175 citations · 1.3k across the 83 of their papers we have counts for
73 papers · 1 filter
VTOS: Learning to Orchestrate Vision Tools by Co-Searching Solutions and Observers
Jinchao Ge, Lingqiao Liu, Shuwen Zhao +1
Vision foundation tools such as open-vocabulary detectors, segmentation models, and post-processing operators are powerful building blocks for computer vision, but their effectiven…
Domain Generalization via Rationale Invariance
Liang Chen, Yong Zhang, Yibing Song +2
This paper offers a new perspective to ease the challenge of domain generalization, which involves maintaining robust results even in unseen environments. Our design focuses on the…
Learning Conditional Attributes for Compositional Zero-Shot Learning
Qingsheng Wang, Lingqiao Liu, Chenchen Jing +4
Compositional Zero-Shot Learning (CZSL) aims to train models to recognize novel compositional concepts based on learned concepts such as attribute-object combinations. One of the c…
The CLIP Model is Secretly an Image-to-Prompt Converter
Yuxuan Ding, Chunna Tian, Haoxuan Ding +1
The Stable Diffusion model is a prominent text-to-image generation model that relies on a text prompt as its input, which is encoded using the Contrastive Language-Image Pre-Traini…
Improved Test-Time Adaptation for Domain Generalization
Liang Chen, Yong Zhang, Yibing Song +2
The main challenge in domain generalization (DG) is to handle the distribution shift problem that lies between the training and test data. Recent studies suggest that test-time tra…
METransformer: Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens
Zhanyu Wang, Lingqiao Liu, Lei Wang +1
In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a "multi-expert joint di…
Revisiting Image Reconstruction for Semi-supervised Semantic Segmentation
Yuhao Lin, Haiming Xu, Lingqiao Liu +2
Autoencoding, which aims to reconstruct the input images through a bottleneck latent representation, is one of the classic feature representation learning strategies. It has been s…
Learning Common Rationale to Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems
Yangyang Shu, Anton van den Hengel, Lingqiao Liu
Self-supervised learning (SSL) strategies have demonstrated remarkable performance in various recognition tasks. However, both our preliminary investigation and recent studies sugg…
Generalizable Person Re-Identification via Viewpoint Alignment and Fusion
Bingliang Jiao, Lingqiao Liu, Liying Gao +5
In the current person Re-identification (ReID) methods, most domain generalization works focus on dealing with style differences between domains while largely ignoring unpredictabl…
ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation
Ziqin Zhou, Bowen Zhang, Yinjie Lei +2
Recently, CLIP has been applied to pixel-level zero-shot learning tasks via a two-stage scheme. The general idea is to first generate class-agnostic region proposals and then feed…
Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization
Hai-Ming Xu, Lingqiao Liu, Qiuchen Bian +1
Semi-supervised semantic segmentation requires the model to effectively propagate the label information from limited annotated images to unlabeled ones. A challenge for such a per-…
Regularizing Neural Network Training via Identity-wise Discriminative Feature Suppression
Avraham Chapman, Lingqiao Liu
It is well-known that a deep neural network has a strong fitting capability and can easily achieve a low training error even with randomly assigned class labels. When the number of…
Dual Decision Improves Open-Set Panoptic Segmentation
Hai-Ming Xu, Hao Chen, Lingqiao Liu +1
Open-set panoptic segmentation (OPS) problem is a new research direction aiming to perform segmentation for both \known classes and \unknown classes, i.e., the objects ("things") t…
Single-Stage Open-world Instance Segmentation with Cross-task Consistency Regularization
Xizhe Xue, Dongdong Yu, Lingqiao Liu +6
Open-World Instance Segmentation (OWIS) is an emerging research topic that aims to segment class-agnostic object instances from images. The mainstream approaches use a two-stage se…
Improving Fine-Grained Visual Recognition in Low Data Regimes via Self-Boosting Attention Mechanism
Yangyang Shu, Baosheng Yu, Haiming Xu +1
The challenge of fine-grained visual recognition often lies in discovering the key discriminative regions. While such regions can be automatically identified from a large-scale lab…
Don't Stop Learning: Towards Continual Learning for the CLIP Model
Yuxuan Ding, Lingqiao Liu, Chunna Tian +2
The Contrastive Language-Image Pre-training (CLIP) Model is a recently proposed large-scale pre-train model which attracts increasing attention in the computer vision community. Be…
Learning Resolution-Adaptive Representations for Cross-Resolution Person Re-Identification
Lin Wu, Lingqiao Liu, Yang Wang +3
The cross-resolution person re-identification (CRReID) problem aims to match low-resolution (LR) query identity images against high resolution (HR) gallery images. It is a challeng…
Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection
Liang Chen, Yong Zhang, Yibing Song +2
Recent studies in deepfake detection have yielded promising results when the training and testing face forgeries are from the same dataset. However, the problem remains challenging…
Multi-Domain Joint Training for Person Re-Identification
Lu Yang, Lingqiao Liu, Yunlong Wang +2
Deep learning-based person Re-IDentification (ReID) often requires a large amount of training data to achieve good performance. Thus it appears that collecting more training data f…
Global and Local Texture Randomization for Synthetic-to-Real Semantic Segmentation
Duo Peng, Yinjie Lei, Lingqiao Liu +2
Semantic segmentation is a crucial image understanding task, where each pixel of image is categorized into a corresponding label. Since the pixel-wise labeling for ground-truth is…
CAT: Cross-Attention Transformer for One-Shot Object Detection
Weidong Lin, Yuyan Deng, Yang Gao +5
Given a query patch from a novel class, one-shot object detection aims to detect all instances of that class in a target image through the semantic similarity comparison. However,…
Center Prediction Loss for Re-identification
Lu Yang, Yunlong Wang, Lingqiao Liu +5
The training loss function that enforces certain training sample distribution patterns plays a critical role in building a re-identification (ReID) system. Besides the basic requir…
Pluggable Weakly-Supervised Cross-View Learning for Accurate Vehicle Re-Identification
Lu Yang, Hongbang Liu, Jinghao Zhou +4
Learning cross-view consistent feature representation is the key for accurate vehicle Re-identification (ReID), since the visual appearance of vehicles changes significantly under…
Where to Look and How to Describe: Fashion Image Retrieval with an Attentional Heterogeneous Bilinear Network
Haibo Su, Peng Wang, Lingqiao Liu +3
Fashion products typically feature in compositions of a variety of styles at different clothing parts. In order to distinguish images of different fashion products, we need to extr…