United States

Incomplete multi-view clustering (IMVC) has garnered increasing attention in recent years due to the common issue of missing data in multi-view datasets. The primary approach to address this challenge involves recovering the missing views before applying conventional multi-view clustering methods. Although imputation-based IMVC methods have achieved significant improvements, they still encounter notable limitations: 1) heavy reliance  on paired data for training the data recovery module, which is impractical in real scenarios with high missing data rates; 2) the generated data often lacks diversity and discriminability, resulting in suboptimal clustering results. 
To address these shortcomings, we propose a novel IMVC method called Diffusion Contrastive Generation (DCG). Motivated by the consistency between the diffusion and clustering processes, DCG learns the distribution characteristics to enhance clustering by applying forward diffusion and reverse denoising processes to intra-view data.
By performing contrastive learning on a limited  set of paired multi-view samples, DCG can align the generated views with the real views, facilitating accurate recovery of views across arbitrary missing view scenarios. Additionally, DCG integrates instance-level and category-level interactive learning to exploit the consistent and complementary information available in multi-view data, achieving robust and end-to-end clustering. 
Extensive experiments demonstrate that our method outperforms state-of-the-art approaches.

AAAI 2025

Incomplete Multi-view Clustering via Diffusion Contrastive Generation

multi-instance

multi-view learning

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

AAAI 2022

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.



We address the challenging task of neural machine translation (NMT) in the entertainment domain, where the objective is to automatically translate a given dialogue from a source language content to a target language. This task has various applications, particularly in automatic dubbing, subtitling, and other content localization tasks, enabling source content to reach a wider audience. Traditional NMT systems typically translate individual sentences in isolation, without facilitating knowledge transfer of crucial elements such as the \textit{context} and \textit{style} from previously encountered sentences. In this work, we emphasize the significance of these fundamental aspects in producing pertinent and captivating translations. We demonstrate their significance through several examples and propose a novel framework for entertainment translation, which, to our knowledge, is the first of its kind. Furthermore, we introduce an algorithm to estimate the context and style of the current \textit{session} and use these estimations to generate a \textit{prompt} that guides a Large Language Model (LLM) to generate high-quality translations. Our method is both language and LLM-agnostic, making it a general-purpose tool. We demonstrate the effectiveness of our algorithm through various numerical studies and observe significant improvement in the COMET scores over various state-of-the-art LLMs. Moreover, our proposed method consistently outperforms baseline LLMs in terms of win-ratio.

Enhancing Entertainment Translation for Indian Languages Using Adaptive Context, Style and LLMs

We analyze a distributed algorithm to compute a low-rank matrix factorization on $N$ clients, each holding a local dataset $\mathbf{S}^i \in \mathbb{R}^{n_i \times d}$, mathematically, we seek to solve $min_{\mathbf{U}^i \in \mathbb{R}^{n_i\times r}, \mathbf{V}\in \mathbb{R}^{d \times r} } \frac{1}{2} \sum_{i=1}^N \|\mathbf{S}^i - \mathbf{U}^i \mathbf{V}^\top\|^2_{\text{F}}$. Considering a power initialization of $\mathbf{V}$, we rewrite the previous smooth non-convex problem into a smooth strongly-convex problem that we solve using a parallel Nesterov gradient descent potentially requiring a single step of communication at the initialization step. For any client $i$ in $\{1, \dots, N\}$, we obtain a global $\mathbf{V}$ in $\mathbb{R}^{d \times r}$ common to all clients and a local variable $\mathbf{U}^i$ in $\mathbb{R}^{n_i \times r}$. We provide a linear rate of convergence of the excess loss which depends on $\sigma_{\max} / \sigma_{r}$, where $\sigma_{r}$ is the $r^{\mathrm{th}}$ singular value of the concatenation $\mathbf{S}$ of the matrices $(\mathbf{S}^i)^N_{i=1}$. This result improves the rates of convergence given in the literature, which depend on $\sigma_{\max}^2 / \sigma_{\min}^2$. We provide an upper bound on the Frobenius-norm error of reconstruction under the power initialization strategy. We complete our analysis with experiments on both synthetic and real data.

