GenAI4ELab/papercli-papers huggingface dataset:
ST3: Accelerating Multimodal Large Language Model by Spatial-Temporal Visual Token Trimming
Authors: Jiedong Zhuang; Lu Lu; Ming Dai; Rui Hu; Jian Chen; Qiang Liu; Haoji Hu
Venue: AAAI 2025 (main)

Abstract: Multimodal large language models (MLLMs) enhance their perceptual capabilities by integrating visual and textual information. However, processing the massive number of visual tokens incurs a significant computational cost. Existing analysis of the MLLM attention mechanisms remains shallow, leading to coarse-grain token pruning strategies that fail to effectively balance speed and accuracy. In this paper, we conduct a comprehensive investigation of MLLM attention mechanisms with LLaVA. We find that numerous visual tokens and partial attention computations are redundant during the decoding process. Based on this insight, we propose Spatial-Temporal Visual Token Trimming (ST3), a framework designed to accelerate MLLM inference without retraining. ST3 consists of two primary components: 1) Progressive Visual Token Pruning (PVTP), which eliminates inattentive visual tokens across layers, and 2)  Visual Token Annealing (VTA), which dynamically reduces the number of visual tokens in each layer as the generated tokens grow. Together, these techniques deliver around 2x faster inference with only about 30% KV cache memory compared to the original LLaVA, while maintaining consistent performance across various datasets. Crucially, ST3 can be seamlessly integrated into existing pre-trained MLLMs, providing a plug-and-play solution for efficient inference.
Dynamic Entity-Masked Graph Diffusion Model for Histopathology Image Representation Learning
Authors: Zhenfeng Zhuang; Min Cen; Yanfeng Li; Fangyu Zhou; Lequan Yu; Baptiste Magnier; Liansheng Wang
Venue: AAAI 2025 (main)

Abstract: Significant disparities between the features of natural images and those inherent to histopathological images make it challenging to directly apply and transfer pre-trained models from natural images to histopathology tasks. Moreover, the frequent lack of annotations in histopathology patch images has driven researchers to explore self-supervised learning methods like mask reconstruction for learning representations from large amounts of unlabeled data. Crucially, previous mask-based efforts in self-supervised learning have often overlooked the spatial interactions among entities, which are essential for constructing accurate representations of pathological entities. To address these challenges, constructing graphs of entities is a promising approach. In addition, the diffusion reconstruction strategy has recently shown superior performance through its random intensity noise addition technique to enhance the robust learned representation. Therefore, we introduce H-MGDM, a novel self-supervised Histopathology image representation learning method through the Dynamic Entity-Masked Graph Diffusion Model. Specifically, we propose to use complementary subgraphs as latent diffusion conditions and self-supervised targets respectively during pre-training. We note that the graph can embed entities' topological relationships and enhance representation. Dynamic conditions and targets can improve pathological fine reconstruction. Our model has conducted pretraining experiments on three large histopathological datasets. The advanced predictive performance and interpretability of H-MGDM are clearly evaluated on comprehensive downstream tasks such as classification and survival analysis on six datasets.
CoCoCo: Improving Text-Guided Video Inpainting for Better Consistency, Controllability and Compatibility
Authors: Bojia Zi; Shihao Zhao; Xianbiao Qi; Jianan Wang; Yukai Shi; Qianyu Chen; Bin Liang; Rong Xiao; Kam-Fai Wong; Lei Zhang
Venue: AAAI 2025 (main)

Abstract: Video inpainting is a crucial task with diverse applications, including fine-grained video editing, video recovery, and video dewatermarking. However, most existing video inpainting methods primarily focus on visual content completion while neglecting text information. There are only a limited number of text-guided video inpainting techniques, and these techniques struggle with maintaining visual quality and exhibit poor semantic representation capabilities. In this paper, we introduce CoCoCo, a text-guided video inpainting diffusion framework. To address the aforementioned challenges, we enhance both the training data and model structure. Specifically, we devise an instance-aware region selection strategy for masked area sampling and develop a novel motion block that incorporates efficient 3D full attention and textual cross attention. Additionally, our CoCoCo framework can be seamlessly integrated with various personalized text-to-image diffusion models through a delicate training-free transfer mechanism. Comprehensive experiments demonstrate that CoCoCo can create high-quality visual content with enhanced temporal consistency, improved text controllability, and better compatibility with personalized image models.
RhythmMamba: Fast, Lightweight, and Accurate Remote Physiological Measurement
Authors: Bochao Zou; Zizheng Guo; Xiaocheng Hu; Huimin Ma
Venue: AAAI 2025 (main)

