pwc-archive/papers-with-abstracts huggingface dataset:
VistaFlow: Photorealistic Volumetric Reconstruction with Dynamic Resolution Management via Q-Learning
Authors: Jayram Palamadai, William Yu
Date: 2025-02-05 00:00:00
Tasks: CPU, Management, Novel View Synthesis, Q-Learning

We introduce VistaFlow, a scalable three-dimensional imaging technique capable of reconstructing fully interactive 3D volumetric images from a set of 2D photographs. Our model synthesizes novel viewpoints through a differentiable rendering system capable of dynamic resolution management on photorealistic 3D scenes. We achieve this through the introduction of QuiQ, a novel intermediate video controller trained through Q-learning to maintain a consistently high framerate by adjusting render resolution with millisecond precision. Notably, VistaFlow runs natively on integrated CPU graphics, making it viable for mobile and entry-level devices while still delivering high-performance rendering. VistaFlow bypasses Neural Radiance Fields (NeRFs), using the PlenOctree data structure to render complex light interactions such as reflection and subsurface scattering with minimal hardware requirements. Our model is capable of outperforming state-of-the-art methods with novel view synthesis at a resolution of 1080p at over 100 frames per second on consumer hardware. By tailoring render quality to the capabilities of each device, VistaFlow has the potential to improve the efficiency and accessibility of photorealistic 3D scene rendering across a wide spectrum of hardware, from high-end workstations to inexpensive microcontrollers.
Learning to Synthesize Compatible Fashion Items Using Semantic Alignment and Collocation Classification: An Outfit Generation Framework
Authors: Dongliang Zhou, Haijun Zhang, Kai Yang, Linlin Liu, Han Yan, Xiaofei Xu, Zhao Zhang, Shuicheng Yan
Date: 2025-02-05 00:00:00
Tasks: Fashion Compatibility Learning, Fashion Synthesis, Image-to-Image Translation

The field of fashion compatibility learning has attracted great attention from both the academic and industrial communities in recent years. Many studies have been carried out for fashion compatibility prediction, collocated outfit recommendation, artificial intelligence (AI)-enabled compatible fashion design, and related topics. In particular, AI-enabled compatible fashion design can be used to synthesize compatible fashion items or outfits in order to improve the design experience for designers or the efficacy of recommendations for customers. However, previous generative models for collocated fashion synthesis have generally focused on the image-to-image translation between fashion items of upper and lower clothing. In this paper, we propose a novel outfit generation framework, i.e., OutfitGAN, with the aim of synthesizing a set of complementary items to compose an entire outfit, given one extant fashion item and reference masks of target synthesized items. OutfitGAN includes a semantic alignment module, which is responsible for characterizing the mapping correspondence between the existing fashion items and the synthesized ones, to improve the quality of the synthesized images, and a collocation classification module, which is used to improve the compatibility of a synthesized outfit. In order to evaluate the performance of our proposed models, we built a large-scale dataset consisting of 20,000 fashion outfits. Extensive experimental results on this dataset show that our OutfitGAN can synthesize photo-realistic outfits and outperform state-of-the-art methods in terms of similarity, authenticity and compatibility measurements.
DiffListener: Discrete Diffusion Model for Listener Generation
Authors: Siyeol Jung, Taehwan Kim
Date: 2025-02-05 00:00:00
Tasks: model

The listener head generation (LHG) task aims to generate natural nonverbal listener responses based on the speaker's multimodal cues. While prior work either rely on limited modalities (e.g. audio and facial information) or employ autoregressive approaches which have limitations such as accumulating prediction errors. To address these limitations, we propose DiffListener, a discrete diffusion based approach for non-autoregressive listener head generation. Our model takes the speaker's facial information, audio, and text as inputs, additionally incorporating facial differential information to represent the temporal dynamics of expressions and movements. With this explicit modeling of facial dynamics, DiffListener can generate coherent reaction sequences in a non-autoregressive manner. Through comprehensive experiments, DiffListener demonstrates state-of-the-art performance in both quantitative and qualitative evaluations. The user study shows that DiffListener generates natural context-aware listener reactions that are well synchronized with the speaker. The code and demo videos are available in https://siyeoljung.github.io/DiffListener
Functional 3D Scene Synthesis through Human-Scene Optimization
Authors: Yao Wei, Matteo Toso, Pietro Morerio, Michael Ying Yang, Alessio Del Bue
Date: 2025-02-05 00:00:00
Tasks: Human-Object Interaction Detection, Scene Generation