In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting

Transformer-based models have recently achieved outstanding performance in image matting. However, their application to high-resolution images remains challenging due to the quadratic complexity of global self-attention. To address this issue, we propose MEMatte, a memory-efficient matting framework for processing high-resolution images. MEMatte incorporates a router before each global attention block, directing informative tokens to the global attention while routing other tokens to a Lightweight Token Refinement Module (LTRM). Specifically, the router employs a local-global strategy to predict the routing probability of each token, and the LTRM utilizes efficient modules to simulate global attention. Additionally, we introduce a Batch-constrained Adaptive Token Routing (BATR) mechanism, which allows each router to dynamically route tokens based on image content and the stages of attention block in the network. Furthermore, we construct an ultra high-resolution image matting dataset, UHR-395, comprising 35,500 training images and 1,000 test images, with an average resolution of $4872\times6017$. This dataset is created by compositing 395 different alpha mattes across 11 categories onto various backgrounds, all with high-quality manual annotation. Extensive experiments demonstrate that MEMatte outperforms existing methods on both high-resolution and real-world datasets, significantly reducing memory usage by approximately 88\% and latency by 50\% on the Composition-1K benchmark.

Memory Efficient Matting with Adaptive Token Routing

In the era of big data, cross-modal retrieval is increasingly important in research and application. Given the latent complexity and non-intuitive nature of cross-modal relationships, leveraging external knowledge such as large models has become a popular approach to facilitate modality alignment. Existing methods typically address these challenges by fine-tuning model encoders or using a fixed number of prompts. However, these approaches struggle with the significant information asymmetry between image-text pairs and the high distribution diversity of image data. These limitations not only introduce noise during training but also constrain the accuracy and generalization capabilities of these methods in cross-modal retrieval tasks. To address the above issues, this paper proposes Adaptive Prompt-Based Semantic Embedding with Inspired Potential of Implicit Knowledge (APSE-IPIK). On one hand, we propose an inspiring potential strategy to extract fine-grained and multi-perspective text descriptions from large-scale pre-trained multimodal models, which can be seen as implicit knowledge injection. These descriptions, once refined and optimized, are integrated into the visual semantic embedding to balance the information asymmetry between different modalities, thereby reducing the embedding of inaccurate mapping relationships. On the other hand, we construct an instance-level query-based prompt pool to adaptively extract the most relevant prompts, addressing alignment biases caused by intra-modal (especially image) data diversity and improving alignment accuracy. Extensive experiments are conducted on two widely used datasets, Flickr30k and MSCOCO, which show the effectiveness of the proposed method.

Adaptive Prompt-Based Semantic Embedding with Inspire Potential of Implicit Knowledge for Cross-Modal Retrieval

Learning graph generative models over latent spaces has received  less attention compared to models that operate on the original data space and has so far demonstrated lacklustre performance. We present GLAD a latent space graph generative model. Unlike most previous latent space graph generative models, GLAD operates on a discrete latent space that preserves to a significant extent the discrete nature of the graph structures making no unnatural assumptions such as latent space continuity. We learn the prior of our discrete latent space by adapting diffusion bridges to its structure. By operating over an appropriately constructed latent space we avoid relying on decompositions that are often used in models that operate in the original data space. We present experiments on a series of graph benchmark datasets that demonstrates GLAD as the first equivariant latent graph generative method achieves competitive performance with the state of the art baselines.

GLAD: Improving Latent Graph Generative Modeling with Simple Quantization

ControlNet has significantly advanced controllable image generation by integrating dense conditions (such as depth and canny edges) with text-to-image diffusion models. However, ControlNet's integration requires an additional amount nearly equal to half of the base diffusion model's parameters, making it inefficient. To address this, we introduce Simple-ControlNet, an efficient and streamlined network for controllable text-to-image generation. It employs a single-scale projection layer to incorporate condition information into the denoising U-Net. It is supplemented by Low-Rank Adapter (LoRA) parameters to facilitate condition learning. Impressively, Simple-ControlNet requires fewer than 3 million parameters for the control mechanism—substantially less than the 300 million needed by ControlNet. Our extensive experiments confirm that Simple-ControlNet matches and surpasses ControlNet's performance across a broad range of tasks and base diffusion models, showcasing its utility and efficiency. All pre-trained models will be made available to the open-source community.