Abstract: Remote photoplethysmography (rPPG) is a method for non-contact measurement of physiological signals from facial videos, holding great potential in various applications such as healthcare, affective computing, and anti-spoofing. Existing deep learning methods struggle to address two core issues of rPPG simultaneously: understanding the periodic pattern of rPPG among long contexts and addressing large spatiotemporal redundancy in video segments. These represent a trade-off between computational complexity and the ability to capture long-range dependencies. In this paper, we introduce RhythmMamba, a state space model-based method that captures long-range dependencies while maintaining linear complexity. By viewing rPPG as a time series task through the proposed frame stem, the periodic variations in pulse waves are modeled as state transitions. Additionally, we design multi-temporal constraint and frequency domain feed-forward, both aligned with the characteristics of rPPG time series, to improve the learning capacity of Mamba for rPPG signals. Extensive experiments show that RhythmMamba achieves state-of-the-art performance with 319% throughput and 23% peak GPU memory.
AdaCo: Overcoming Visual Foundation Model Noise in 3D Semantic Segmentation via Adaptive Label Correction
Authors: Pufan Zou; Shijia Zhao; Weijie Huang; Qiming Xia; Chenglu Wen; Wei Li; Cheng Wang
Venue: AAAI 2025 (main)

Abstract: Recently,  Visual Foundation Models (VFMs) have shown a remarkable generalization performance in 3D perception tasks.
However,  their effectiveness in large-scale outdoor datasets remains constrained by the scarcity of accurate supervision signals, the extensive noise caused by variable outdoor conditions, and the abundance of unknown objects. In this work, we propose a novel label-free learning method, Adaptive Label Correction (AdaCo), for 3D semantic segmentation.
AdaCo first introduces the Cross-modal Label Generation Module (CLGM), providing cross-modal supervision with the formidable interpretive capabilities of the VFMs. Subsequently,  AdaCo incorporates the Adaptive Noise Corrector (ANC), updating and adjusting the noisy samples within this supervision iteratively during training. Moreover, we develop an Adaptive Robust Loss (ARL) function to modulate each sample's sensitivity to noisy supervision, preventing potential underfitting issues associated with robust loss. Our proposed AdaCo can effectively mitigate the performance limitations of label-free learning networks in 3D semantic segmentation tasks. Extensive experiments on two outdoor benchmark datasets highlight the superior performance of our method.
L-Man: A Large Multi-modal Model Unifying Human-centric Tasks
Authors: Jialong Zuo; Ying Nie; Tianyu Guo; Huaxin Zhang; Jiahao Hong; Nong Sang; Changxin Gao; Kai Han
Venue: AAAI 2025 (main)

Abstract: Large language models (LLMs) have recently shown notable progress in unifying various visual tasks with an open-ended form. However, when transferred to human-centric tasks, despite their remarkable multi-modal understanding ability in general domains, they lack further human-related domain knowledge and show unsatisfactory performance. Meanwhile, current human-centric unified models are mostly restricted to a pre-defined form and lack open-ended task capability. Therefore, it is necessary to propose a large multi-modal model which utilizes LLMs to unify various human-centric tasks. We forge ahead along this path from the aspects of dataset and model. Specifically, we first construct a large-scale language-image instruction-following dataset named HumanIns based on existing 20 open datasets from 6 diverse downstream tasks, which provides sufficient and diverse data to implement multi-modal training. Then, a model named L-Man including a query adapter is designed to extract the multi-grained semantics of image and align the cross-modal information between image and text. In practice, we introduce a two-stage training strategy, where the first stage extracts generic text-relevant visual information, and the second stage maps the visual features to the embedding space of the LLM. By tuning on HumanIns, our model shows significant superiority on human-centric tasks compared with existing large multi-modal models, and also achieves even better results on downstream datasets compared with respective task-specific models.
Cirbo: A New Tool for Boolean Circuit Analysis and Synthesis
Authors: Daniil Averkov; Tatiana Belova; Gregory Emdin; Mikhail Goncharov; Viktoriia Krivogornitsyna; Alexander S. Kulikov; Fedor Kurmazov; Daniil Levtsov; Georgie Levtsov; Vsevolod Vaskin; Aleksey Vorobiev
Venue: AAAI 2025 (main)

