arXiv subjects

Chenwei Xie

Publications and source records attributed to Chenwei Xie.

10 recordsLinked to original sources

arXiv2026

DySkew: Dynamic Data Redistribution for Skew-Resilient Snowpark UDF Execution

Snowflake revolutionized data warehousing with an elastic architecture that decouples compute and storage, enabling scalable solutions for diverse data analytics needs. Building on this foundation, Snowflake has advanced its AI Data Cloud vision by introducing Snowpark, a managed turnkey solution that supports data engineering and AI/ML workloads using Python and other programming languages. While Snowpark's User-Defined Function (UDF) execution model offers high throughput, it is highly vulnerable to performance degradation from data skew, where uneven data partitioning causes straggler tasks and unpredictable latency. The non-uniform computational cost of arbitrary user code further exacerbates this classic challenge. This paper presents DySkew, a novel, data-skew-aware execution strategy for Snowpark UDFs. Built upon Snowflake's new generalized skew handling solution, an adaptive data distribution mechanism utilizing per-link state machines. DySkew addresses the unique challenges of user-defined logic with goals of fine-grained per-row mitigation, dynamic runtime adaptation, and low-overhead, cost-aware redistribution. Specifically, for Snowpark, we introduce crucial optimizations, including an eager redistribution strategy and a Row Size Model to dynamically manage overhead for extremely large rows. This dynamic approach replaces the limitations of the previous static round-robin method. We detail the architecture of this framework and showcase its effectiveness through performance evaluations and real-world case studies, demonstrating significant improvements in the execution time and resource utilization for large-scale Snowpark UDF workloads.

cs.DC↗

arXiv2026

Wan-Weaver: Interleaved Multi-modal Generation via Decoupled Training

Recent unified models have made unprecedented progress in both understanding and generation. However, while most of them accept multi-modal inputs, they typically produce only single-modality outputs. This challenge of producing interleaved content is mainly due to training data scarcity and the difficulty of modeling long-range cross-modal context. To address this issue, we decompose interleaved generation into textual planning and visual consistency modeling, and introduce a framework consisting of a planner and a visualizer. The planner produces dense textual descriptions for visual content, while the visualizer synthesizes images accordingly. Under this guidance, we construct large-scale textual-proxy interleaved data (where visual content is represented in text) to train the planner, and curate reference-guided image data to train the visualizer. These designs give rise to Wan-Weaver, which exhibits emergent interleaved generation ability with long-range textual coherence and visual consistency. Meanwhile, the integration of diverse understanding and generation data into planner training enables Wan-Weaver to achieve robust task reasoning and generation proficiency. To assess the model's capability in interleaved generation, we further construct a benchmark that spans a wide range of use cases across multiple dimensions. Extensive experiments demonstrate that, even without access to any real interleaved data, Wan-Weaver achieves superior performance over existing methods.

cs.CV↗

arXiv2025

SEE++: Evolving Snowpark Execution Environment for Modern Workloads

Snowpark enables Data Engineering and AI/ML workloads to run directly within Snowflake by deploying a secure sandbox on virtual warehouse nodes. This Snowpark Execution Environment (SEE) allows users to execute arbitrary workloads in Python and other languages in a secure and performant manner. As adoption has grown, the diversity of workloads has introduced increasingly sophisticated needs for sandboxing. To address these evolving requirements, Snowpark transitioned its in-house sandboxing solution to gVisor, augmented with targeted optimizations. This paper describes both the functional and performance objectives that guided the upgrade, outlines the new sandbox architecture, and details the challenges encountered during the journey, along with the solutions developed to resolve them. Finally, we present case studies that highlight new features enabled by the upgraded architecture, demonstrating SEE's extensibility and flexibility in supporting the next generation of Snowpark workloads.

cs.DB↗

arXiv2025

Snowpark: Performant, Secure, User-Friendly Data Engineering and AI/ML Next To Your Data