Simplifying Control Mechanism in Text-to-Image Diffusion Models

We present InstantSticker, a disentangled reconstruction pipeline based on Image-Based Lighting (IBL), which focuses on highly realistic decal blending, simulates stickers attached to the reconstructed surface, and allows for instant editing and real-time rendering. To achieve stereoscopic impression of the decal, we introduce shadow factor into IBL, which can be adaptively optimized during training. This allows the shadow brightness of surfaces to be accurately decomposed rather than baked into the diffuse color, ensuring that the edited texture exhibits authentic shading. To address the issues of warping and blurriness in previous methods, we apply As-Rigid-As-Possible (ARAP) parameterization to pre-unfold a specified area of the mesh and use the local UV mapping combined with a neural texture map to enhance the ability to express high-frequency details in that area. For instant editing, we utilize the Disney BRDF model, explicitly defining material colors with 3-channel diffuse albedo. This enables instant replacement of albedo RGB values during the editing process, avoiding the prolonged optimization required in previous approaches. In our experiment, we introduce the Ratio Variance Warping (RVW) metric to evaluate the local geometric warping of the decal area. Extensive experimental results demonstrate that our method surpasses previous decal blending methods in terms of editing quality, editing speed and rendering speed, achieving the state-of-the-art.

InstantSticker: Realistic Decal Blending via Disentangled Object Reconstruction

World models have demonstrated superiority in autonomous driving, particularly in the generation of multi-view driving videos. However, significant challenges still exist in generating customized driving videos. In this paper, we propose DriveDreamer-2, which incorporates a Large Language Model (LLM) to facilitate the creation of user-defined driving videos. Specifically, a trajectory generation function library is developed to produce trajectories that conform to user descriptions. Subsequently, an HDMap generator is designed to learn the mapping from trajectories to road structures. Ultimately, we propose the Unified Multi-View Model (UniMVM) to enhance temporal and spatial coherence in the generated multi-view driving videos. To the best of our knowledge, DriveDreamer-2 is the first world model to generate customized driving videos, and it can generate uncommon driving videos (e.g., vehicles abruptly cut in) in a user-friendly manner. Besides, experimental results demonstrate that the generated videos enhance the training of driving perception methods (e.g., 3D detection and tracking). Furthermore, video generation quality of DriveDreamer-2 surpasses other state-of-the-art methods, showcasing FID and FVD scores of 11.2 and 55.7, representing relative improvements of $\sim$30\% and $\sim$50\%.

DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation

Recent research on LiDAR-based 3D object detectors has shown strong performance; however, evaluations typically focus on dominant classes, overlooking rare classes, such as strollers, which could be critical in real autonomous driving scenarios. This oversight is problematic because state-of-the-art 3D object detectors show significantly lower performance on rare classes compared to dominant ones when trained on both. To address this issue and achieve accurate 3D rare object detection using only LiDAR data, we propose the Neighbor-Based confidence Adjustment for 3D rare class predictions (NBA3D). NBA3D utilizes a graph neural network to analyze the surrounding environment of rare class prediction boxes, enabling more effective distinction between true positives and false positives based on their local context. Our approach leverages both 3D prediction box characteristics and CLIP-based class semantic information to better contextualize neighboring objects. Various experiments demonstrate that NBA3D effectively improves the detection performance of rare class objects, regardless of the type of 3D object detectors used.

NBA3D: Neighbor-Based Confidence Adjustment for 3D Rare Object Detection Using LiDAR

The imperative for texture compression emerges from the critical demand for high-quality rendering, which necessitates sophisticated textures that, in turn, require substantial storage and memory resources. Thus, low-bitrate compression is crucial, especially in modern games demanding higher texture resolutions. Concurrent methodologies in texture compression predominantly employ a block-based paradigm based on color space, which inevitably leads to representational redundancies and a limited compression scope, particularly at lower bitrates. In the context of mobile devices, bandwidth during texture loading and runtime memory are major bottlenecks, making existing compression algorithms inadequate for high-resolution textures. To mitigate these limitations, we propose a novel multi-resolution texture compression scheme, Neural Block Compression (NBC), developed within the neural feature domain. Our encoding scheme is constructed on a hierarchy of multi-resolution neural feature blocks, and the key ingredient is the variable bitrates quantization scheme. This scheme allocates higher bitrates to higher feature mip-levels and lower bitrates to lower feature mip-levels, thereby extending the concept of block compression from color domain into neural feature domain. Extensive experiments demonstrate the superior texture compression quality achieved by the proposed scheme, especially at low bitrates.

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Enhancing Entertainment Translation for Indian Languages Using Adaptive Context, Style and LLMs

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