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Claim this profileWorks162 from public data
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- Learning Semantic Concepts and Order for Image and Sentence Matching342
A semantic-enhanced image and sentence matching model is proposed, which can improve the image representation by learning semantic concepts and then organizing them in a correct semantic order.
- ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments292
A new navigation framework, ETPNav, which focuses on two critical skills: the capability to abstract environments and generate long-range navigation plans, and the ability of obstacle-avoiding control in continuous environments.
- Bidirectional recurrent convolutional networks for multi-frame super-resolution273
This work proposes a bidirectional recurrent convolutional network for efficient multi-frame SR, different from vanilla RNNs, which has a low computational complexity and runs orders of magnitude faster than other multi- frame methods.
- Box-Driven Class-Wise Region Masking and Filling Rate Guided Loss for Weakly Supervised Semantic Segmentation257
This paper introduces a box-driven class-wise masking model (BCM) to remove irrelevant regions of each class and proposes a filling rate guided adaptive loss (FR-Loss) to help the model ignore the wrongly labeled pixels in proposals.
- Improving Description-Based Person Re-Identification by Multi-Granularity Image-Text Alignments238
A Multi-granularity Image-text Alignments (MIA) model is proposed to alleviate the cross-modal fine-grained problem for better similarity evaluation in description-based person Re-id and obtains the state-of-the-art performance on the CUHK-PEDES dataset.
- Instance-Aware Image and Sentence Matching with Selective Multimodal LSTM235
Extensive experiments show that the proposed selective multimodal Long Short-Term Memory network can well match image and sentence with complex content, and achieve the state-of-the-art results on two public benchmark datasets.
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- Rethinking the Heatmap Regression for Bottom-up Human Pose Estimation205
The scale-adaptive heatmap regression (SAHR) method, which can adaptively adjust the standard deviation for each keypoint, is proposed, which largely improves the accuracy of bottom-up human pose estimation and is comparable with the performances of most top-down methods.
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- M3: Multimodal Memory Modelling for Video Captioning159
A Multimodal Memory Model (M3) is proposed to describe videos, which builds a visual and textual shared memory to model the long-term visual-textual dependency and further guide visual attention on described visual targets to solve visual- Textual alignments.
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- GaitNet: An end-to-end network for gait based human identification146
This paper integrates an end-to-end network for gait recognition, named GaitNet, composed of two convolutional neural networks: one corresponds to gait segmentation, and the other corresponds to classification, which greatly simplifies the traditional step-by-step manner and is thus much more efficient for practical applications.
- Cross-Modal Ranking with Soft Consistency and Noisy Labels for Robust RGB-T Tracking145
A novel approach to suppress background effects for RGB-T tracking by integrating the soft cross-modality consistency into the ranking model which allows the sparse inconsistency to account for the different properties between these two modalities.
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- Part-Level Graph Convolutional Network for Skeleton-Based Action Recognition120
A novel Part-Level Graph Convolutional Network (PL-GCN) is proposed to capture part-level information of skeletons, which aims at learning high-level relations between body parts while the PA block aims at highlighting the important body parts in the action.
- Neighbor-view Enhanced Model for Vision and Language Navigation99
This work proposes a multi-module Neighbor-View Enhanced Model (NvEM) to adaptively incorporate visual contexts from neighbor views for better textual-visual matching of existing methods.
- Target tracking based on a distributed particle filter in underwater sensor networks85
Based on a distributed particle filter, two tracking algorithms are proposed for tracking mobile targets in cluster-based underwater sensor networks (USNs) and results indicate that one tracking algorithm achieves higher tracking accuracy while the other achieves dramatic reduction of communication cost, energy cost, and tracking response time.
- Advances in Online Audio-Visual Meeting Transcription84
A system that generates speaker-annotated transcripts of meetings by using a microphone array and a 360-degree camera and an online audio-visual speaker diarization method that leverages face tracking and identification, sound source localization, speaker identification and, if available, prior speaker information for robustness to various real world challenges is described.
- Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting83
This paper proposes Regularized Graph Structure Learning (RGSL) model to incorporate both explicit prior structure and implicit structure together, and learn the forecasting deep networks along with the graph structure.
- Constraint-Aware Zero-Shot Vision-Language Navigation in Continuous Environments77
A Constraint-Aware Navigator (CA-Nav), which reframes zero-shot VLN-CE as a sequential, constraint-aware sub-instruction completion process, and achieves the state-of-the-art performance on two VLN-CE benchmarks, surpassing the previous best method by 12% and 13% in Success Rate on the validation unseen splits of R2R-CE and RxR-CE, respectively.
- Relational Prototypical Network for Weakly Supervised Temporal Action Localization73
This paper proposes a weakly supervised temporal action localization method on untrimmed videos based on prototypical networks that outperforms the state-of-the-art methods on action-background separation and action relation construction.
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 2026-10-15. 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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