23 citations · 146 across the 33 of their papers we have counts for
30 papers · 1 filter
Effective Whole-body Pose Estimation with Two-stages Distillation
Zhendong Yang, Ailing Zeng, Chun Yuan +1
Whole-body pose estimation localizes the human body, hand, face, and foot keypoints in an image. This task is challenging due to multi-scale body parts, fine-grained localization f…
DreamDiffusion: Generating High-Quality Images from Brain EEG Signals
Yunpeng Bai, Xintao Wang, Yan-pei Cao +3
This paper introduces DreamDiffusion, a novel method for generating high-quality images directly from brain electroencephalogram (EEG) signals, without the need to translate though…
Towards Effective Collaborative Learning in Long-Tailed Recognition
Zhengzhuo Xu, Zenghao Chai, Chengyin Xu +2
Real-world data usually suffers from severe class imbalance and long-tailed distributions, where minority classes are significantly underrepresented compared to the majority ones.…
CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers
Dachuan Shi, Chaofan Tao, Anyi Rao +3
Recent vision-language models have achieved tremendous advances. However, their computational costs are also escalating dramatically, making model acceleration exceedingly critical…
HMC: Hierarchical Mesh Coarsening for Skeleton-free Motion Retargeting
Haoyu Wang, Shaoli Huang, Fang Zhao +2
We present a simple yet effective method for skeleton-free motion retargeting. Previous methods transfer motion between high-resolution meshes, failing to preserve the inherent loc…
From Knowledge Distillation to Self-Knowledge Distillation: A Unified Approach with Normalized Loss and Customized Soft Labels
Zhendong Yang, Ailing Zeng, Zhe Li +3
Knowledge Distillation (KD) uses the teacher's prediction logits as soft labels to guide the student, while self-KD does not need a real teacher to require the soft labels. This wo…
HiFace: High-Fidelity 3D Face Reconstruction by Learning Static and Dynamic Details
Zenghao Chai, Tianke Zhang, Tianyu He +7
3D Morphable Models (3DMMs) demonstrate great potential for reconstructing faithful and animatable 3D facial surfaces from a single image. The facial surface is influenced by the c…
Rethink Long-tailed Recognition with Vision Transformers
Zhengzhuo Xu, Shuo Yang, Xingjun Wang +1
In the real world, data tends to follow long-tailed distributions w.r.t. class or attribution, motivating the challenging Long-Tailed Recognition (LTR) problem. In this paper, we r…
UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers
Dachuan Shi, Chaofan Tao, Ying Jin +3
Real-world data contains a vast amount of multimodal information, among which vision and language are the two most representative modalities. Moreover, increasingly heavier models,…
Learning Imbalanced Data with Vision Transformers
Zhengzhuo Xu, Ruikang Liu, Shuo Yang +2
The real-world data tends to be heavily imbalanced and severely skew the data-driven deep neural networks, which makes Long-Tailed Recognition (LTR) a massive challenging task. Exi…
Uni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language Tasks
Hao Li, Jinguo Zhu, Xiaohu Jiang +8
Despite the remarkable success of foundation models, their task-specific fine-tuning paradigm makes them inconsistent with the goal of general perception modeling. The key to elimi…
High-fidelity Facial Avatar Reconstruction from Monocular Video with Generative Priors
Yunpeng Bai, Yanbo Fan, Xuan Wang +4
High-fidelity facial avatar reconstruction from a monocular video is a significant research problem in computer graphics and computer vision. Recently, Neural Radiance Field (NeRF)…
Darwinian Model Upgrades: Model Evolving with Selective Compatibility
Binjie Zhang, Shupeng Su, Yixiao Ge +5
The traditional model upgrading paradigm for retrieval requires recomputing all gallery embeddings before deploying the new model (dubbed as "backfilling"), which is quite expensiv…
DeViT: Deformed Vision Transformers in Video Inpainting
Jiayin Cai, Changlin Li, Xin Tao +2
This paper proposes a novel video inpainting method. We make three main contributions: First, we extended previous Transformers with patch alignment by introducing Deformed Patch-b…
ViTKD: Practical Guidelines for ViT feature knowledge distillation
Zhendong Yang, Zhe Li, Ailing Zeng +3
Knowledge Distillation (KD) for Convolutional Neural Network (CNN) is extensively studied as a way to boost the performance of a small model. Recently, Vision Transformer (ViT) has…
Rethinking Knowledge Distillation via Cross-Entropy
Zhendong Yang, Zhe Li, Yuan Gong +4
Knowledge Distillation (KD) has developed extensively and boosted various tasks. The classical KD method adds the KD loss to the original cross-entropy (CE) loss. We try to decompo…
HyP Loss: Beyond Hypersphere Metric Space for Multi-label Image Retrieval
Chengyin Xu, Zenghao Chai, Zhengzhuo Xu +3
Image retrieval has become an increasingly appealing technique with broad multimedia application prospects, where deep hashing serves as the dominant branch towards low storage and…
Masked Generative Distillation
Zhendong Yang, Zhe Li, Mingqi Shao +3
Knowledge distillation has been applied to various tasks successfully. The current distillation algorithm usually improves students' performance by imitating the output of the teac…
Privacy-Preserving Model Upgrades with Bidirectional Compatible Training in Image Retrieval
Shupeng Su, Binjie Zhang, Yixiao Ge +4
The task of privacy-preserving model upgrades in image retrieval desires to reap the benefits of rapidly evolving new models without accessing the raw gallery images. A pioneering…
Towards Universal Backward-Compatible Representation Learning
Binjie Zhang, Yixiao Ge, Yantao Shen +6
Conventional model upgrades for visual search systems require offline refresh of gallery features by feeding gallery images into new models (dubbed as "backfill"), which is time-co…
REALY: Rethinking the Evaluation of 3D Face Reconstruction
Zenghao Chai, Haoxian Zhang, Jing Ren +5
The evaluation of 3D face reconstruction results typically relies on a rigid shape alignment between the estimated 3D model and the ground-truth scan. We observe that aligning two…
Hot-Refresh Model Upgrades with Regression-Alleviating Compatible Training in Image Retrieval
Binjie Zhang, Yixiao Ge, Yantao Shen +5
The task of hot-refresh model upgrades of image retrieval systems plays an essential role in the industry but has never been investigated in academia before. Conventional cold-refr…
Semantic-Sparse Colorization Network for Deep Exemplar-based Colorization
Yunpeng Bai, Chao Dong, Zenghao Chai +3
Exemplar-based colorization approaches rely on reference image to provide plausible colors for target gray-scale image. The key and difficulty of exemplar-based colorization is to…
StrokeNet: Stroke Assisted and Hierarchical Graph Reasoning Networks
Lei Li, Kai Fan, Chun Yuan
Scene text detection is still a challenging task, as there may be extremely small or low-resolution strokes, and close or arbitrary-shaped texts. In this paper, StrokeNet is propos…