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- Video Question Answering via Gradually Refined Attention over Appearance and Motion840
This paper proposes an end-to-end model which gradually refines its attention over the appearance and motion features of the video using the question as guidance and demonstrates the effectiveness of the model by analyzing the refined attention weights during the question answering procedure.
- Deeply-Learned Part-Aligned Representations for Person Re-identification813
This paper proposes a simple yet effective human part-aligned representation for handling the body part misalignment problem, and shows state-of-the-art results over standard datasets, Market-1501,CUHK03, CUHK01 and VIPeR.
- ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering793
This work introduces ActivityNet-QA, a fully annotated and large scale VideoQA dataset, which consists of 58,000 QA pairs on 5,800 complex web videos derived from the popular ActivityNet dataset and explores various video representation strategies to improve videoQA performance.
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- DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection538
This paper proposes a multi-task deep saliency model based on a fully convolutional neural network with global input (whole raw images) and global output (Whole saliency maps) and presents a graph Laplacian regularized nonlinear regression model for saliency refinement.
- Dynamic Network Embedding by Modeling Triadic Closure Process514
This paper presents a novel representation learning approach, DynamicTriad, to preserve both structural information and evolution patterns of a given network, and demonstrates that, compared with several state-of-the-art techniques, this approach achieves substantial gains in several application scenarios.
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- Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning412
This paper proposes a new approach, namely Hierarchical Recurrent Neural Encoder (HRNE), to exploit temporal information of videos to exploit video temporal structure in a longer range by reducing the length of input information flow, and compositing multiple consecutive inputs at a higher level.
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- Counterfactual Samples Synthesizing for Robust Visual Question Answering341
A model-agnostic Counterfactual Samples Synthesizing (CSS) training scheme that significantly improves both visual-explainable and question-sensitive abilities of VQA models and, in return, the performance of these models is further boosted.
- Multiple knowledge representation for big data artificial intelligence: framework, applications, and case studies309
MKR is an advanced AI representation framework for more complete intelligent functions, such as raw signal perception, feature extraction and vectorization, knowledge symbolization, and logical reasoning, and it is expected that MKR research and its applications will drive the evolution of AI 2.0 and beyond.
- Towards a new generation of artificial intelligence in China285
This Perspective briefly introduces the New Generation Artificial Intelligence Development Plan of China (2015–2030) from the point of view of the authors, a group of AI experts from academia and industry who have been involved in various stages of the plan.
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- Challenges and opportunities: from big data to knowledge in AI 2.0210
It is concluded that integrating data-driven machine learning with human knowledge can effectively lead to explainable, robust, and general AI.
- A Free Lunch for Unsupervised Domain Adaptive Object Detection without Source Data193
Self-entropy descent (SED) is a metric proposed to search an appropriate confidence threshold for reliable pseudo label generation without using any handcrafted labels in object detection, where completely clean labels are still unattainable.
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- Ranking with local regression and global alignment for cross media retrieval191
A novel ranking algorithm, namely ranking with Local Regression and Global Alignment (LRGA), which learns a robust Laplacian matrix for data ranking and a unified objective function to globally align the local models from all the data points so that an optimal ranking value can be assigned to each data point.
- Federated unsupervised representation learning174
The federated contrastive averaging with dictionary and alignment (FedCA) algorithm, composed of two key modules: a dictionary module to aggregate the representations of samples from each client which can be shared with all clients for consistency of representation space, is adopted for local model training.
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- Automatic generation of human animation based on motion programming: Virtual Humans and Social Agents165
This paper proposes a method for rendering photo-realistic animations of dynamic scenes under environment illumination in real time and makes a distinction between self-shadow and shadows cast by other objects and computes these two kinds of shadows efficiently.
- Momentor: Advancing Video Large Language Model with Fine-Grained Temporal Reasoning156
The proposed Momentor, a Video-LLM capable of accomplishing fine-grained temporal understanding tasks, is designed and trained on Moment-10M, a large-scale video instruction dataset with segment-level instruction data, enabling it to perform segment-level reasoning and localization.
- Zero-Shot Recognition Using Dual Visual-Semantic Mapping Paths156
A novel framework for zero-shot recognition is proposed that can not only apply prior semantic knowledge to infer underlying semantic manifold in the image feature space, but also generate optimized semantic embedding space, which can enhance the transfer ability of the visual-semantic mapping to unseen classes.
- Discriminative coupled dictionary hashing for fast cross-media retrieval150
The experiments show that the DCDH and MV-DCDH outperform the state-of-the-art methods significantly on cross-media retrieval and conjecture that a balanced representation is crucial in cross- media retrieval.
- Effective multi-modal retrieval based on stacked auto-encoders150
This paper proposes an effective mapping mechanism based on deep learning (i.e., stacked auto-encoders) for multi-modal retrieval that achieves significant improvement in search accuracy over the state-of-the-art methods.
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 2026-10-14. 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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