This paper presents a novel generative approach that outputs 3D indoor environments solely from a textual description of the scene. Current methods often treat scene synthesis as a mere layout prediction task, leading to rooms with overlapping objects or overly structured scenes, with limited consideration of the practical usability of the generated environment. Instead, our approach is based on a simple, but effective principle: we condition scene synthesis to generate rooms that are usable by humans. This principle is implemented by synthesizing 3D humans that interact with the objects composing the scene. If this human-centric scene generation is viable, the room layout is functional and it leads to a more coherent 3D structure. To this end, we propose a novel method for functional 3D scene synthesis, which consists of reasoning, 3D assembling and optimization. We regard text guided 3D synthesis as a reasoning process by generating a scene graph via a graph diffusion network. Considering object functional co-occurrence, a new strategy is designed to better accommodate human-object interaction and avoidance, achieving human-aware 3D scene optimization. We conduct both qualitative and quantitative experiments to validate the effectiveness of our method in generating coherent 3D scene synthesis results.
Diffusion-empowered AutoPrompt MedSAM
Authors: Peng Huang, Shu Hu, Bo Peng, Xun Gong, Penghang Yin, Hongtu Zhu, Xi Wu, Xin Wang
Date: 2025-02-05 00:00:00
Tasks: Decoder

MedSAM, a medical foundation model derived from the SAM architecture, has demonstrated notable success across diverse medical domains. However, its clinical application faces two major challenges: the dependency on labor-intensive manual prompt generation, which imposes a significant burden on clinicians, and the absence of semantic labeling in the generated segmentation masks for organs or lesions, limiting its practicality for non-expert users. To address these limitations, we propose AutoMedSAM, an end-to-end framework derived from SAM, designed to enhance usability and segmentation performance. AutoMedSAM retains MedSAM's image encoder and mask decoder structure while introducing a novel diffusion-based class prompt encoder. The diffusion-based encoder employs a dual-decoder structure to collaboratively generate prompt embeddings guided by sparse and dense prompt definitions. These embeddings enhance the model's ability to understand and process clinical imagery autonomously. With this encoder, AutoMedSAM leverages class prompts to embed semantic information into the model's predictions, transforming MedSAM's semi-automated pipeline into a fully automated workflow. Furthermore, AutoMedSAM employs an uncertainty-aware joint optimization strategy during training to effectively inherit MedSAM's pre-trained knowledge while improving generalization by integrating multiple loss functions. Experimental results across diverse datasets demonstrate that AutoMedSAM achieves superior performance while broadening its applicability to both clinical settings and non-expert users. Code is available at https://github.com/HP-ML/AutoPromptMedSAM.git.
Diffusion Instruction Tuning
Authors: Chen Jin, Ryutaro Tanno, Amrutha Saseendran, Tom Diethe, Philip Teare
Date: 2025-02-04 00:00:00
Tasks: Image Generation

We introduce Lavender, a simple supervised fine-tuning (SFT) method that boosts the performance of advanced vision-language models (VLMs) by leveraging state-of-the-art image generation models such as Stable Diffusion. Specifically, Lavender aligns the text-vision attention in the VLM transformer with the equivalent used by Stable Diffusion during SFT, instead of adapting separate encoders. This alignment enriches the model's visual understanding and significantly boosts performance across in- and out-of-distribution tasks. Lavender requires just 0.13 million training examples, 2.5% of typical large-scale SFT datasets, and fine-tunes on standard hardware (8 GPUs) in a single day. It consistently improves state-of-the-art open-source multimodal LLMs (e.g., Llama-3.2-11B, MiniCPM-Llama3-v2.5), achieving up to 30% gains and a 68% boost on challenging out-of-distribution medical QA tasks. By efficiently transferring the visual expertise of image generators with minimal supervision, Lavender offers a scalable solution for more accurate vision-language systems. All code, training data, and models will be shared at https://astrazeneca.github.io/vlm/.
Harness Local Rewards for Global Benefits: Effective Text-to-Video Generation Alignment with Patch-level Reward Models
Authors: Shuting Wang, Haihong Tang, Zhicheng Dou, Chenyan Xiong
Date: 2025-02-04 00:00:00
Tasks: Text-to-Video Generation, Video Generation

