The technical complexity of research papers often limits their reach, necessitating more accessible formats like scientific videos to disseminate key insights through engaging narration. However, existing automated methods primarily focus on static posters or slide presentations that remain template-bound and linear. Shifting to audience-adaptive video synthesis requires addressing non-linear narrative orchestration and the joint synchronization of disparate multimodal assets. We introduce VideoAgent, a modular framework that redefines scientific video synthesis as an intent-driven planning problem. By decoupling content understanding from multimodal synthesis, VideoAgent adaptively interleaves static slides with dynamic animations to match the semantic density of the narration. We further propose SciVidEval, a benchmark evaluating multimodal quality and pedagogical utility through automated metrics and human knowledge transfer studies. Extensive experiments demonstrate that VideoAgent effectively conveys complex technical logic with high narrative fidelity and communicative impact.

Video anomaly detection (VAD) is essential for intelligent surveillance and public security. While modern VAD models have achieved high accuracy, their heavy computational demands make them difficult to deploy on resource-constrained edge devices, where real-time anomaly detection is most needed. In this paper, we propose an efficient VAD method (BC-VAD) designed for edge devices, which utilizes a cross-frame information fusion mechanism with background feature caching. Specifically, the proposed method utilizes the first frame as a static background prior to construct a Key-Value (KV) cache. By reusing these cached features, we eliminate the need to repeatedly encode background content, which is a major source of inefficiency in traditional VAD methods. Furthermore, we introduce a data augmentation strategy inspired by physical principles to enhance model robustness in real-world applications. We successfully deploy our method on typical edge devices, achieving superior inference speeds with only 1.7M parameters and 0.7 GFLOPs. Meanwhile, our method maintains competitive accuracy on the Avenue and ShanghaiTech benchmarks.

Dynamic graph-level embedding aims to capture structural evolution in networks, which is essential for modeling real-world scenarios. However, existing methods face two critical yet under-explored issues: Structural Visit Bias, where random walk sampling disproportionately emphasizes high-degree nodes, leading to redundant and noisy structural representations; and Abrupt Evolution Blindness, the failure to effectively detect sudden structural changes due to rigid or overly simplistic temporal modeling strategies, resulting in inconsistent temporal embeddings. To overcome these challenges, we propose EvoFormer, an evolution-aware Transformer framework tailored for dynamic graph-level representation learning. To mitigate Structural Visit Bias, EvoFormer introduces a Structure-Aware Transformer Module that incorporates positional encoding based on node structural roles, allowing the model to globally differentiate and accurately represent node structures. To overcome Abrupt Evolution Blindness, EvoFormer employs an Evolution-Sensitive Temporal Module, which explicitly models temporal evolution through a sequential three-step strategy: (I) Random Walk Timestamp Classification, generating initial timestamp-aware graph-level embeddings; (II) Graph-Level Temporal Segmentation, partitioning the graph stream into segments reflecting structurally coherent periods; and (III) Segment-Aware Temporal Self-Attention combined with an Edge Evolution Prediction task, enabling the model to precisely capture segment boundaries and perceive structural evolution trends, effectively adapting to rapid temporal shifts. Extensive evaluations on five benchmark datasets confirm that EvoFormer achieves state-of-the-art performance in graph similarity ranking, temporal anomaly detection, and temporal segmentation tasks, validating its effectiveness in correcting structural and temporal biases. Code is available at https://github.com/zlx0823/EvoFormerCode.

Human pose estimation has achieved significant progress on high-resolution (HR) images, but it experiences severe performance degradation on low-resolution (LR) images. One key reason is that LR images lack sufficient appearance details and fine-grained spatial information. In this paper, we propose a prior representation-guided method (PRG) for low-resolution human pose estimation. Our approach consists of two stages: in the first stage, we design a prior representation extraction network to obtain prior representation from HR images. Then we propose dynamic residual blocks that utilize the extracted prior representation to guide the pose estimation network in focusing on detailed features around joint areas. In the second stage, we use a compact diffusion model with fewer iterations to generate the consistent prior representation from LR images, eliminating the reliance on HR images. Extensive experiments demonstrate that our method achieves significant improvements across various resolutions and backbone networks. In particular, our method improves 16.4 AP compared to the SimCC-Res50 baseline at a resolution of 32×32.

