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Traced back 37 generations →Advisors
Works35 from public data
- 232
- Context-aware Biaffine Localizing Network for Temporal Sentence Grounding195
A novel localization framework that scores all pairs of start and end indices within the video simultaneously with a biaffine mechanism that significantly outperforms state-of-thearts on three public datasets, demonstrating the effectiveness of the proposed localization framework.
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- 3D Human Pose Estimation Using Spatio-Temporal Networks with Explicit Occlusion Training155
This work introduces a spatio-temporal network for robust 3D human pose estimation and designs a spatio-temporal discriminator based on body structures as well as limb motions to assess whether the predicted pose forms a valid pose and a valid movement.
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- Playing Lottery Tickets with Vision and Language64
This work performs the first empirical study to assess whether trainable subnetworks also exist in pre-trained VL models, and finds that LTH remains relevant when using other training methods (e.g., adversarial training).
- Graph and Temporal Convolutional Networks for 3D Multi-person Pose Estimation in Monocular Videos57
This work proposes a novel framework integrating graph convolutional networks (GCNs), which unlike the existing GCN, is based on a directed graph that employs the 2D pose estimator's confidence scores to improve the pose estimation results.
- Monocular 3D Multi-Person Pose Estimation by Integrating Top-Down and Bottom-Up Networks47
This work proposes a two-person pose discriminator that enforces natural two- person interactions and applies a semi-supervised method to overcome the 3D ground-truth data scarcity.
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- Annotations Are Not All You Need: A Cross-modal Knowledge Transfer Network for Unsupervised Temporal Sentence Grounding38
By modulating and transferring both appearance and action knowledge into the authors' challenging unsupervised task, the model can directly utilize this general knowledge to correlate videos and queries, and accurately retrieve the relevant segment without training.
- Dual Networks Based 3D Multi-Person Pose Estimation From Monocular Video28
This work proposes the integration of top-down and bottom-up approaches to address the common gaps between training and testing data, and introduces a two-person pose discriminator that enforces natural two- person interactions.
- Unsupervised Domain Adaptative Temporal Sentence Localization with Mutual Information Maximization26
This paper proposes an effective and novel baseline for UDA-TSL to bridge the multi-modal gap across different domains and learns the potential correspondence between the video-query pairs in target domain, and develops separate modality-specific domain adaptation modules to smoothly balance the minimization of the domain shifts in cross-dataset video and query domains.
- Object Tracking Using Spatio-Temporal Networks for Future Prediction Location24
An object tracking algorithm that predicts the future locations of the target object and assists the tracker to handle object occlusion and sets new state-of-the-art performance on a few commonly used tracking benchmarks.
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- DecenterNet: Bottom-Up Human Pose Estimation Via Decentralized Pose Representation19
The proposed DecenterNet is an end-to-end deep architecture to perform robust and efficient pose estimation in crowded scenes and introduces a decentralized pose representation that uses all visible keypoints as the root points to represent human poses, which is more robust in the entanglement area.
- 3D Human Mesh Reconstruction by Learning to Sample Joint Adaptive Tokens for Transformers17
A Learnable Sampling module to generate joint adaptive tokens and then use transformers to aggregate global information and achieves lower reconstruction errors in terms of both the vertex-based metric and the joint- based metric compared to previous state of the arts.
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- Filling the Information Gap between Video and Query for Language-Driven Moment Retrieval16
A Teacher-Student Moment Retrieval framework to fill the cross-modal information gap during the moment retrieval guided by a single query and introduces an adaptive knowledge distillation mechanism to train a student model with a single queries input by selectively absorbing the knowledge from the teacher model.
- Extrapolating and Decoupling Image-to-Video Generation Models: Motion Modeling is Easier Than You Think15
A novel Extrapolating and Decoupling framework is proposed, which introduces model merging techniques to the I2V domain for the first time and introduces a training-free extrapolation strategy to amplify the dynamic range of the motion, effectively reversing the fine-tuning process to enhance the motion degree significantly.
- Transform-Equivariant Consistency Learning for Temporal Sentence Grounding13
A novel Equivariant Consistency Regulation Learning (ECRL) framework is introduced to learn more discriminative query-related frame-wise representations for each video, in a self-supervised manner, to address the temporal sentence grounding.
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- Text-to-Decision Agent: Offline Meta-Reinforcement Learning from Natural Language Supervision7
A simple and scalable framework that supervises offline meta-RL with natural language with a generalized world model to encode multi-task decision data into a dynamics-aware embedding space and predicts which textual description goes with which decision embedding.
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- SkatingVerse: A large‐scale benchmark for comprehensive evaluation on human action understanding7
SkatingVerse significantly helps the training and evaluation of HAU methods, the performance of existing HAU methods has much room to improve, and SkatingVerse helps to reduce such gaps, and unifying relevant tasks in HAU through a uniform dataset can facilitate more practical applications.
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- Low-shot Object Detection via Classification Refinement.2
Experiments show the proposed low-shot classification correction network (LSCN) can bring significant performance gains to rarely labeled categories and outperforms previous work on COCO and PASCAL VOC by a large margin.
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- Curriculum Semantic Retrieval System based on Distant Supervision–
The experimental results show that the proposed model provides an effective solution for the development and application of knowledge graph in the field of intelligent education.
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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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