundary shapes. Moreover, the proposed method can utilize unlabeled data in order to improve accuracy of discrimination. We evaluate our method using various real-world datasets and compare it with other state of the art approaches of data description. Experiments show promising results and improved performance over other data description and one-class learning algorithms.

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