Abstract: We present an open-source tool for manipulating Boolean circuits. It implements efficient algorithms, both existing and novel, for a rich variety of frequently used circuit tasks such as satisfiability, synthesis, and minimization. We tested the tool on a wide range of practically relevant circuits (computing, in particular, symmetric and arithmetic functions) that have been optimized intensively by the community for the last three years. The tool helped us to win the IWLS 2024 Programming Contest. In 2023, it was Google DeepMind who took the first place in the competition. We were able to reduce the size of the best circuits from 2023 by 12% on average, whereas for some individual circuits, our size reduction was as large as 83%.
Learning Valid Dual Bounds in Constraint Programming: Boosted Lagrangian Decomposition with Self-Supervised Learning
Authors: Swann Bessa; Darius Dabert; Max Bourgeat; Louis-Martin Rousseau; Quentin Cappart
Venue: AAAI 2025 (main)

Abstract: Lagrangian decomposition (LD) is a relaxation method that provides a dual bound for constrained optimization problems by decomposing them into more manageable sub-problems. This bound can be used in branch-and-bound algorithms to prune the search space effectively.In brief, a vector of Lagrangian multipliers is associated with each sub-problem, and an iterative procedure (e.g., a sub-gradient optimization) adjusts these multipliers to find the tightest bound. Initially applied to integer programming, Lagrangian decomposition also had success in constraint programming due to its versatility and the fact that global constraints provide natural sub-problems. However, the non-linear and combinatorial nature of sub-problems in constraint programming makes it computationally intensive to optimize the Lagrangian multipliers with sub-gradient methods at each node of the tree search. This currently limits the practicality of LD as a general bounding mechanism for constraint programming. To address this challenge, we propose a self-supervised learning approach that leverages neural networks to generate multipliers directly, yielding tight bounds. This approach significantly reduces the number of sub-gradient optimization steps required, enhancing the pruning efficiency and reducing the execution time of constraint programming solvers. This contribution is one of the few that leverage learning to enhance bounding mechanisms on the dual side, a critical element in the design of combinatorial solvers.  This work presents a generic method for learning valid dual bounds in constraint programming. We validate our approach on two challenging combinatorial problems: The multi-dimensional knapsack problem and the shift scheduling problem. The  results show that our approach can solve more instances than the standard application of LD to constraint programming, reduce execution time by more than half, and has promising generalization ability through fine-tuning.
Exploiting Symmetries in MUS Computation
Authors: Ignace Bleukx; Hélène Verhaeghe; Bart Bogaerts; Tias Guns
Venue: AAAI 2025 (main)

Abstract: In eXplainable Constraint Solving (XCS), it is common to extract a Minimal Unsatisfiable Subset (MUS) from a set of unsatisfiable constraints. This helps explain to a user why a constraint specification does not admit a solution. Finding MUSes can be computationally expensive for highly symmetric problems, as many combinations of constraints need to be considered. In the traditional context of solving satisfaction problems, symmetry has been well studied, and effective ways to detect and exploit symmetries during the search exist. However, in the setting of finding MUSes of unsatisfiable constraint programs, symmetries are understudied. In this paper, we take inspiration from existing symmetry-handling techniques and adapt well-known MUS-computation methods to exploit symmetries in the specification, speeding-up overall computation time. Our results display a significant reduction of runtime for our adapted algorithms compared to the baseline on symmetric problems.
Optimal Classification Trees for Continuous Feature Data Using Dynamic Programming with Branch-and-Bound
Authors: Cătălin E. Brița; Jacobus G. M. van der Linden; Emir Demirović
Venue: AAAI 2025 (main)