The emergence of diffusion models (DMs) has significantly improved the quality of text-to-video generation models (VGMs). However, current VGM optimization primarily emphasizes the global quality of videos, overlooking localized errors, which leads to suboptimal generation capabilities. To address this issue, we propose a post-training strategy for VGMs, HALO, which explicitly incorporates local feedback from a patch reward model, providing detailed and comprehensive training signals with the video reward model for advanced VGM optimization. To develop an effective patch reward model, we distill GPT-4o to continuously train our video reward model, which enhances training efficiency and ensures consistency between video and patch reward distributions. Furthermore, to harmoniously integrate patch rewards into VGM optimization, we introduce a granular DPO (Gran-DPO) algorithm for DMs, allowing collaborative use of both patch and video rewards during the optimization process. Experimental results indicate that our patch reward model aligns well with human annotations and HALO substantially outperforms the baselines across two evaluation methods. Further experiments quantitatively prove the existence of patch defects, and our proposed method could effectively alleviate this issue.
Efficient Diffusion Models: A Survey
Authors: Hui Shen, JingXuan Zhang, Boning Xiong, Rui Hu, Shoufa Chen, Zhongwei Wan, Xin Wang, Yu Zhang, Zixuan Gong, Guangyin Bao, Chaofan Tao, Yongfeng Huang, Ye Yuan, Mi Zhang
Date: 2025-02-03 00:00:00
Tasks: Survey

Diffusion models have emerged as powerful generative models capable of producing high-quality contents such as images, videos, and audio, demonstrating their potential to revolutionize digital content creation. However, these capabilities come at the cost of their significant computational resources and lengthy generation time, underscoring the critical need to develop efficient techniques for practical deployment. In this survey, we provide a systematic and comprehensive review of research on efficient diffusion models. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient diffusion model topics from algorithm-level, system-level, and framework perspective, respectively. We have also created a GitHub repository where we organize the papers featured in this survey at https://github.com/AIoT-MLSys-Lab/Efficient-Diffusion-Model-Survey. We hope our survey can serve as a valuable resource to help researchers and practitioners gain a systematic understanding of efficient diffusion model research and inspire them to contribute to this important and exciting field.
On the existence of pure epsilon-equilibrium
Authors: Bary S. R. Pradelski, Bassel Tarbush
Date: 2025-02-11 00:00:00

We show that for any $\epsilon>0$, as the number of agents gets large, the share of games that admit a pure $\epsilon$-equilibrium converges to 1. Our result holds even for pure $\epsilon$-equilibrium in which all agents, except for at most one, play a best response. In contrast, it is known that the share of games that admit a pure Nash equilibrium, that is, for $\epsilon=0$, is asymptotically $1-1/e\approx 0.63$. This suggests that very small deviations from perfect rationality, captured by positive values of $\epsilon$, suffice to ensure the general existence of stable outcomes. We also study the existence of pure $\epsilon$-equilibrium when the number of actions gets large. Our proofs rely on the probabilistic method and on the Chen-Stein method.
Approximating Human Strategic Reasoning with LLM-Enhanced Recursive Reasoners Leveraging Multi-agent Hypergames
Authors: Vince Trencsenyi, Agnieszka Mensfelt, Kostas Stathis
Date: 2025-02-11 00:00:00