Snowflake revolutionized data analytics with an elastic architecture that decouples compute and storage, enabling scalable solutions supporting data architectures like data lake, data warehouse, data lakehouse, and data mesh. Building on this foundation, Snowflake has advanced its AI Data Cloud vision by introducing Snowpark, a managed turnkey solution that supports data engineering and AI and ML workloads using Python and other programming languages. This paper outlines Snowpark's design objectives towards high performance, strong security and governance, and ease of use. We detail the architecture of Snowpark, highlighting its elastic scalability and seamless integration with Snowflake core compute infrastructure. This includes leveraging Snowflake control plane for distributed computing and employing a secure sandbox for isolating Snowflake SQL workloads from Snowpark executions. Additionally, we present core innovations in Snowpark that drive further performance enhancements, such as query initialization latency reduction through Python package caching, improved workload scheduling for customized workloads, and data skew management via efficient row redistribution. Finally, we showcase real-world case studies that illustrate Snowpark's efficiency and effectiveness for large-scale data engineering and AI and ML tasks.

cs.DC↗

arXiv2025

UniLiP: Adapting CLIP for Unified Multimodal Understanding, Generation and Editing

In this paper, we propose UniLIP, a unified framework that adapts CLIP for multimodal understanding, generation and editing. Although CLIP excels at understanding, it lacks reconstruction abilities required to be a unified visual encoder. However, previous CLIP-based unified methods fail to balance understanding and reconstruction, leading to semantic degradation or inconsistent reconstructions. In contrast, we introduce a novel two-stage training scheme with a self-distillation strategy that progressively endows CLIP with high-fidelity reconstruction abilities while preserving its original comprehension performance. For enhanced reasoning and consistency in generation and editing, we further develop a dual-condition architecture built upon the MetaQuery framework. Our architecture jointly utilizes multimodal hidden states for rich contextual details and learnable query embeddings to harness the powerful reasoning abilities of Multimodal Large Language Models (MLLMs). Leveraging advanced image representation and architectural design, UniLIP demonstrates superior instruction following and edit fidelity. With only 1B and 3B parameters, UniLIP can outperform larger unified models such as BAGEL (7B) and Uniworld-V1 (12B), achieving state-of-the-art performance of 0.90 on GenEval, 0.63 on WISE, and 3.94 on ImgEdit. These results demonstrate that UniLIP successfully expands the application of CLIP, establishing its continuous features to not only serve as the optimal choice for understanding tasks but also achieve highly competitive performance in generation and editing tasks. Code and models are available at https://github.com/nnnth/UniLIP.

cs.CV↗

arXiv2025

DynImg: Key Frames with Visual Prompts are Good Representation for Multi-Modal Video Understanding

In recent years, the introduction of Multi-modal Large Language Models (MLLMs) into video understanding tasks has become increasingly prevalent. However, how to effectively integrate temporal information remains a critical research focus. Traditional approaches treat spatial and temporal information separately. Due to issues like motion blur, it is challenging to accurately represent the spatial information of rapidly moving objects. This can lead to temporally important regions being underemphasized during spatial feature extraction, which in turn hinders accurate spatio-temporal interaction and video understanding. To address this limitation, we propose an innovative video representation method called Dynamic-Image (DynImg). Specifically, we introduce a set of non-key frames as temporal prompts to highlight the spatial areas containing fast-moving objects. During the process of visual feature extraction, these prompts guide the model to pay additional attention to the fine-grained spatial features corresponding to these regions. Moreover, to maintain the correct sequence for DynImg, we employ a corresponding 4D video Rotary Position Embedding. This retains both the temporal and spatial adjacency of DynImg, helping MLLM understand the spatio-temporal order within this combined format. Experimental evaluations reveal that DynImg surpasses the state-of-the-art methods by approximately 2% across multiple video understanding benchmarks, proving the effectiveness of our temporal prompts in enhancing video comprehension.

cs.CV↗

arXiv2025

UFO: A Unified Approach to Fine-grained Visual Perception via Open-ended Language Interface