Composed image retrieval is a challenging multimodal task that refers to the process of retrieving target image by taking advantage of both complementary and synergistic image and text input. Existing efforts often focus on designing interaction models to fuse global query image and text features. However, these approaches struggle to capture fine-grained semantic association information between query image and text, especially when it comes to identifying specific objects or attributes in the query image that need to be modified in the text. In addition, these methods fail to adequately model cross-modal attention when dealing with composed query and target image, resulting in the model's inability to accurately map the semantic information from the composed query to the corresponding regions in the target image. To address these challenges, we propose a Text-Guided Attribute Enhancement Framework for Composed Image Retrieval (TAE-CIR). Our approach consists of three key modules: (a) Multi-granularity vision aggregation module, which extracts multi-granularity visual features and captures fine-grained object-level features related to the query text, refining object and attribute representations for more precise retrieval; (b) Multi-level fusion interaction module, which facilitates deep cross-modal interactions between the composed query and target image features, effectively capturing complex semantic relationships from the composed query to target image; (c) Composed feature alignment, which fuses multi-granularity visual features with the text using a text-guided Q-Former and contrastive learning to ensure accurate alignment between the composed query and the target image. Our extensive experiments on benchmark datasets FashionIQ and CIRR demonstrate the superiority of our proposed method.

Text-based person retrieval aims to search for target persons based on a given text description query. However, existing methods often have the following problems: (1) Ignoring local attribute information between different persons in feature learning, which results in the low distinguishability of similar people's feature representations. (2) Lacking fine-grained semantics alignment between visual images and text descriptions, which leads to inconsistency in person details between query and target. To address these issues, we propose a Fine-grained Semantics-aware Representation Learning (FSRL) method that establishing intra-modal local attribute correlations and inter-modal fine-grained semantic correlations. Specifically, we first design an identity self-distillation module, which explores soft identity labels that reflect local attribute similarities among different people. The soft identity labels assist the model in learning discriminative features associated with fine-grained attributes of persons. Secondly, we propose a visual-language relationship modeling module that enforces the model to proofread "error words" randomly changed in text during the cross-modal interaction process to establish fine-grained image-text semantic correlations. Extensive experiments show that the proposed method achieves new state-of-the-art results on three benchmark datasets and also performs well on the domain generalization task. Our code is available at https://github.com/y416f/FSRL.

Recently, a large amount of work has emerged for knowledge graph completion (KGC), which aims to reason over known facts and to infer the missing links. Meanwhile, contrastive learning has been applied to the KGC tasks, which can improve the representation quality of entities and relations. However, existing KGC approaches tend to improve their performance with high-dimensional embeddings and complex models, which make them suffer from large storage space and high training costs. Furthermore, contrastive loss with single positive sample learns little structural and semantic information in knowledge graphs due to the complex relation types. To address these challenges, we propose a novel knowledge graph completion model named ConKGC with the embedding dimension scaling and a relation-aware multi-positive contrastive loss. In order to achieve both space consumption reduction and model performance improvement, a new scoring function is proposed to map the raw low-dimensional embeddings of entities and relations to high-dimensional embedding space, and predict low-dimensional tail entities with latent semantic information of high-dimensional embeddings. In addition, ConKGC designs a multiple weak positive samples based contrastive loss under different relation types to maintain two important training targets, Alignment and Uniformity. This loss function and few parameters of the model ensure that ConKGC performs best and has fast convergence speed. Extensive experiments on three standard datasets confirm the effectiveness of our innovations, and the performance of ConKGC is significantly improved compared to the state-of-the-art methods.

Cross-modal hashing has been widely investigated recently for its efficiency in large-scale cross-media retrieval. However, most existing cross-modal hashing methods learn hash functions in a batch-based learning mode. Such mode is not suitable for large-scale data sets due to the large memory consumption and loses its efficiency when training streaming data. Online cross-modal hashing can deal with the above problems by learning hash model in an online learning process. However, existing online cross-modal hashing methods cannot update hash codes of old data by the newly learned model. In this paper, we propose Online Collective Matrix Factorization Hashing (OCMFH) based on collective matrix factorization hashing (CMFH), which can adaptively update hash codes of old data according to dynamic changes of hash model without accessing to old data. Specifically, it learns discriminative hash codes for streaming data by collective matrix factorization in an online optimization scheme. Unlike conventional CMFH which needs to load the entire data points into memory, the proposed OCMFH retrains hash functions only by newly arriving data points. Meanwhile, it generates hash codes of new data and updates hash codes of old data by the latest updated hash model. In such way, hash codes of new data and old data are well-matched. Furthermore, a zero mean strategy is developed to solve the mean-varying problem in the online hash learning process. Extensive experiments on three benchmark data sets demonstrate the effectiveness and efficiency of OCMFH on online cross-media retrieval.