arXiv subjects

Zijun Li

Publications and source records attributed to Zijun Li.

31 records · Page 2Linked to original sources

arXiv2025

TMCIR: Token Merge Benefits Composed Image Retrieval

Composed Image Retrieval (CIR) retrieves target images using a multi-modal query that combines a reference image with text describing desired modifications. The primary challenge is effectively fusing this visual and textual information. Current cross-modal feature fusion approaches for CIR exhibit an inherent bias in intention interpretation. These methods tend to disproportionately emphasize either the reference image features (visual-dominant fusion) or the textual modification intent (text-dominant fusion through image-to-text conversion). Such an imbalanced representation often fails to accurately capture and reflect the actual search intent of the user in the retrieval results. To address this challenge, we propose TMCIR, a novel framework that advances composed image retrieval through two key innovations: 1) Intent-Aware Cross-Modal Alignment. We first fine-tune CLIP encoders contrastively using intent-reflecting pseudo-target images, synthesized from reference images and textual descriptions via a diffusion model. This step enhances the encoder ability of text to capture nuanced intents in textual descriptions. 2) Adaptive Token Fusion. We further fine-tune all encoders contrastively by comparing adaptive token-fusion features with the target image. This mechanism dynamically balances visual and textual representations within the contrastive learning pipeline, optimizing the composed feature for retrieval. Extensive experiments on Fashion-IQ and CIRR datasets demonstrate that TMCIR significantly outperforms state-of-the-art methods, particularly in capturing nuanced user intent.

cs.CV↗

arXiv2025

Language-Depth Navigated Thermal and Visible Image Fusion

Depth-guided multimodal fusion combines depth information from visible and infrared images, significantly enhancing the performance of 3D reconstruction and robotics applications. Existing thermal-visible image fusion mainly focuses on detection tasks, ignoring other critical information such as depth. By addressing the limitations of single modalities in low-light and complex environments, the depth information from fused images not only generates more accurate point cloud data, improving the completeness and precision of 3D reconstruction, but also provides comprehensive scene understanding for robot navigation, localization, and environmental perception. This supports precise recognition and efficient operations in applications such as autonomous driving and rescue missions. We introduce a text-guided and depth-driven infrared and visible image fusion network. The model consists of an image fusion branch for extracting multi-channel complementary information through a diffusion model, equipped with a text-guided module, and two auxiliary depth estimation branches. The fusion branch uses CLIP to extract semantic information and parameters from depth-enriched image descriptions to guide the diffusion model in extracting multi-channel features and generating fused images. These fused images are then input into the depth estimation branches to calculate depth-driven loss, optimizing the image fusion network. This framework aims to integrate vision-language and depth to directly generate color-fused images from multimodal inputs.

cs.CV↗

arXiv2025

SymmCompletion: High-Fidelity and High-Consistency Point Cloud Completion with Symmetry Guidance

Point cloud completion aims to recover a complete point shape from a partial point cloud. Although existing methods can form satisfactory point clouds in global completeness, they often lose the original geometry details and face the problem of geometric inconsistency between existing point clouds and reconstructed missing parts. To tackle this problem, we introduce SymmCompletion, a highly effective completion method based on symmetry guidance. Our method comprises two primary components: a Local Symmetry Transformation Network (LSTNet) and a Symmetry-Guidance Transformer (SGFormer). First, LSTNet efficiently estimates point-wise local symmetry transformation to transform key geometries of partial inputs into missing regions, thereby generating geometry-align partial-missing pairs and initial point clouds. Second, SGFormer leverages the geometric features of partial-missing pairs as the explicit symmetric guidance that can constrain the refinement process for initial point clouds. As a result, SGFormer can exploit provided priors to form high-fidelity and geometry-consistency final point clouds. Qualitative and quantitative evaluations on several benchmark datasets demonstrate that our method outperforms state-of-the-art completion networks.