Generalist models have achieved remarkable success in both language and vision-language tasks, showcasing the potential of unified modeling. However, effectively integrating fine-grained perception tasks like detection and segmentation into these models remains a significant challenge. This is primarily because these tasks often rely heavily on task-specific designs and architectures that can complicate the modeling process. To address this challenge, we present \ours, a framework that \textbf{U}nifies \textbf{F}ine-grained visual perception tasks through an \textbf{O}pen-ended language interface. By transforming all perception targets into the language space, \ours unifies object-level detection, pixel-level segmentation, and image-level vision-language tasks into a single model. Additionally, we introduce a novel embedding retrieval approach that relies solely on the language interface to support segmentation tasks. Our framework bridges the gap between fine-grained perception and vision-language tasks, significantly simplifying architectural design and training strategies while achieving comparable or superior performance to methods with intricate task-specific designs. After multi-task training on five standard visual perception datasets, \ours outperforms the previous state-of-the-art generalist models by 12.3 mAP on COCO instance segmentation and 3.3 mIoU on ADE20K semantic segmentation. Furthermore, our method seamlessly integrates with existing MLLMs, effectively combining fine-grained perception capabilities with their advanced language abilities, thereby enabling more challenging tasks such as reasoning segmentation. Code and models are available at https://github.com/nnnth/UFO.

cs.CV↗

arXiv2024

ACE: All-round Creator and Editor Following Instructions via Diffusion Transformer

Diffusion models have emerged as a powerful generative technology and have been found to be applicable in various scenarios. Most existing foundational diffusion models are primarily designed for text-guided visual generation and do not support multi-modal conditions, which are essential for many visual editing tasks. This limitation prevents these foundational diffusion models from serving as a unified model in the field of visual generation, like GPT-4 in the natural language processing field. In this work, we propose ACE, an All-round Creator and Editor, which achieves comparable performance compared to those expert models in a wide range of visual generation tasks. To achieve this goal, we first introduce a unified condition format termed Long-context Condition Unit (LCU), and propose a novel Transformer-based diffusion model that uses LCU as input, aiming for joint training across various generation and editing tasks. Furthermore, we propose an efficient data collection approach to address the issue of the absence of available training data. It involves acquiring pairwise images with synthesis-based or clustering-based pipelines and supplying these pairs with accurate textual instructions by leveraging a fine-tuned multi-modal large language model. To comprehensively evaluate the performance of our model, we establish a benchmark of manually annotated pairs data across a variety of visual generation tasks. The extensive experimental results demonstrate the superiority of our model in visual generation fields. Thanks to the all-in-one capabilities of our model, we can easily build a multi-modal chat system that responds to any interactive request for image creation using a single model to serve as the backend, avoiding the cumbersome pipeline typically employed in visual agents. Code and models will be available on the project page: https://ali-vilab.github.io/ace-page/.

cs.CV↗

arXiv2024

ESND: An Embedding-based Framework for Signed Network Dismantling

Network dismantling aims to maximize the disintegration of a network by removing a specific set of nodes or edges and is applied to various tasks in diverse domains, such as cracking down on crime organizations, delaying the propagation of rumors, and blocking the transmission of viruses. Most of the current network dismantling methods are tailored for unsigned networks, which only consider the connection between nodes without evaluating the nature of the relationships, such as friendship/hostility, enhancing/repressing, and trust/distrust. We here propose an embedding-based algorithm, namely ESND, to solve the signed network dismantling problem. The algorithm generally iterates the following four steps, i.e., giant component detection, network embedding, node clustering, and removal node selection. To illustrate the efficacy and stability of ESND, we conduct extensive experiments on six signed network datasets as well as null models, and compare the performance of our method with baselines. Experimental results consistently show that the proposed ESND is superior to the baselines and displays stable performance with the change in the network structure. Additionally, we examine the impact of sign proportions on network robustness via ESND, observing that networks with a high ratio of negative edges are generally easier to dismantle than networks with high positive edges.

physics.soc-ph↗

arXiv2024

Directed network comparison using motifs

Analyzing and characterizing the differences between networks is a fundamental and challenging problem in network science. Previously, most network comparison methods that rely on topological properties have been restricted to measuring differences between two undirected networks. However, many networks, such as biological networks, social networks, and transportation networks, exhibit inherent directionality and higher-order attributes that should not be ignored when comparing networks. Therefore, we propose a motif-based directed network comparison method that captures local, global, and higher-order differences between two directed networks. Specifically, we first construct a motif distribution vector for each node, which captures the information of a node's involvement in different directed motifs. Then, the dissimilarity between two directed networks is defined on the basis of a matrix which is composed of the motif distribution vector of every node and Jensen-Shannon divergence. The performance of our method is evaluated via the comparison of six real directed networks with their null models as well as their perturbed networks based on edge perturbation. Our method is superior to the state-of-the-art baselines and is robust with different parameter settings.

physics.soc-ph↗