Text-to-Image Person Retrieval (TIPR) is a cross-modal matching task designed to identify the person images that best correspond to a given textual description. The key difficulty in TIPR is to realize robust correspondence between the textual and visual modalities within a unified latent representation space. To address this challenge, prior approaches incorporate attention mechanisms for implicit cross-modal local alignment. However, they lack the ability to verify whether all local features are correctly aligned. Moreover, existing methods tend to emphasize the utilization of hard negative samples during model optimization to strengthen discrimination between positive and negative pairs, often neglecting incorrectly matched positive pairs. To mitigate these problems, we propose FMFA, a cross-modal Full-Mode Fine-Grained Alignment framework, which enhances global matching through Explicit Fine-Grained Alignment (EFA) and existing implicit relational reasoning—hence the term “full-mode”—without introducing extra supervisory signals. In particular, we propose an Adaptive Similarity Distribution Matching (A-SDM) module to rectify unmatched positive sample pairs. A-SDM adaptively pulls the unmatched positive pairs closer in the joint embedding space, thereby achieving more precise global alignment. Additionally, we introduce an EFA module, which makes up for the lack of verification capability of implicit relational reasoning. EFA strengthens explicit cross-modal fine-grained interactions by sparsifying the similarity matrix and employs a hard coding method for local alignment. We evaluate our method on three public datasets, where it attains state-of-the-art results among all global matching methods. The code for our method is publicly accessible at https://github.com/yinhao1102/FMFA .

Weather forecasting is a crucial task for meteorologic research, with direct social and economic impacts. Recently, data-driven weather forecasting models based on deep learning have shown great potential, achieving superior performance compared with traditional numerical weather prediction methods. However, these models often require massive training data and computational resources. In this paper, we propose EWMoE, an effective model for accurate global weather forecasting, which requires significantly less training data and computational resources. Our model incorporates three key components to enhance prediction accuracy: 3D absolute position embedding, a core Mixture-of-Experts (MoE) layer, and two specific loss functions. We conduct our evaluation on the ERA5 dataset using only two years of training data. Extensive experiments demonstrate that EWMoE outperforms current models such as FourCastNet and ClimaX at all forecast time, achieving competitive performance compared with the state-of-the-art models Pangu-Weather and GraphCast in evaluation metrics such as Anomaly Correlation Coefficient (ACC) and Root Mean Square Error (RMSE). Additionally, ablation studies indicate that applying the MoE architecture to weather forecasting offers significant advantages in improving accuracy and resource efficiency.

Solid-state drives (SSDs) have become the storage device for many personal computers and enterprise servers. However, the access speed of SSDs is still a bottleneck for computer systems. With prefetching, SSDs are able to predict future requests before moving data to a faster storage unit. Nevertheless, the interleaved access flow of applications poses challenges for existing prefetchers. We propose a model named G&L consisting of two components, namely a generative pre-training (GPT) prefetcher and a logical block address (LBA)-I/O size table. The GPT prefetcher applies a decoder-only framework with a self-attention mechanism to forecast the next LBA. The LBA-I/O size table maps LBAs to their most frequently appeared I/Osize for prediction. To evaluate the entire performance of the G&L model, we propose an algorithm to simulate the buffer when the I/O size changes dynamically. Experiments show that the accuracy, coverage, and f1 score of the GPT prefetcher surpass the baselines in most cases. The prediction accuracy of the LBA-I/O size table exceeds the baseline on all datasets. The G&L model's prefetching accuracy, coverage, and f1 score also outperform the baseline considering variable I/O size.

Pre-training model on large-scale unlabeled web videos followed by task-specific fine-tuning is a canonical approach to learning video and language representations. However, the accompanying Automatic Speech Recognition (ASR) transcripts in these videos are directly transcribed from audio, which may be inconsistent with visual information and would impair the language modeling ability of the model. Meanwhile, previous V-L models fuse visual and language modality features using single- or dual-stream architectures, which are not suitable for the current situation. Besides, traditional V-L research focuses mainly on the interaction between vision and language modalities and leaves the modeling of relationships within modalities untouched. To address these issues and maintain a small manual labor cost, we add automatically extracted dense captions as a supplementary text and propose a new trilinear video-language interaction framework TEVL (Trilinear Encoder for Video-Language representation learning). TEVL contains three unimodal encoders, a TRIlinear encOder (TRIO) block, and a temporal Transformer. TRIO is specially designed to support effective text-vision-text interaction, which encourages inter-modal cooperation while maintaining intra-modal dependencies. We pre-train TEVL on the HowTo100M and TV datasets with four task objectives. Experimental results demonstrate that TEVL can learn powerful video-text representation and achieve competitive performance on three downstream tasks, including multimodal video captioning, video Question Answering (QA), as well as video and language inference. Implementation code is available at https://github.com/Gufrannn/TEVL .

Fully mining visual cues to aid in content understanding is crucial for video captioning. However, most state-of-the-art video captioning methods are limited to generating captions purely based on straightforward information while ignoring the scenario and context information. To fill the gap, we propose a novel, simple but effective scenario-aware recurrent transformer (SART) model to execute video captioning. Our model contains a “scenario understanding” module to obtain a global perspective across multiple frames, providing a specific scenario to guarantee a goal-directed description. Moreover, for the sake of achieving narrative continuity in the generated paragraph, a unified recurrent transformer is adopted. To demonstrate the effectiveness of our proposed SART, we have conducted comprehensive experiments on various large-scale video description datasets, including ActivityNet, YouCookII, and VideoStory. Additionally, we extend a story-oriented evaluation framework for assessing the quality of the generated caption more precisely. The superior performance has shown that SART has a strong ability to generate correct, deliberative, and narrative coherent video descriptions.