LLM-driven multi-agent-based simulations have been gaining traction with applications in game-theoretic and social simulations. While most implementations seek to exploit or evaluate LLM-agentic reasoning, they often do so with a weak notion of agency and simplified architectures. We implement a role-based multi-agent strategic interaction framework tailored to sophisticated recursive reasoners, providing the means for systematic in-depth development and evaluation of strategic reasoning. Our game environment is governed by the umpire responsible for facilitating games, from matchmaking through move validation to environment management. Players incorporate state-of-the-art LLMs in their decision mechanism, relying on a formal hypergame-based model of hierarchical beliefs. We use one-shot, 2-player beauty contests to evaluate the recursive reasoning capabilities of the latest LLMs, providing a comparison to an established baseline model from economics and data from human experiments. Furthermore, we introduce the foundations of an alternative semantic measure of reasoning to the k-level theory. Our experiments show that artificial reasoners can outperform the baseline model in terms of both approximating human behaviour and reaching the optimal solution.
White Hat Search Engine Optimization using Large Language Models
Authors: Niv Bardas, Tommy Mordo, Oren Kurland, Moshe Tennenholtz, Gal Zur
Date: 2025-02-11 00:00:00
Tasks: Document Ranking

We present novel white-hat search engine optimization techniques based on genAI and demonstrate their empirical merits.
Bayesian Optimization for Building Social-Influence-Free Consensus
Authors: Masaki Adachi, Siu Lun Chau, Wenjie Xu, Anurag Singh, Michael A. Osborne, Krikamol Muandet
Date: 2025-02-11 00:00:00
Tasks: Bayesian Optimization, Decision Making, energy trading

We introduce Social Bayesian Optimization (SBO), a vote-efficient algorithm for consensus-building in collective decision-making. In contrast to single-agent scenarios, collective decision-making encompasses group dynamics that may distort agents' preference feedback, thereby impeding their capacity to achieve a social-influence-free consensus -- the most preferable decision based on the aggregated agent utilities. We demonstrate that under mild rationality axioms, reaching social-influence-free consensus using noisy feedback alone is impossible. To address this, SBO employs a dual voting system: cheap but noisy public votes (e.g., show of hands in a meeting), and more accurate, though expensive, private votes (e.g., one-to-one interview). We model social influence using an unknown social graph and leverage the dual voting system to efficiently learn this graph. Our theoretical findigns show that social graph estimation converges faster than the black-box estimation of agents' utilities, allowing us to reduce reliance on costly private votes early in the process. This enables efficient consensus-building primarily through noisy public votes, which are debiased based on the estimated social graph to infer social-influence-free feedback. We validate the efficacy of SBO across multiple real-world applications, including thermal comfort, team building, travel negotiation, and energy trading collaboration.
On the Impact of the Utility in Semivalue-based Data Valuation
Authors: Mélissa Tamine, Benjamin Heymann, Patrick Loiseau, Maxime Vono
Date: 2025-02-10 00:00:00
Tasks: Data Valuation, valid

Semivalue-based data valuation uses cooperative-game theory intuitions to assign each data point a value reflecting its contribution to a downstream task. Still, those values depend on the practitioner's choice of utility, raising the question: How robust is semivalue-based data valuation to changes in the utility? This issue is critical when the utility is set as a trade-off between several criteria and when practitioners must select among multiple equally valid utilities. We address it by introducing the notion of a dataset's spatial signature: given a semivalue, we embed each data point into a lower-dimensional space where any utility becomes a linear functional, making the data valuation framework amenable to a simpler geometric picture. Building on this, we propose a practical methodology centered on an explicit robustness metric that informs practitioners whether and by how much their data valuation results will shift as the utility changes. We validate this approach across diverse datasets and semivalues, demonstrating strong agreement with rank-correlation analyses and offering analytical insight into how choosing a semivalue can amplify or diminish robustness.
How Humans Help LLMs: Assessing and Incentivizing Human Preference Annotators
Authors: Shang Liu, Hanzhao Wang, Zhongyao Ma, Xiaocheng Li
Date: 2025-02-10 00:00:00
Tasks: text annotation