cs.CV↗

arXiv2025

Efficient Function-as-a-Service for Large Language Models with TIDAL

Large Language Model (LLM) applications have emerged as a prominent use case for Function-as-a-Service (FaaS) due to their high computational demands and sporadic invocation patterns. However, serving LLM functions within FaaS frameworks faces significant GPU-side cold start. A fundamental approach involves leveraging a template with function state saved on GPUs to bypass the cold start for new invocations. Yet, this approach struggles with the high GPU footprint, dynamic initialization behaviors, and lazy GPU kernel loading inherent in LLM functions, primarily due to a lack of insight into the underlying execution details. In this paper, we introduce TIDAL, an optimized FaaS framework for LLM applications that achieves fast startups by tracing fine-grained execution paths. By utilizing the traced execution details, TIDAL generates adaptive function templates, effectively breaking startup barriers for LLM functions. Extensive evaluations demonstrate that TIDAL reduces cold start latency by $1.79\times\text{\textasciitilde}2.11\times$ and improves the $95\%$-ile time-to-first-token by $76.0\%$, surpassing state-of-the-art methods.

cs.OS↗

arXiv2025

Multi-modal expressive personality recognition in data non-ideal audiovisual based on multi-scale feature enhancement and modal augment

Automatic personality recognition is a research hotspot in the intersection of computer science and psychology, and in human-computer interaction, personalised has a wide range of applications services and other scenarios. In this paper, an end-to-end multimodal performance personality is established for both visual and auditory modal datarecognition network , and the through feature-level fusion , which effectively of the two modalities is carried out the cross-attention mechanismfuses the features of the two modal data; and a is proposed multiscale feature enhancement modalitiesmodule , which enhances for visual and auditory boththe expression of the information of effective the features and suppresses the interference of the redundant information. In addition, during the training process, this paper proposes a modal enhancement training strategy to simulate non-ideal such as modal loss and noise interferencedata situations , which enhances the adaptability ofand the model to non-ideal data scenarios improves the robustness of the model. Experimental results show that the method proposed in this paper is able to achieve an average Big Five personality accuracy of , which outperforms existing 0.916 on the personality analysis dataset ChaLearn First Impressionother methods based on audiovisual and audio-visual both modalities. The ablation experiments also validate our proposed , respectivelythe contribution of module and modality enhancement strategy to the model performance. Finally, we simulate in the inference phase multi-scale feature enhancement six non-ideal data scenarios to verify the modal enhancement strategy's improvement in model robustness.

cs.SD↗

arXiv2024

ConDo: Continual Domain Expansion for Absolute Pose Regression

Visual localization is a fundamental machine learning problem. Absolute Pose Regression (APR) trains a scene-dependent model to efficiently map an input image to the camera pose in a pre-defined scene. However, many applications have continually changing environments, where inference data at novel poses or scene conditions (weather, geometry) appear after deployment. Training APR on a fixed dataset leads to overfitting, making it fail catastrophically on challenging novel data. This work proposes Continual Domain Expansion (ConDo), which continually collects unlabeled inference data to update the deployed APR. Instead of applying standard unsupervised domain adaptation methods which are ineffective for APR, ConDo effectively learns from unlabeled data by distilling knowledge from scene-agnostic localization methods. By sampling data uniformly from historical and newly collected data, ConDo can effectively expand the generalization domain of APR. Large-scale benchmarks with various scene types are constructed to evaluate models under practical (long-term) data changes. ConDo consistently and significantly outperforms baselines across architectures, scene types, and data changes. On challenging scenes (Fig.1), it reduces the localization error by >7x (14.8m vs 1.7m). Analysis shows the robustness of ConDo against compute budgets, replay buffer sizes and teacher prediction noise. Comparing to model re-training, ConDo achieves similar performance up to 25x faster.

