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- Thomas S. HuangSuggested from co-authorship
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Works523 from public data
- Person Transfer GAN to Bridge Domain Gap for Person Re-identification2,033
A Person Transfer Generative Adversarial Network (PTGAN) is proposed to relieve the expensive costs of annotating new training samples and comprehensive experiments show that the domain gap could be substantially narrowed-down by the PTGAN.
- HunyuanVideo: A Systematic Framework For Large Video Generative Models1,773
HunyuanVideo is introduced, an innovative open-source video foundation model that demonstrates performance in video generation comparable to, or even surpassing, that of leading closed-source models.
- 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering1,670
This work proposes 4D Gaussian Splatting (4D-GS) as a holistic representation for dynamic scenes rather than applying 3D-GS for each individual frame, and achieves real-time rendering under high resolutions, 82 FPS at an 800x800 resolution on an RTX 3090 GPU while maintaining comparable or better quality than previous state- of-the-art methods.
- Pose-Driven Deep Convolutional Model for Person Re-identification843
A Pose-driven Deep Convolutional (PDC) model is proposed to learn improved feature extraction and matching models from end to end and explicitly leverages the human part cues to alleviate the pose variations and learn robust feature representations from both the global image and different local parts.
- Pixel Difference Networks for Efficient Edge Detection541
A simple, lightweight yet effective architecture named Pixel Difference Network (PiDiNet) for efficient edge detection that adopts novel pixel difference convolutions that integrate the traditional edge detection operators into the popular convolutional operations in modern CNNs for enhanced performance on the task, which enjoys the best of both worlds.
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- Modeling Point Clouds With Self-Attention and Gumbel Subset Sampling455
This work develops Point Attention Transformers (PATs), using a parameter-efficient Group Shuffle Attention (GSA) to replace the costly Multi-Head Attention, and proposes an end-to-end learnable and task-agnostic sampling operation, named Gumbel Subset Sampling (GSS), to select a representative subset of input points.
- Deep Representation Learning With Part Loss for Person Re-Identification439
Experimental results on three person ReID datasets, i.e., Market1501, CUHK03, and VIPeR, show that the proposed deep representation learning procedure named part loss network outperforms existing deep representations.
- GLAD410
A Global-Local-Alignment Descriptor (GLAD) and an efficient indexing and retrieval framework that leverages the local and global cues in human body to generate a discriminative and robust representation are proposed.
- ControlVideo: Training-free Controllable Text-to-Video Generation392
ControlVideo, adapted from ControlNet, leverages coarsely structural consistency from input motion sequences, and introduces three modules to improve video generation, which outperforms the state-of-the-arts on extensive motion-prompt pairs quantitatively and qualitatively.
- Dynamic Multiscale Graph Neural Networks for 3D Skeleton Based Human Motion Prediction374
Novel dynamic multiscale graph neural networks (DMGNN) to predict 3D skeleton-based human motions and outperforms state-of-the-art methods in both short and long-term predictions on the datasets of Human 3.6M and CMU Mocap.
- DR2-Net: Deep Residual Reconstruction Network for image compressive sensing359
A novel Deep Residual Reconstruction Network (DR2-Net) to reconstruct the image from its Compressively Sensed measurement by outperforms traditional iterative methods and recent deep learning-based methods by large margins at measurement rates 0.01, 0.1, and 0.25.
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- Scalable Person Re-identification on Supervised Smoothed Manifold342
An unconventional manifold-preserving algorithm is proposed, which can make best use of supervision from training data, whose label information is given as pairwise constraints, scale up to large repositories with low on-line time complexity, and be plunged into most existing algorithms, serving as a generic postprocessing procedure to further boost the identification accuracies.
- GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models339
This paper attempts to bridge the power from the two types of diffusion models via the recent explicit and efficient 3D Gaussian splatting representation via a fast 3D object gener-ation framework, named as GaussianDreamer, where the 3D diffusion model provides priors for initial-ization and the 2D diffusion model enriches the geometry and appearance.
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- DisturbLabel: Regularizing CNN on the Loss Layer266
An extremely simple algorithm which randomly replaces a part of labels as incorrect values in each iteration, which prevents the network training from over-fitting by implicitly averaging over exponentially many networks which are trained with different label sets.
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- Bottom-Up Temporal Action Localization with Mutual Regularization213
Two regularization terms are introduced to mutually regularize the learning procedure for temporal action localization, and the Intra-phase Consistency (IntraC) regularization is proposed to make the predictions verified inside each phase; and the Inter- phase Consistencies (InterC)Regularization is suggested to keep consistency between these phases.
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- Adversarial Training Towards Robust Multimedia Recommender System208
This paper proposes a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning, to train the model to defend an adversary, which adds perturbations to the target image with the purpose of decreasing the model's accuracy.
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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 2026-10-14. 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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