32 citations · 141 across the 25 of their papers we have counts for
25 papers
InstaGen: Enhancing Object Detection by Training on Synthetic Dataset
Chengjian Feng, Yujie Zhong, Zequn Jie +2
In this paper, we present a novel paradigm to enhance the ability of object detector, e.g., expanding categories or improving detection performance, by training on synthetic datase…
MotionTrack: Learning Motion Predictor for Multiple Object Tracking
Changcheng Xiao, Qiong Cao, Yujie Zhong +4
Significant progress has been achieved in multi-object tracking (MOT) through the evolution of detection and re-identification (ReID) techniques. Despite these advancements, accura…
Intelligent Grimm -- Open-ended Visual Storytelling via Latent Diffusion Models
Chang Liu, Haoning Wu, Yujie Zhong +3
Generative models have recently exhibited exceptional capabilities in text-to-image generation, but still struggle to generate image sequences coherently. In this work, we focus on…
Bridging the Gap Between End-to-end and Non-End-to-end Multi-Object Tracking
Feng Yan, Weixin Luo, Yujie Zhong +2
Existing end-to-end Multi-Object Tracking (e2e-MOT) methods have not surpassed non-end-to-end tracking-by-detection methods. One potential reason is its label assignment strategy d…
Open-Vocabulary Semantic Segmentation with Decoupled One-Pass Network
Cong Han, Yujie Zhong, Dengjie Li +2
Recently, the open-vocabulary semantic segmentation problem has attracted increasing attention and the best performing methods are based on two-stream networks: one stream for prop…
Adaptive Sparse Pairwise Loss for Object Re-Identification
Xiao Zhou, Yujie Zhong, Zhen Cheng +2
Object re-identification (ReID) aims to find instances with the same identity as the given probe from a large gallery. Pairwise losses play an important role in training a strong R…
TriDet: Temporal Action Detection with Relative Boundary Modeling
Dingfeng Shi, Yujie Zhong, Qiong Cao +3
In this paper, we present a one-stage framework TriDet for temporal action detection. Existing methods often suffer from imprecise boundary predictions due to the ambiguous action…
DiP: Learning Discriminative Implicit Parts for Person Re-Identification
Dengjie Li, Siyu Chen, Yujie Zhong +1
In person re-identification (ReID) tasks, many works explore the learning of part features to improve the performance over global image features. Existing methods explicitly extrac…
AeDet: Azimuth-invariant Multi-view 3D Object Detection
Chengjian Feng, Zequn Jie, Yujie Zhong +2
Recent LSS-based multi-view 3D object detection has made tremendous progress, by processing the features in Brid-Eye-View (BEV) via the convolutional detector. However, the typical…
Contrastive Video-Language Learning with Fine-grained Frame Sampling
Zixu Wang, Yujie Zhong, Yishu Miao +2
Despite recent progress in video and language representation learning, the weak or sparse correspondence between the two modalities remains a bottleneck in the area. Most video-lan…
CounTR: Transformer-based Generalised Visual Counting
Chang Liu, Yujie Zhong, Andrew Zisserman +1
In this paper, we consider the problem of generalised visual object counting, with the goal of developing a computational model for counting the number of objects from arbitrary se…
ReAct: Temporal Action Detection with Relational Queries
Dingfeng Shi, Yujie Zhong, Qiong Cao +4
This work aims at advancing temporal action detection (TAD) using an encoder-decoder framework with action queries, similar to DETR, which has shown great success in object detecti…
Cross-Architecture Self-supervised Video Representation Learning
Sheng Guo, Zihua Xiong, Yujie Zhong +4
In this paper, we present a new cross-architecture contrastive learning (CACL) framework for self-supervised video representation learning. CACL consists of a 3D CNN and a video tr…
DearKD: Data-Efficient Early Knowledge Distillation for Vision Transformers
Xianing Chen, Qiong Cao, Yujie Zhong +3
Transformers are successfully applied to computer vision due to their powerful modeling capacity with self-attention. However, the excellent performance of transformers heavily dep…
PromptDet: Towards Open-vocabulary Detection using Uncurated Images
Chengjian Feng, Yujie Zhong, Zequn Jie +5
The goal of this work is to establish a scalable pipeline for expanding an object detector towards novel/unseen categories, using zero manual annotations. To achieve that, we make…
InsCLR: Improving Instance Retrieval with Self-Supervision
Zelu Deng, Yujie Zhong, Sheng Guo +1
This work aims at improving instance retrieval with self-supervision. We find that fine-tuning using the recently developed self-supervised (SSL) learning methods, such as SimCLR a…
OH-Former: Omni-Relational High-Order Transformer for Person Re-Identification
Xianing Chen, Chunlin Xu, Qiong Cao +6
Transformers have shown preferable performance on many vision tasks. However, for the task of person re-identification (ReID), vanilla transformers leave the rich contexts on high-…
TOOD: Task-aligned One-stage Object Detection
Chengjian Feng, Yujie Zhong, Yu Gao +2
One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to…
Exploring Classification Equilibrium in Long-Tailed Object Detection
Chengjian Feng, Yujie Zhong, Weilin Huang
The conventional detectors tend to make imbalanced classification and suffer performance drop, when the distribution of the training data is severely skewed. In this paper, we prop…
Mutually-aware Sub-Graphs Differentiable Architecture Search
Haoxian Tan, Sheng Guo, Yujie Zhong +2
Differentiable architecture search is prevalent in the field of NAS because of its simplicity and efficiency, where two paradigms, multi-path algorithms and single-path methods, ar…
Unchain the Search Space with Hierarchical Differentiable Architecture Search
Guanting Liu, Yujie Zhong, Sheng Guo +2
Differentiable architecture search (DAS) has made great progress in searching for high-performance architectures with reduced computational cost. However, DAS-based methods mainly…
Watch and Learn: Mapping Language and Noisy Real-world Videos with Self-supervision
Yujie Zhong, Linhai Xie, Sen Wang +2
In this paper, we teach machines to understand visuals and natural language by learning the mapping between sentences and noisy video snippets without explicit annotations. Firstly…
Representation Sharing for Fast Object Detector Search and Beyond
Yujie Zhong, Zelu Deng, Sheng Guo +2
Region Proposal Network (RPN) provides strong support for handling the scale variation of objects in two-stage object detection. For one-stage detectors which do not have RPN, it i…
Compact Deep Aggregation for Set Retrieval
Yujie Zhong, Relja Arandjelović, Andrew Zisserman
The objective of this work is to learn a compact embedding of a set of descriptors that is suitable for efficient retrieval and ranking, whilst maintaining discriminability of the…