1k citations · 7.4k across the 81 of their papers we have counts for
84 papers
Divide, Evaluate, and Refine: Evaluating and Improving Text-to-Image Alignment with Iterative VQA Feedback
Jaskirat Singh, Liang Zheng
The field of text-conditioned image generation has made unparalleled progress with the recent advent of latent diffusion models. While remarkable, as the complexity of given text i…
Large-scale Training Data Search for Object Re-identification
Yue Yao, Huan Lei, Tom Gedeon +1
We consider a scenario where we have access to the target domain, but cannot afford on-the-fly training data annotation, and instead would like to construct an alternative training…
Adma-GAN: Attribute-Driven Memory Augmented GANs for Text-to-Image Generation
Xintian Wu, Hanbin Zhao, Liangli Zheng +2
As a challenging task, text-to-image generation aims to generate photo-realistic and semantically consistent images according to the given text descriptions. Existing methods mainl…
Multi-View Correlation Consistency for Semi-Supervised Semantic Segmentation
Yunzhong Hou, Stephen Gould, Liang Zheng
Semi-supervised semantic segmentation needs rich and robust supervision on unlabeled data. Consistency learning enforces the same pixel to have similar features in different augmen…
Multiview Detection with Cardboard Human Modeling
Jiahao Ma, Zicheng Duan, Liang Zheng +1
Multiview detection uses multiple calibrated cameras with overlapping fields of views to locate occluded pedestrians. In this field, existing methods typically adopt a ``human mode…
The 6th AI City Challenge
Milind Naphade, Shuo Wang, David C. Anastasiu +14
The 6th edition of the AI City Challenge specifically focuses on problems in two domains where there is tremendous unlocked potential at the intersection of computer vision and art…
Attribute Descent: Simulating Object-Centric Datasets on the Content Level and Beyond
Yue Yao, Liang Zheng, Xiaodong Yang +2
This article aims to use graphic engines to simulate a large number of training data that have free annotations and possibly strongly resemble to real-world data. Between synthetic…
Intelli-Paint: Towards Developing Human-like Painting Agents
Jaskirat Singh, Cameron Smith, Jose Echevarria +1
The generation of well-designed artwork is often quite time-consuming and assumes a high degree of proficiency on part of the human painter. In order to facilitate the human painti…
Adaptive Affinity for Associations in Multi-Target Multi-Camera Tracking
Yunzhong Hou, Zhongdao Wang, Shengjin Wang +1
Data associations in multi-target multi-camera tracking (MTMCT) usually estimate affinity directly from re-identification (re-ID) feature distances. However, we argue that it might…
Voxelized 3D Feature Aggregation for Multiview Detection
Jiahao Ma, Jinguang Tong, Shan Wang +3
Multi-view detection incorporates multiple camera views to alleviate occlusion in crowded scenes, where the state-of-the-art approaches adopt homography transformations to project…
How to Synthesize a Large-Scale and Trainable Micro-Expression Dataset?
Yuchi Liu, Zhongdao Wang, Tom Gedeon +1
This paper does not contain technical novelty but introduces our key discoveries in a data generation protocol, a database and insights. We aim to address the lack of large-scale d…
Critical local well-posedness for the fully nonlinear Peskin problem
Stephen Cameron, Robert M. Strain
We study the problem where a one-dimensional elastic string is immersed in a two-dimensional steady Stokes fluid. This is known as the Stokes immersed boundary problem and also as…
Hierarchical Image Classification with A Literally Toy Dataset
Long He, Dandan Song, Liang Zheng
Unsupervised domain adaptation (UDA) in image classification remains a big challenge. In existing UDA image dataset, classes are usually organized in a flattened way, where a plain…
Ranking Models in Unlabeled New Environments
Xiaoxiao Sun, Yunzhong Hou, Weijian Deng +2
Consider a scenario where we are supplied with a number of ready-to-use models trained on a certain source domain and hope to directly apply the most appropriate ones to different…
Memory-Free Generative Replay For Class-Incremental Learning
Xiaomeng Xin, Yiran Zhong, Yunzhong Hou +2
Regularization-based methods are beneficial to alleviate the catastrophic forgetting problem in class-incremental learning. With the absence of old task images, they often assume t…
Multiview Detection with Shadow Transformer (and View-Coherent Data Augmentation)
Yunzhong Hou, Liang Zheng
Multiview detection incorporates multiple camera views to deal with occlusions, and its central problem is multiview aggregation. Given feature map projections from multiple views…
What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?
Weijian Deng, Stephen Gould, Liang Zheng
Understanding classifier decision under novel environments is central to the community, and a common practice is evaluating it on labeled test sets. However, in real-world testing,…
Synthetic Data Are as Good as the Real for Association Knowledge Learning in Multi-object Tracking
Yuchi Liu, Zhongdao Wang, Xiangxin Zhou +1
Association, aiming to link bounding boxes of the same identity in a video sequence, is a central component in multi-object tracking (MOT). To train association modules, e.g., para…
The 5th AI City Challenge
Milind Naphade, Shuo Wang, David C. Anastasiu +11
The AI City Challenge was created with two goals in mind: (1) pushing the boundaries of research and development in intelligent video analysis for smarter cities use cases, and (2)…
VTNet: Visual Transformer Network for Object Goal Navigation
Heming Du, Xin Yu, Liang Zheng
Object goal navigation aims to steer an agent towards a target object based on observations of the agent. It is of pivotal importance to design effective visual representations of…
Boosting Semi-Supervised Face Recognition with Noise Robustness
Yuchi Liu, Hailin Shi, Hang Du +4
Although deep face recognition benefits significantly from large-scale training data, a current bottleneck is the labelling cost. A feasible solution to this problem is semi-superv…
Visualizing Adapted Knowledge in Domain Transfer
Yunzhong Hou, Liang Zheng
A source model trained on source data and a target model learned through unsupervised domain adaptation (UDA) usually encode different knowledge. To understand the adaptation proce…
Sparse Attention Guided Dynamic Value Estimation for Single-Task Multi-Scene Reinforcement Learning
Jaskirat Singh, Liang Zheng
Training deep reinforcement learning agents on environments with multiple levels / scenes from the same task, has become essential for many applications aiming to achieve generaliz…
Fine-grained Classification via Categorical Memory Networks
Weijian Deng, Joshua Marsh, Stephen Gould +1
Motivated by the desire to exploit patterns shared across classes, we present a simple yet effective class-specific memory module for fine-grained feature learning. The memory modu…