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- 521
- All in One: Exploring Unified Video-Language Pre-Training258
This work introduces an end-to-end VLP model, namely all-in-one Transformer, that embeds raw video and textual signals into joint representations using a unified backbone architecture and introduces a novel and effective token rolling operation to encode temporal representations from video clips in a non-parametric manner.
- 155
- Removing the Background by Adding the Background: Towards Background Robust Self-supervised Video Representation Learning115
Self-supervised learning has shown great potentials in improving the video representation ability of deep neural networks by getting supervision from the data itself, but some of the current methods tend to cheat from the background, so this work proposes to remove the background impact by adding the background.
- A Simple Recipe towards Reducing Hallucination in Neural Surface Realisation92
This work proposes to integrate a language understanding module for data refinement with self-training iterations to effectively induce strong equivalence between the input data and the paired text to mitigate neural language generation systems often hallucinate contents.
- Enhancing Unsupervised Video Representation Learning by Decoupling the Scene and the Motion73
This work proposes to decouple the scene and the motion (DSM) with two simple operations, so that the model attention towards the motion information is better paid and the impact of the scene is weakened while the temporal sensitivity of the network is further enhanced.
- Improving Entity Linking by Modeling Latent Entity Type Information72
This work proposes to inject latent entity type information into the entity embeddings based on pre-trained BERT, and integrates a BERT-based entity similarity score into the local context model of a state-of-the-art model to better capture latententity type information.
- Love Evolution Algorithm: a stimulus–value–role theory-inspired evolutionary algorithm for global optimization69
The efficiency of the LEA is validated through numerical experiments with CEC2017 benchmark functions, outperforming seven metaheuristic algorithms as evidenced by the Wilcoxon signed-rank test and the Friedman test.
- Cross-Batch Negative Sampling for Training Two-Tower Recommenders66
This work proposes a simple yet effective sampling strategy called Cross-Batch Negative Sampling (CBNS), which takes advantage of the encoded item embeddings from recent mini-batches to boost the model training.
- MambaVC: Learned Visual Compression with Selective State Spaces53
This work introduces MambaVC, a simple, strong and efficient compression network based on SSM, which achieves superior rate-distortion performance with lower computational and memory overheads and shows even greater improvements with high-resolution images, highlighting its potential and scalability in real-world applications.
- Position-Guided Text Prompt for Vision-Language Pre-Training50
This work proposes a novel Position-guided Text Prompt (PTP) paradigm to enhance the visual grounding ability of cross-modal models trained with VLP, and introduces PTP into several state-of-the-art VLP frameworks.
- Operation-guided Neural Networks for High Fidelity Data-To-Text Generation47
This paper proposes a framework called Operation-guided Attention-based sequence-to-sequence network (OpAtt), with a specifically designed gating mechanism as well as a quantization module for operation results to utilize information from pre-executed symbolic operations.
- GMMFormer: Gaussian-Mixture-Model Based Transformer for Efficient Partially Relevant Video Retrieval40
This paper proposes GMMFormer, a Gaussian-Mixture-Model based Transformer which models clip representations implicitly, and incorporates Gaussian-Mixture-Model constraints to focus each frame on its adjacent frames instead of the whole video.
- A Statistical Framework for Product Description Generation39
A statistical framework that generates accurate and fluent product description from product attributes by using learned knowledge to decide what to say and how to say for product description generation is presented.
- Enhancing Neural Data-To-Text Generation Models with External Background Knowledge37
This paper enhances neural data-to-text models with external knowledge in a simple but effective way to improve the fidelity of generated text by attends to relevant external knowledge, encoded as a temporary memory, and combines this knowledge with the context representation of data before generating words.
- What Makes for Good Visual Instructions? Synthesizing Complex Visual Reasoning Instructions for Visual Instruction Tuning33
It is found that instructions focusing on complex visual reasoning tasks are particularly effective in improving the performance of MLLMs, with results correlating to instruction complexity.
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- Mention and Entity Description Co-Attention for Entity Disambiguation33
A type-aware co-attention model for entity disambiguation is proposed, which tries to identify the most discriminative words from mention contexts and most relevant sentences from corresponding entity descriptions simultaneously.
- Infer User Interests via Link Structure Regularization32
Both quantitative and qualitative experiments show that the proposed regularization framework based on the relation bipartite graph outperforms a few competitive baselines in learning user interests over a set of predefined topics and gives superior results compared to the baselines on retweet prediction and topical authority identification.
- Video-Text Pre-training with Learned Regions for Retrieval30
This work proposes a simple yet effective module for video-text representation learning, namely RegionLearner, which can take into account the structure of objects during pre-training on large-scale video- text pairs and is much more computationally efficient.
- 26
- Data2Text Studio: Automated Text Generation from Structured Data23
This work conducts experiments on RotoWire datasets for template extraction and text generation and shows that the Semi-HMMs model achieves improvements on both tasks.
- Adversarial open set domain adaptation via progressive selection of transferable target samples22
A novel method namely Thresholded Domain Adversarial Network (ThDAN), which progressively selects transferable target samples for distribution alignment and outperforms state-of-the-art domain adaptation and open set recognition approaches on benchmarks.
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- Escape after love: Philoponella prominens optimizer and its application to 3D path planning20
A novel metaheuristic inspired by the special mating behavior of the P. prominens, and simulates escape, sexual cannibalism and predation behaviors of males after mating, which demonstrates PPO's competitiveness against six state-of-the-art metaheuristics.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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