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As a long-range prior, motion consensus essentially forces the overall spatial transformation between a pair of images to be smooth and consistent, which is naturally well-suited for two-view correspondence learning. However, such precious property remains under-explored by most existing studies due to the modeling challenges posed by the sparsity and uneven distributions of putative correspondences. In this paper, we propose DeMo, a novel and cutting-edge network for outlier rejection, which possesses the capacity to fully capture global motion consensus clues by way of consensus interpolation over the entire high-dimensional motion field generated by putative correspondences. Specifically, through incorporating regularization techniques into a Reproducing Kernel Hilbert Space (RKHS), a concise interpolation formula can be derived for the high-dimensional motion field, which inherently allows a closed-form solution. Subsequently, learnable deep kernels are collaboratively used to flexibly and efficiently capture the relationships between global inputs, thus maintaining the entire motion field consensus. In addition, to remedy the $\mathcal{O}(N^3)$ computational overhead of explicit interpolation, a scene-adaptive sampling strategy is introduced, which implicitly selects the more scene-representative motions, reducing the computational complexity of motion consensus interpolation to be approximately linear while maintaining the accuracy. Moreover, to deal with underlying depth discontinuities caused by complicated scene variations, a local consensus complementation block is designed, which maintains local bilateral consensus across both feature and spatial channels. Without bells and whistles, DeMo achieves superior performance in various geometric tasks, including relative pose estimation, homography estimation, and visual localization.

AAAI 2025

DeMo: Deep Motion Field Consensus with Learnable Kernels for Two-view Correspondence Learning

motion tracking

poster

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



Recent advancements in text-to-3D generation can generate neural radiance fields (NeRFs) with score distillation sampling, enabling 3D asset creation without real-world data capture. With the rapid advancement in NeRF generation quality, protecting the copyright of the generated NeRF has become increasingly important. While prior works can watermark NeRFs in a post-generation way, they suffer from two vulnerabilities. First, a delay lies between NeRF generation and watermarking because the secret message is embedded into the NeRF model post-generation through fine-tuning. Second, generating a non-watermarked NeRF as an intermediate creates a potential vulnerability for theft. To address both issues, we propose Dreamark to embed a secret message by backdooring the NeRF during NeRF generation. In detail, we first pre-train a watermark decoder. Then, the Dreamark generates backdoored NeRFs in a way that the target secret message can be verified by the pre-trained watermark decoder on an arbitrary trigger viewport. We evaluate the generation quality and watermark robustness against image- and model-level attacks. Extensive experiments show that the watermarking process will not degrade the generation quality, and the watermark achieves 90+% accuracy among both image-level attacks (e.g., Gaussian noise) and model-level attacks (e.g., pruning attack).

DreaMark: Rooting Watermark in Score Distillation Sampling Generated Neural Radiance Fields

Mitochondria segmentation from electron microscopy (EM) images plays a crucial role in biological and medical research. However, models trained on source domains often suffer from performance degradation when applied to target domains due to domain shift. Unsupervised domain adaptation (UDA) methods have been proposed to address this issue, but they often overlook the reliability of pseudo-labels and the effectiveness of supervision signals. In this paper, we propose R4MITO, a novel UDA framework for robust mitochondria segmentation. First, we introduce Reliable Prototype Pseudo-labels to mitigate the inconsistency of class-level features between across domains by leveraging source prototypes to model target prototypes. Second, we devise Correlation-wise Consistency Regularization to exploit inter-pixel correlations, aligning agent-level correlations under various perturbations. Third, we propose Rank-aware Relationship Consistency Regularization to fully utilize the rich information encoded in inter-agent relationships by imposing rank-aware constraints on agent-ranking probability distributions. Extensive experiments on multiple EM datasets demonstrate the superiority of our R4MITO over existing state-of-the-art UDA methods for mitochondria segmentation.

Alleviate and Mining: Rethinking Unsupervised Domain Adaptation for Mitochondria Segmentation from Pseudo-Label Perspective

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 $Di$ffusion-based $V$I-ReID data $E$xpansion ($DiVE$), 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.

