95 citations · 210 across the 17 of their papers we have counts for
17 papers
Dynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptation
Wangbo Zhao, Jiasheng Tang, Yizeng Han +5
Existing parameter-efficient fine-tuning (PEFT) methods have achieved significant success on vision transformers (ViTs) adaptation by improving parameter efficiency. However, the e…
Dual-view Curricular Optimal Transport for Cross-lingual Cross-modal Retrieval
Yabing Wang, Shuhui Wang, Hao Luo +5
Current research on cross-modal retrieval is mostly English-oriented, as the availability of a large number of English-oriented human-labeled vision-language corpora. In order to b…
Region Generation and Assessment Network for Occluded Person Re-Identification
Shuting He, Weihua Chen, Kai Wang +4
Person Re-identification (ReID) plays a more and more crucial role in recent years with a wide range of applications. Existing ReID methods are suffering from the challenges of mis…
SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels
Henry Hengyuan Zhao, Pichao Wang, Yuyang Zhao +3
Pre-trained vision transformers have strong representation benefits to various downstream tasks. Recently, many parameter-efficient fine-tuning (PEFT) methods have been proposed, a…
DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data Augmentation
Zelin Zang, Hao Luo, Kai Wang +4
Unsupervised Contrastive learning has gained prominence in fields such as vision, and biology, leveraging predefined positive/negative samples for representation learning. Data aug…
Efficient Token-Guided Image-Text Retrieval with Consistent Multimodal Contrastive Training
Chong Liu, Yuqi Zhang, Hongsong Wang +5
Image-text retrieval is a central problem for understanding the semantic relationship between vision and language, and serves as the basis for various visual and language tasks. Mo…
Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks
Weihua Chen, Xianzhe Xu, Jian Jia +5
Human-centric visual tasks have attracted increasing research attention due to their widespread applications. In this paper, we aim to learn a general human representation from mas…
Revisit Parameter-Efficient Transfer Learning: A Two-Stage Paradigm
Hengyuan Zhao, Hao Luo, Yuyang Zhao +3
Parameter-Efficient Transfer Learning (PETL) aims at efficiently adapting large models pre-trained on massive data to downstream tasks with limited task-specific data. In view of t…
KVT: k-NN Attention for Boosting Vision Transformers
Pichao Wang, Xue Wang, Fan Wang +4
Convolutional Neural Networks (CNNs) have dominated computer vision for years, due to its ability in capturing locality and translation invariance. Recently, many vision transforme…
An Empirical Study of Vehicle Re-Identification on the AI City Challenge
Hao Luo, Weihua Chen, Xianzhe Xu +7
This paper introduces our solution for the Track2 in AI City Challenge 2021 (AICITY21). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synt…
City-Scale Multi-Camera Vehicle Tracking Guided by Crossroad Zones
Chong Liu, Yuqi Zhang, Hao Luo +6
Multi-Target Multi-Camera Tracking has a wide range of applications and is the basis for many advanced inferences and predictions. This paper describes our solution to the Track 3…
Augmented Transformer with Adaptive Graph for Temporal Action Proposal Generation
Shuning Chang, Pichao Wang, Fan Wang +2
Temporal action proposal generation (TAPG) is a fundamental and challenging task in video understanding, especially in temporal action detection. Most previous works focus on captu…
TransReID: Transformer-based Object Re-Identification
Shuting He, Hao Luo, Pichao Wang +3
Extracting robust feature representation is one of the key challenges in object re-identification (ReID). Although convolution neural network (CNN)-based methods have achieved grea…
1st Place Solution to ECCV-TAO-2020: Detect and Represent Any Object for Tracking
Fei Du, Bo Xu, Jiasheng Tang +3
We extend the classical tracking-by-detection paradigm to this tracking-any-object task. Solid detection results are first extracted from TAO dataset. Some state-of-the-art techniq…
1st Place Solution to VisDA-2020: Bias Elimination for Domain Adaptive Pedestrian Re-identification
Jianyang Gu, Hao Luo, Weihua Chen +6
This paper presents our proposed methods for domain adaptive pedestrian re-identification (Re-ID) task in Visual Domain Adaptation Challenge (VisDA-2020). Considering the large gap…
Multi-Domain Learning and Identity Mining for Vehicle Re-Identification
Shuting He, Hao Luo, Weihua Chen +5
This paper introduces our solution for the Track2 in AI City Challenge 2020 (AICITY20). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synt…
3D-Assisted Image Feature Synthesis for Novel Views of an Object
Hao Su, Fan Wang, Li Yi +1
Comparing two images in a view-invariant way has been a challenging problem in computer vision for a long time, as visual features are not stable under large view point changes. In…