9 citations · 28 across the 15 of their papers we have counts for
15 papers
Thinking Like an Expert:Multimodal Hypergraph-of-Thought (HoT) Reasoning to boost Foundation Modals
Fanglong Yao, Changyuan Tian, Jintao Liu +6
Reasoning ability is one of the most crucial capabilities of a foundation model, signifying its capacity to address complex reasoning tasks. Chain-of-Thought (CoT) technique is wid…
Towards Open-World Segmentation of Parts
Tai-Yu Pan, Qing Liu, Wei-Lun Chao +1
Segmenting object parts such as cup handles and animal bodies is important in many real-world applications but requires more annotation effort. The largest dataset nowadays contain…
TOT: Topology-Aware Optimal Transport For Multimodal Hate Detection
Linhao Zhang, Li Jin, Xian Sun +6
Multimodal hate detection, which aims to identify harmful content online such as memes, is crucial for building a wholesome internet environment. Previous work has made enlightenin…
A Light-weight Interpretable Compositional Model for Nuclei Detection and Weakly-Supervised Segmentation
Yixiao Zhang, Adam Kortylewski, Qing Liu +9
The field of computational pathology has witnessed great advancements since deep neural networks have been widely applied. These networks usually require large numbers of annotated…
Visual analogy: Deep learning versus compositional models
Nicholas Ichien, Qing Liu, Shuhao Fu +3
Is analogical reasoning a task that must be learned to solve from scratch by applying deep learning models to massive numbers of reasoning problems? Or are analogies solved by comp…
Weakly Supervised Instance Segmentation for Videos with Temporal Mask Consistency
Qing Liu, Vignesh Ramanathan, Dhruv Mahajan +2
Weakly supervised instance segmentation reduces the cost of annotations required to train models. However, existing approaches which rely only on image-level class labels predomina…
Learning Part Segmentation through Unsupervised Domain Adaptation from Synthetic Vehicles
Qing Liu, Adam Kortylewski, Zhishuai Zhang +7
Part segmentations provide a rich and detailed part-level description of objects. However, their annotation requires an enormous amount of work, which makes it difficult to apply s…
Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion
Adam Kortylewski, Qing Liu, Angtian Wang +2
Computer vision systems in real-world applications need to be robust to partial occlusion while also being explainable. In this work, we show that black-box deep convolutional neur…
Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion
Adam Kortylewski, Ju He, Qing Liu +1
Recent findings show that deep convolutional neural networks (DCNNs) do not generalize well under partial occlusion. Inspired by the success of compositional models at classifying…
Incremental Meta-Learning via Indirect Discriminant Alignment
Qing Liu, Orchid Majumder, Alessandro Achille +3
Majority of the modern meta-learning methods for few-shot classification tasks operate in two phases: a meta-training phase where the meta-learner learns a generic representation b…
Localizing Occluders with Compositional Convolutional Networks
Adam Kortylewski, Qing Liu, Huiyu Wang +2
Compositional convolutional networks are generative compositional models of neural network features, that achieve state of the art results when classifying partially occluded objec…
Combining Compositional Models and Deep Networks For Robust Object Classification under Occlusion
Adam Kortylewski, Qing Liu, Huiyu Wang +2
Deep convolutional neural networks (DCNNs) are powerful models that yield impressive results at object classification. However, recent work has shown that they do not generalize we…
Semantic-Aware Knowledge Preservation for Zero-Shot Sketch-Based Image Retrieval
Qing Liu, Lingxi Xie, Huiyu Wang +1
Sketch-based image retrieval (SBIR) is widely recognized as an important vision problem which implies a wide range of real-world applications. Recently, research interests arise in…
Semantic Part Detection via Matching: Learning to Generalize to Novel Viewpoints from Limited Training Data
Yutong Bai, Qing Liu, Lingxi Xie +3
Detecting semantic parts of an object is a challenging task in computer vision, particularly because it is hard to construct large annotated datasets due to the difficulty of annot…
Few-shot Learning by Exploiting Visual Concepts within CNNs
Boyang Deng, Qing Liu, Siyuan Qiao +1
Convolutional neural networks (CNNs) are one of the driving forces for the advancement of computer vision. Despite their promising performances on many tasks, CNNs still face major…