Abstract: Computing an optimal classification tree that provably maximizes training performance within a given size limit, is NP-hard, and in practice, most state-of-the-art methods do not scale beyond computing optimal trees of depth three. Therefore, most methods rely on a coarse binarization of continuous features to maintain scalability. We propose a novel algorithm that optimizes trees directly on the continuous feature data using dynamic programming with branch-and-bound. We develop new pruning techniques that eliminate many sub-optimal splits in the search when similar to previously computed splits and we provide an efficient subroutine for computing optimal depth-two trees. Our experiments demonstrate that these techniques improve runtime by one or more orders of magnitude over state-of-the-art optimal methods and improve test accuracy by 5% over greedy heuristics.
Massively Parallel Continuous Local Search for Hybrid SAT Solving on GPUs
Authors: Yunuo Cen; Zhiwei Zhang; Xuanyao Fong
Venue: AAAI 2025 (main)

Abstract: Although state-of-the-art (SOTA) SAT solvers based on conflict-driven clause learning (CDCL) have achieved remarkable engineering success, their sequential nature limits the parallelism that may be extracted for acceleration on platforms such as the graphics processing unit (GPU). In this work, we propose FastFourierSAT, a highly parallel hybrid SAT solver based on gradient-driven continuous local search (CLS). This is achieved by a parallel algorithm inspired by the fast Fourier transform (FFT)-based convolution for computing the elementary symmetric polynomials (ESPs), which is the major computational task in previous CLS methods. The complexity of our algorithm matches the best previous result. Furthermore, the substantial parallelism inherent in our algorithm can leverage the GPU for acceleration, demonstrating significant improvement over the previous CLS approaches. FastFourierSAT is compared with a wide set of SOTA parallel SAT solvers on extensive benchmarks including combinatorial and industrial problems. Results show that FastFourierSAT computes the gradient 100+ times faster than previous prototypes on CPU. Moreover, FastFourierSAT solves most instances and demonstrates promising performance on larger-size instances.
Linear Equations with Min and Max Operators: Computational Complexity
Authors: Krishnendu Chatterjee; Ruichen Luo; Raimundo Saona; Jakub Svoboda
Venue: AAAI 2025 (main)

Abstract: We consider a class of optimization problems defined by a system of linear equations with min and max operators. 
	This class of optimization problems has been studied under restrictive conditions, such as,
    (C1) the halting or stability condition;
    (C2) the non-negative coefficients condition;
    (C3) the sum upto 1 condition; and
    (C4) the only min or only max operator condition.
	Several seminal results in the literature focus on special cases.
	For example, turn-based stochastic games correspond to conditions C2 and C3; and Markov decision process to conditions C2, C3, and C4.
	However, the systematic computational complexity study of all the cases has not been explored, which we address in this work.
	Some highlights of our results are: with conditions C2 and C4, and with conditions C3 and C4, the problem is NP-complete, whereas with condition C1 only, the problem is in UP intersects coUP.
	Finally, we establish the computational complexity of the decision problem of checking the respective conditions.
Quantified Linear and Polynomial Arithmetic Satisfiability via Template-based Skolemization
Authors: Krishnendu Chatterjee; Ehsan Kafshdar Goharshady; Mehrdad Karrabi; Harshit J Motwani; Maximilian Seeliger; Đorđe Žikelić
Venue: AAAI 2025 (main)

Abstract: The problem of checking satisfiability of linear real arithmetic (LRA) and non-linear real arithmetic (NRA) formulas has broad applications, in particular, they are at the heart of logic-related applications such as logic for artificial intelligence, program analysis, etc. While there has been much work on checking satisfiability of unquantified LRA and NRA formulas, the problem of checking satisfiability of quantified LRA and NRA formulas remains a significant challenge. The main bottleneck in the existing methods is a computationally expensive quantifier elimination step. In this work, we propose a novel method for efficient quantifier elimination in quantified LRA and NRA formulas. We propose a template-based Skolemization approach, where we automatically synthesize linear/polynomial Skolem functions in order to eliminate quantifiers in the formula. The key technical ingredient in our approach are Positivstellensätze theorems from algebraic geometry, which allow for an efficient manipulation of polynomial inequalities. Our method offers a range of appealing theoretical properties combined with a strong practical performance. On the theory side, our method is sound, semi-complete, and runs in subexponential time and polynomial space, as opposed to existing sound and complete quantifier elimination methods that run in doubly-exponential time and at least exponential space. On the practical side, our experiments show superior performance compared to state of the art SMT solvers in terms of the number of solved instances and runtime, both on LRA and on NRA benchmarks.
GPU-Accelerated Parallel Bilevel Optimization for Roubst 6G ISAC
Authors: Xingdi Chen; Kai Yang
Venue: AAAI 2025 (main)