cs.CV↗

arXiv2024

Towards Fast Setup and High Throughput of GPU Serverless Computing

Integrating GPUs into serverless computing platforms is crucial for improving efficiency. However, existing solutions for GPU-enabled serverless computing platforms face two significant problems due to coarse-grained GPU management: long setup time and low function throughput. To address these issues, we propose SAGE, a GPU serverless framework with fast setup and high throughput. First, based on the data knowability of GPU function ahead of actual execution, SAGE first devises the parallelized function setup mechanism, which parallelizes the data preparation and context creation. In this way, SAGE achieves fast setup of GPU function invocations.Second, SAGE further proposes the sharing-based memory management mechanism, which shares the read-only memory and context memory across multiple invocations of the same function. The memory sharing mechanism avoids repeated data preparation and then unnecessary data-loading contention. As a consequence, the function throughput could be improved. Our experimental results show that SAGE reduces function duration by 11.3X and improves function density by 1.22X compared to the state-of-the-art serverless platform.

cs.DC↗

arXiv2024

GIM: Learning Generalizable Image Matcher From Internet Videos

Image matching is a fundamental computer vision problem. While learning-based methods achieve state-of-the-art performance on existing benchmarks, they generalize poorly to in-the-wild images. Such methods typically need to train separate models for different scene types and are impractical when the scene type is unknown in advance. One of the underlying problems is the limited scalability of existing data construction pipelines, which limits the diversity of standard image matching datasets. To address this problem, we propose GIM, a self-training framework for learning a single generalizable model based on any image matching architecture using internet videos, an abundant and diverse data source. Given an architecture, GIM first trains it on standard domain-specific datasets and then combines it with complementary matching methods to create dense labels on nearby frames of novel videos. These labels are filtered by robust fitting, and then enhanced by propagating them to distant frames. The final model is trained on propagated data with strong augmentations. We also propose ZEB, the first zero-shot evaluation benchmark for image matching. By mixing data from diverse domains, ZEB can thoroughly assess the cross-domain generalization performance of different methods. Applying GIM consistently improves the zero-shot performance of 3 state-of-the-art image matching architectures; with 50 hours of YouTube videos, the relative zero-shot performance improves by 8.4%-18.1%. GIM also enables generalization to extreme cross-domain data such as Bird Eye View (BEV) images of projected 3D point clouds (Fig. 1(c)). More importantly, our single zero-shot model consistently outperforms domain-specific baselines when evaluated on downstream tasks inherent to their respective domains. The video presentation is available at https://www.youtube.com/watch?v=FU_MJLD8LeY.

cs.CV↗

arXiv2023

Transition from localized to delocalized trajectories in random walk subject to random drives

Random walk subject to random drive has been extensively employed as a model for physical and biological processes. While equilibrium statistical physics has yielded significant insights into the distributions of dynamical fixed points of such a system, its non-equilibrium properties remain largely unexplored. In contrast, most real-world applications concern the dynamical aspects of this model. In particular, dynamical quantities like heat dissipation and work absorption play a central role in predicting and controlling non-equilibrium phases of matter. Recent advances in non-equilibrium statistical physics enable a more refined study of the dynamical aspects of random walk under random drives. We perform a numerical study on this model and demonstrate that it exhibits two distinct phases: a localized phase where typical random walk trajectories are non-extensive and confined to the neighborhood of fixed points, and a delocalized phase where typical random walk trajectories are extensive and can transition between fixed points. We propose different summary statistics for the heat dissipation and show that these two phases are distinctly different. Our characterization of these distinctive phases deepens the understanding of and provides novel strategies for the non-equilibrium phase of this model.

cond-mat.stat-mech↗

arXiv2023

DFlow: Efficient Dataflow-based Invocation Workflow Execution for Function-as-a-Service