Diffusion-based Synthetic Data Generation for Visible-Infrared Person Re-Identification

To facilitate understanding of users' diverse queries against the back-end databases in web applications, researchers have introduced Text-to-SQL models that can generate well-structured SQL queries from users' query texts in natural language. As the Text-to-SQL model decouples the user queries with the back-end databases, it inherently mitigates the SQL injection risk posed by inserting users' input into pre-written SQL queries. However, what security risks to web applications may be posed by Text-to-SQL models remains an open question. In this paper, we present a new attack framework, named Autoregression-based Injection Attacks (AIA), to evaluate the security risks of Text-to-SQL models. In particular, AIA makes target models generate attack payloads by constructing specific inputs and adjusting the input auto-regressively. Our evaluation demonstrates that AIA can cause Text-to-SQL models to generate target output by adversarial inputs with success rates of over 70\% in most scenarios. The generated adversarial input has certain transferability in target Text-to-SQL models. Additionally, practice experiments show that AIA can make Text-to-SQL models extract administrator user lists from databases and even delete data in databases directly.

AIA: Autoregression-Based Injection Attacks Against Text2SQL Models

Recently, there has been a focus on the streaming setting in a line of works on the Multi-Armed Bandit (MAB). In this scenario, a large number of arms arrive in a streaming manner, and the algorithm scans through the stream and stores some arms in its limited processing memory. We advance this line of research by introducing the Linear Streaming Bandit setup, where the arriving arms have profile vectors observable to the algorithm. The profile of an arm has a linear correlation with the expected reward. This setup is motivated by real-world applications, such as when a company or a crowdsourcing platform hires a worker from many sequentially arriving applicants with their resumes. We address two problems in this setup: Regret Minimization and Fixed-Budget $\epsilon$-Best Arm Identification. For the former, we propose an algorithm whose regret is independent of the number of arms, thus it is able to handle arbitrarily long arm streams. For the latter, we present a multi-pass algorithm whose error probability is sub-linear w.r.t the number of arms, and an algorithm identifying the exact best arm in only a single pass. We validate the effectiveness of all proposed algorithms through experiments on both synthetic and real-world datasets.

Linear Streaming Bandit: Regret Minimization and Fixed-Budget $\epsilon$-Best Arm Identification

As the demands for superior agents grow, the training complexity of Deep Reinforcement Learning (DRL) becomes higher. Thus, accelerating training of DRL has become a major research focus. Dividing the DRL training process into sub-tasks and using parallel computation can effectively reduce training costs. However, current DRL training systems lack sufficient parallelization due to data assignment between sub-task components. This assignment issue has been ignored, but addressing it can further boost training efficiency. Therefore, we propose a high-throughput distributed RL training system called TianJi. It relaxes assignment dependencies between sub-task components and enables event-driven asynchronous communication. Meanwhile, TianJi maintains clear boundaries between sub-task components. To address convergence uncertainty from relaxed assignment dependencies, TianJi proposes a distributed strategy based on the balance of sample production and consumption. The strategy controls the staleness of samples to correct their quality, ensuring convergence. We conducted extensive experiments. TianJi achieves a convergence time acceleration ratio of up to 4.37 compared to related comparison frameworks. When scaled to eight computational nodes, TianJi shows a convergence time speedup of 1.6 and a throughput speedup of 7.13 relative to XingTian, emonstrating its capability to accelerate training and scalability. In data transmission efficiency experiments, TianJi significantly outperforms other frameworks, approaching hardware limits. TianJi also shows effectiveness in on-policy algorithms, achieving convergence time acceleration ratios of 4.36 and 2.95 compared to RLlib and XingTian.

Highly Parallelized Reinforcement Learning Training with Relaxed Assignment Dependencies

