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- Shuicheng YanSuggested from co-authorship
- Bingfeng ZhouSuggested from co-authorship
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Works137 from public data
- Deep High-Resolution Representation Learning for Visual Recognition5,145
The superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, is shown, suggesting that the HRNet is a stronger backbone for computer vision problems.
- Fast Fourier Convolution848
FFC is a generic operator that can directly replace vanilla convolutions in a large body of existing networks, without any adjustments and with comparable complexity metrics ( e.g. , FLOPs).
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- Pyramidal Flow Matching for Efficient Video Generative Modeling395
A unified pyramidal flow matching algorithm that reinterprets the original denoising trajectory as a series of pyramid stages, where only the final stage operates at the full resolution, thereby enabling more efficient video generative modeling.
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- Weakly-Supervised Action Localization by Generative Attention Modeling184
This paper proposes to model the class-agnostic frame-wise probability conditioned on the frame attention using conditional Variational Auto-Encoder (VAE), and demonstrates advantage of the method and effectiveness in handling action-context confusion problem.
- High-Capacity Convolutional Video Steganography with Temporal Residual Modeling175
This work proposes a novel solution to the problem of hiding a video into another video by developing the model based on deep convolutional neural networks, which is the first of its kind in the literature of video steganography.
- Attention-Based Multi-Context Guiding for Few-Shot Semantic Segmentation165
An Attentionbased Multi-Context Guiding (A-MCG) network is proposed, which consists of three branches: the support branch, the query branch, the feature fusion branch, and adopts a spatial attention along the fusion branch to highlight context information from several scales, enhancing self-supervision in one-shot learning.
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- InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior143
Ind InstructScene, a novel generative framework that integrates a semantic graph prior and a layout decoder to improve controllability and fidelity for 3D scene synthesis, is introduced.
- Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization109
This paper proposes an efficient video decomposition that represents each video as keyframes and temporal motions, then adapted to an LLM using well-designed tokenizers that discretize visual and temporal information as a few tokens, thus enabling unified generative pre-training of videos, images, and text.
- Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization104
A well-designed visual tokenizer is introduced to translate the non-linguistic image into a sequence of discrete tokens like a foreign language that LLM can read, which empowers LaVIT to serve as an impressive generalist interface to understand and generate multi-modal content simultaneously.
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- OmniPhysGS: 3D Constitutive Gaussians for General Physics-Based Dynamics Generation77
Comprehensive experiments demonstrate that OmniPhysGS achieves more general and realistic physical dynamics across a broader spectrum of materials, including elastic, viscoelastic, plastic, and fluid substances, as well as interactions between different materials.
- Non-Metric Locality-Sensitive Hashing77
This paper utilizes the norm-keeping property of p-stable functions to ensure that two data's collision probability reflects their non-metric distance in original feature space and investigates various concrete examples to validate the proposed algorithm.
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- Learning End-to-End Autonomous Steering Model from Spatial and Temporal Visual Cues67
This work focuses on a vision-based model that directly maps raw input images to steering angles using deep networks, and demonstrates the effectiveness and robustness of the model, even under scenarios like drastic lighting changes and abrupt turning.
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- DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian Splat Generation65
DiffSplat is introduced, a novel 3D generative framework that natively generates 3D Gaussian splats by taming large-scale text-to-image diffusion models by effectively utilizing web-scale 2D priors while maintaining 3D consistency in a unified model.
- Efficient large-scale image annotation by probabilistic collaborative multi-label propagation64
A novel multi-label propagation scheme that encodes the tag information of an image as a unit label confidence vector, which naturally imposes inter-label constraints and manipulates labels interactively and outperforms the state-of-the-art algorithms.
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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