The Serverless Computing is becoming increasingly popular due to its ease of use and fine-grained billing. These features make it appealing for stateful application or serverless workflow. However, current serverless workflow systems utilize a controlflow-based invocation pattern to invoke functions. In this execution pattern, the function invocation depends on the state of the function. A function can only begin executing once all its precursor functions have completed. As a result, this pattern may potentially lead to longer end-to-end execution time. We design and implement the DFlow, a novel dataflow-based serverless workflow system that achieves high performance for serverless workflow. DFlow introduces a distributed scheduler (DScheduler) by using the dataflow-based invocation pattern to invoke functions. In this pattern, the function invocation depends on the data dependency between functions. The function can start to execute even its precursor functions are still running. DFlow further features a distributed store (DStore) that utilizes effective fine-grained optimization techniques to eliminate function interaction, thereby enabling efficient data exchange. With the support of DScheduler and DStore, DFlow can achieving an average improvement of 60% over CFlow, 40% over FaaSFlow, 25% over FaasFlowRedis, and 40% over KNIX on 99%-ile latency respectively. Further, it can improve network bandwidth utilization by 2x-4x over CFlow and 1.5x-3x over FaaSFlow, FaaSFlowRedis and KNIX, respectively. DFlow effectively reduces the cold startup latency, achieving an average improvement of 5.6x over CFlow and 1.1x over FaaSFlow

cs.DC↗

arXiv2023

DataFlower: Exploiting the Data-flow Paradigm for Serverless Workflow Orchestration

Serverless computing that runs functions with auto-scaling is a popular task execution pattern in the cloud-native era. By connecting serverless functions into workflows, tenants can achieve complex functionality. Prior researches adopt the control-flow paradigm to orchestrate a serverless workflow. However, the control-flow paradigm inherently results in long response latency, due to the heavy data persistence overhead, sequential resource usage, and late function triggering. Our investigation shows that the data-flow paradigm has the potential to resolve the above problems, with careful design and optimization. We propose DataFlower, a scheme that achieves the data-flow paradigm for serverless workflows. In DataFlower, a container is abstracted to be a function logic unit and a data logic unit. The function logic unit runs the functions, and the data logic unit handles the data transmission asynchronously. Moreover, a host-container collaborative communication mechanism is used to support efficient data transfer. Our experimental results show that compared to state-of-the-art serverless designs, DataFlower reduces the 99\%-ile latency of the benchmarks by up to 35.4\%, and improves the peak throughput by up to 3.8X.

cs.DC↗

arXiv2022

The Serverless Computing Survey: A Technical Primer for Design Architecture

The development of cloud infrastructures inspires the emergence of cloud-native computing. As the most promising architecture for deploying microservices, serverless computing has recently attracted more and more attention in both industry and academia. Due to its inherent scalability and flexibility, serverless computing becomes attractive and more pervasive for ever-growing Internet services. Despite the momentum in the cloud-native community, the existing challenges and compromises still wait for more advanced research and solutions to further explore the potentials of the serverless computing model. As a contribution to this knowledge, this article surveys and elaborates the research domains in the serverless context by decoupling the architecture into four stack layers: Virtualization, Encapsule, System Orchestration, and System Coordination. Inspired by the security model, we highlight the key implications and limitations of these works in each layer, and make suggestions for potential challenges to the field of future serverless computing.

cs.DC↗

arXiv2021

Pagurus: Eliminating Cold Startup in Serverless Computing with Inter-Action Container Sharing

Serverless computing provides fine-grain resource sharing between Cloud tenants through containers. Each function invocation (action) runs in an individual container. When there is not an already started container for a user function, a new container has to be created for it. However, the long cold startup time of a container results in the long response latency of the action. Our investigation shows that the containers for some user actions share most of the software packages. If an action that requires a new container can ``borrow'' a similar warm container from other actions, the long cold startup can be eliminated. Based on the above finding, we propose Pagurus, a runtime container management system for eliminating the cold startup in serverless computing. Pagurus is comprised of an inter-action container scheduler and an intra-action container scheduler for each action. The inter-action container scheduler schedules shared containers among actions. The intra-action container scheduler deals with the management of the container lifecycle. Our experimental results show that Pagurus effectively eliminates the time-consuming container cold startup. An action may start to run in 10ms with Pagurus, even if there is not warm container for it.

cs.DC↗