Abstract: This paper initiates the first exploratory study to investigate the robust integrated sensing and communication (ISAC) systems under channel estimation errors from the perspective of GPU-Accelerated bilevel optimization. Within this framework, the upper-level problem is dedicated to simultaneously optimizing communication and sensing objectives, quantified respectively by weighted sum rate and Cram\'er-Rao lower bound, while the lower-level problem considers the channel uncertainties. We then propose an efficient algorithm that can find a set of Pareto optimal solutions with different trade-offs among communication rates and sensing accuracy. The theoretical analysis regarding the convergence rate has also been provided. Furthermore, we design a bilevel optimization inspired deep neural network architecture for that can be realized efficiently on GPU platform. Experiments have been conducted to evaluate the performances of proposed methods. In particular, the proposed GPU-accelerated parallel bilevel optimization can accelerate the convergence speed by up to 50 times compared to conventional gradient-based methods. This characteristic renders it especially suitable for real-time applications, exemplified by the demanding requirements of robust ISAC in upcoming 6G networks.
Proof Simulation via Round-based Strategy Extraction for QBF
Authors: Leroy Chew
Venue: AAAI 2025 (main)

Abstract: Proof systems can be used for certification of logic problems, and proof complexity can inform us how succinct certificates can be.  

In the PSPACE complete logic QBF (Quantified Boolean Formulas)  refutation proofs often contain information that reproduce the witnesses of the quantified variables. This is known as strategy extraction. There are two known kinds of strategy extraction for proof systems, local strategy extraction and round-based strategy extraction. Formalisation of local strategy extraction was done previously, in this paper we formalise round-based strategy extraction. 

By formalising the strategy extraction into circuits we can show new p-simulations. P-simulations are processes that allow you to transform proofs from a weaker proof system to a stronger proof system. Thus we solve an open problem in QBF proof complexity that Extended QBF Frege p-simulates LD-Q(\Drrs)-Resolution. LD-Q(\Drrs)-Resolution is the underlying proof system for the solver Qute.

This is a positive result for certification. By clarifying the hierarchy of proof systems further suggests the feasibility of using known formats such as Extended QU-Resolution or QRAT to certify QCDCL solvers.

The p-simulation is our main result, but we also make other observations from the specifics of the formalisation.
Diffusion-based Synthetic Data Generation for Visible-Infrared Person Re-Identification
Authors: Wenbo Dai; Lijing Lu; Zhihang Li
Venue: AAAI 2025 (main)

Abstract: The performance of models is intricately linked to the abundance of training data. In Visible-Infrared person Re-IDentification (VI-ReID) tasks, collecting and annotating large-scale images of each individual under various cameras and modalities is tedious, time-expensive, costly and must comply with data protection laws, posing a severe challenge in meeting dataset requirements. Current research investigates the generation of synthetic data as an efficient and privacy-ensuring alternative to collecting real data in the field. However, a specific data synthesis technique tailored for VI-ReID models has yet to be explored. In this paper, we present a novel data generation framework, dubbed Diffusion-based VI-ReID data Expansion (DiVE), that automatically obtain massive RGB-IR paired images with identity preserving by decoupling identity and modality to improve the performance of VI-ReID models. Specifically, identity representation is acquired from a set of samples sharing the same ID, whereas the modality of images is learned by fine-tuning the Stable Diffusion (SD) on modality-specific data. DiVE extend the text-driven image synthesis to identity-preserving RGB-IR multimodal image synthesis. This approach significantly reduces data collection and annotation costs by directly incorporating synthetic data into ReID model training. Experiments have demonstrated that VI-ReID models trained on synthetic data produced by DiVE consistently exhibit notable enhancements. In particular, the state-of-the-art method, CAJ, trained with synthetic images, achieves an improvement of about 9%  in mAP over the baseline on the LLCM dataset.