Neural networks remain as black-box systems, {\em unsure} about their outputs, and their performances may drop unpredictably in real applications. An open question is how to qualitatively extend neural networks that are {\em sure} about their reasoning results, or {\em reason for sure}. In symbolic logic, the validity of {\em reasoning} is guaranteed by {\em a proof} that is a sequence of true-false statements, either assumed true in the task or deduced from previous statements, where statements are either features of or relations between/among entities. 
Here, we introduce set-theoretic relations explicitly and seamlessly into neural networks by extending vector embedding into a sphere, so that part-whole relations can explicitly encode set-theoretic relations through sphere boundaries. {\em A neural proof} turns out to be a process of model construction in the form of a sequential transformation from the premise to the conclusion sphere configurations. We propose the criterion of {\em neural reasoning for sure} and apply it to Aristotelian syllogistic reasoning, the fundamental reasoning system. We implement 
Hyperbolic Sphere Neural Network (HSphNN), the first neural network that has a theoretical proof of reasoning {\em for sure} with syllogistic statements. In experiments, HSphNN reached the {\em reason-for-sure} criterion for all types of syllogistic reasoning and successfully checked both decisions and explanations of ChatGPT (gpt-3.5-turbo and gpt-4o). Through sending feedback using prompts, HSphNN improved the performance of ChatGPT-3.5-turbo from 42.19\% to 63.28\%, and of ChatGPT-4o from 82.42\% to 85.16\%. We show ways to extend HSphNN for logical reasoning and statistical reasoning, and to seamlessly integrate with traditional neural networks.

Neural Reasoning for Sure Through Constructing Explainable Models

Grasping the intricacies of human motion, which involve perceiving spatio-temporal dependence and multi-scale effects, is essential for predicting human motion.
While humans inherently possess the requisite skills to navigate this issue, it proves to be markedly more challenging for machines to emulate. 
To bridge the gap, we propose the Human-like Vision and Inference System (HVIS) for human motion prediction, which is designed to emulate human observation and forecast future movements.
HVIS comprises two components: the human-like vision encode (HVE) module and the human-like motion inference (HMI) module.
The HVE module mimics and refines the human visual process, incorporating a retina-analog component that captures spatiotemporal information separately to avoid unnecessary crosstalk. Additionally,  a visual cortex-analogy component is designed to hierarchically extract and treat complex motion features, focusing on both global and local features of human poses.
The HMI is employed to simulate the multi-stage learning model of the human brain. The spontaneous learning network simulates the neuronal fracture generation process for the adversarial generation of future motions. Subsequently, the deliberate learning network is optimized for hard-to-train joints to prevent misleading learning.
Experimental results demonstrate that our method achieves new state-of-the-art performance, significantly outperforming existing methods by 19.8\% on Human3.6M, 15.7\% on CMU Mocap, and 11.1\% on G3D. Our code is anonymously released.

HVIS: A Human-like Vision and Inference System for Human Motion Prediction

Federated learning is often used in environments with many unverified participants. Therefore, federated learning under adversarial attacks receives significant attention. This paper proposes an algorithmic framework for list-decodable federated learning, where a central server maintains a list of models, with at least one guaranteed to perform well. The framework has no strict restriction on the fraction of honest workers, extending the applicability of Byzantine federated learning to the scenario with more than half adversaries. Under proper assumptions on the loss function, we prove a convergence theorem for our method. Experimental results, including image classification tasks with both convex and non-convex losses, demonstrate that the proposed algorithm can withstand the malicious majority under various attacks.

LiD-FL: Towards List-Decodable Federated Learning

Backward error analysis allows finding a modified loss function, which the parameter updates really follow under the influence of an optimization method. The additional loss terms included in this modified function is called implicit regularizer. In this paper, we attempt to find the implicit regularizer for various federated learning algorithms on non-IID data distribution, and explain why each method shows different convergence behavior. We first show that the implicit regularizer of FedAvg disperses the gradient of each client from the average gradient, thus increasing the gradient variance. We also empirically show that the implicit regularizer hampers its convergence. Similarly, we compute the implicit regularizers of FedSAM and SCAFFOLD, and explain why they converge better. While existing convergence analyses only point out the advantages of FedSAM and SCAFFOLD, our approach can explain their limitations in complex non-convex settings. In specific, we demonstrate that FedSAM can partially remove the bias in the first-order term of the implicit regularizer in FedAvg, whereas SCAFFOLD can fully eliminate the bias in the first-order term, but not in the second-order term. Consequently, the implicit regularizer can provide a useful insight on the convergence behavior of federated learning from a different theoretical perspective.

DeMo: Deep Motion Field Consensus with Learnable Kernels for Two-view Correspondence Learning

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DreaMark: Rooting Watermark in Score Distillation Sampling Generated Neural Radiance Fields

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