Human-annotated preference data play an important role in aligning large language models (LLMs). In this paper, we investigate the questions of assessing the performance of human annotators and incentivizing them to provide high-quality annotations. The quality assessment of language/text annotation faces two challenges: (i) the intrinsic heterogeneity among annotators, which prevents the classic methods that assume the underlying existence of a true label; and (ii) the unclear relationship between the annotation quality and the performance of downstream tasks, which excludes the possibility of inferring the annotators' behavior based on the model performance trained from the annotation data. Then we formulate a principal-agent model to characterize the behaviors of and the interactions between the company and the human annotators. The model rationalizes a practical mechanism of a bonus scheme to incentivize annotators which benefits both parties and it underscores the importance of the joint presence of an assessment system and a proper contract scheme. From a technical perspective, our analysis extends the existing literature on the principal-agent model by considering a continuous action space for the agent. We show the gap between the first-best and the second-best solutions (under the continuous action space) is of $\Theta(1/\sqrt{n \log n})$ for the binary contracts and $\Theta(1/n)$ for the linear contracts, where $n$ is the number of samples used for performance assessment; this contrasts with the known result of $\exp(-\Theta(n))$ for the binary contracts when the action space is discrete. Throughout the paper, we use real preference annotation data to accompany our discussions.
Meta-Computing Enhanced Federated Learning in IIoT: Satisfaction-Aware Incentive Scheme via DRL-Based Stackelberg Game
Authors: Xiaohuan Li, Shaowen Qin, Xin Tang, Jiawen Kang, Jin Ye, Zhonghua Zhao, Yusi Zheng, Dusit Niyato
Date: 2025-02-10 00:00:00
Tasks: Deep Reinforcement Learning, Distributed Computing, Federated Learning

The Industrial Internet of Things (IIoT) leverages Federated Learning (FL) for distributed model training while preserving data privacy, and meta-computing enhances FL by optimizing and integrating distributed computing resources, improving efficiency and scalability. Efficient IIoT operations require a trade-off between model quality and training latency. Consequently, a primary challenge of FL in IIoT is to optimize overall system performance by balancing model quality and training latency. This paper designs a satisfaction function that accounts for data size, Age of Information (AoI), and training latency for meta-computing. Additionally, the satisfaction function is incorporated into the utility functions to incentivize nodes in IIoT participation in model training. We model the utility functions of servers and nodes as a two-stage Stackelberg game and employ a deep reinforcement learning approach to learn the Stackelberg equilibrium. This approach ensures balanced rewards and enhances the applicability of the incentive scheme for IIoT. Simulation results demonstrate that, under the same budget constraints, the proposed incentive scheme improves utility by at least 23.7% compared to existing FL schemes without compromising model accuracy.
Verifying Proportionality in Temporal Voting
Authors: Edith Elkind, Svetlana Obraztsova, Jannik Peters, Nicholas Teh
Date: 2025-02-09 00:00:00

We study a model of temporal voting where there is a fixed time horizon, and at each round the voters report their preferences over the available candidates and a single candidate is selected. Prior work has adapted popular notions of justified representation as well as voting rules that provide strong representation guarantees from the multiwinner election setting to this model. In our work, we focus on the complexity of verifying whether a given outcome offers proportional representation. We show that in the temporal setting verification is strictly harder than in multiwinner voting, but identify natural special cases that enable efficient algorithms.
Riemannian Manifold Learning for Stackelberg Games with Neural Flow Representations
Authors: Larkin Liu, Kashif Rasul, Yutong Chao, Jalal Etesami
Date: 2025-02-08 00:00:00

We present a novel framework for online learning in Stackelberg general-sum games, where two agents, the leader and follower, engage in sequential turn-based interactions. At the core of this approach is a learned diffeomorphism that maps the joint action space to a smooth Riemannian manifold, referred to as the Stackelberg manifold. This mapping, facilitated by neural normalizing flows, ensures the formation of tractable isoplanar subspaces, enabling efficient techniques for online learning. By assuming linearity between the agents' reward functions on the Stackelberg manifold, our construct allows the application of standard bandit algorithms. We then provide a rigorous theoretical basis for regret minimization on convex manifolds and establish finite-time bounds on simple regret for learning Stackelberg equilibria. This integration of manifold learning into game theory uncovers a previously unrecognized potential for neural normalizing flows as an effective tool for multi-agent learning. We present empirical results demonstrating the effectiveness of our approach compared to standard baselines, with applications spanning domains such as cybersecurity and economic supply chain optimization.