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Claim this profileWorks276 from public data
- Cross-Modal Self-Attention Network for Referring Image Segmentation603
A cross-modal self-attention (CMSA) module that effectively captures the long-range dependencies between linguistic and visual features and a gated multi-level fusion module to selectively integrateSelf-attentive cross- modal features corresponding to different levels in the image.
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- Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization389
An iterative multiview agreement strategy by minimizing the divergence objective among all factorized latent data-cluster representations during each iteration of optimization process, where such latent representation from each view serves to regulate those from other views, and such an intuitive process iteratively coordinates all views to be agreeable.
- Occlusion Aware Unsupervised Learning of Optical Flow344
This work introduces a new method which models occlusion explicitly and a new warping way that facilitates the learning of large motion and shows promising results on Flying Chairs, MPI-Sintel and KITTI benchmark datasets.
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- A Discriminative Latent Model of Object Classes and Attributes256
This work presents a discriminatively trained model for joint modelling of object class labels and their visual attributes and captures the correlations among attributes using an undirected graphical model built from training data.
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- Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry, and Fusion250
A substantial overview of the existing state-of-the-arts in the field of multi-modal data analytics from shallow to deep spaces is provided and it is indicated that the critical components for this field go to collaboration, adversarial competition, and fusion over multi- modal spaces.
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- Where-and-When to Look: Deep Siamese Attention Networks for Video-Based Person Re-Identification231
The proposed Siamese model is end-to-end trainable to jointly learn comparable hidden representations for paired pedestrian videos and their similarity value and outperforming state-of-the-art methods.
- Keyphrase Extraction Using Deep Recurrent Neural Networks on Twitter198
A novel deep recurrent neural network model is proposed to combine keywords and context information to perform keyphrases from tweets and the experimental results showed that the proposed method performs significantly better than previous methods.
- Deep adaptive feature embedding with local sample distributions for person re-identification170
A novel objective function is proposed to jointly optimize similarity metric learning, local positive mining and robust deep feature embedding for person re-id by proposing a novel sampling to mine suitable positives within a local range to improve the deep embedding in the context of large intra-class variations.
- High Resolution Acquisition, Learning and Transfer of Dynamic 3‐D Facial Expressions165
New expressions can be synthesized, either as dynamic morphing between individuals, or as expression transfer from a source face to a target face, as demonstrated in a series of experiments.
- Beyond Actions: Discriminative Models for Contextual Group Activities163
The proposed model jointly captures the group activity, the individual person actions, and the interactions among them, and implicitly infer it during learning and inference can significantly improve activity recognition performance.
- Effective Multi-Query Expansions: Collaborative Deep Networks for Robust Landmark Retrieval159
This paper proposes a novel framework, namely, multi-query expansions, to retrieve semantically robust landmarks by two steps, and proposes to learn a collaborative deep networks-based semantically, nonlinear, and high-level features over the latent factor for landmark photo as the training set.
- Manifold-Based Incomplete Multi-View Clustering via Bi-Consistency Guidance135
A novel method termed Manifold-based Incomplete Multi-view clustering via Bi-consistency guidance (MIMB), which flexibly recovers incomplete data among various views, and attempts to achieve biconsistency guidance via reverse regularization.
- What-and-where to match: Deep spatially multiplicative integration networks for person re-identification129
A novel deep multiplicative integration gating function is proposed, which answers the question of what-and-where to match for effective person re-id and is designed to be end-to-end trainable to characterize local pairwise feature interactions in a spatially aligned manner.
- Try This Instead127
This paper proposes attribute-aware collaborative filtering (A2CF) to perform substitute recommendation by addressing issues from both personalization and interpretability perspectives, and proves the recommendation effectiveness and interpretation quality of A2CF.
- Beyond Low-Rank Representations: Orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering121
This paper decomposes LRR into latent clustered orthogonal representation via low-rank matrix factorization, to encode the more flexible cluster structures than LRR over primal data objects and converts the problem of L RR into that of simultaneously learning Orthogonal clustered representation and optimized local graph structure for each view.
- Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding115
Experiments on KITTI 2015 dataset show that the estimated geometry, 3D motion and moving object masks, not only are constrained to be consistent, but also significantly outperforms other SOTA algorithms, demonstrating the benefits of the approach.
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- 3-D PersonVLAD: Learning Deep Global Representations for Video-Based Person Reidentification109
This paper presents the global deep video representation learning to video-based person reidentification (re-ID) that aggregates local 3-D features across the entire video extent to be complementary to3-D ConvNets as a novel layer to capture the appearance and motion dynamics in full-length videos.
- Few-Shot Deep Adversarial Learning for Video-Based Person Re-Identification92
A novel few-shot deep learning approach to video-based person re-identification, to learn comparable representations that are discriminative and view-invariant in matching persons.
- High-order pattern discovery from discrete-valued data91
The authors propose a novel method to discover qualitative and quantitative patterns (or event associations) inherent in a data set that uses the adjusted residual analysis in statistics to test the significance of the occurrence of a pattern candidate against its expectation.
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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