More Like this

1. Weakly-Supervised Object Representation Learning for Few-Shot Semantic Segmentation

   Ying, Xiaowen ; Li, Xin ; Chuah, Mooi Choo ( January 2021 , IEEE Winter Conference on Applications of Computer Vision)

   null (Ed.)

   Training a semantic segmentation model requires large densely-annotated image datasets that are costly to obtain. Once the training is done, it is also difficult to add new ob- ject categories to such segmentation models. In this pa- per, we tackle the few-shot semantic segmentation prob- lem, which aims to perform image segmentation task on un- seen object categories merely based on one or a few sup- port example(s). The key to solving this few-shot segmen- tation problem lies in effectively utilizing object informa- tion from support examples to separate target objects from the background in a query image. While existing meth- ods typically generate object-level representations by av- eraging local features in support images, we demonstrate that such object representations are typically noisy and less distinguishing. To solve this problem, we design an ob- ject representation generator (ORG) module which can ef- fectively aggregate local object features from support im- age(s) and produce better object-level representation. The ORG module can be embedded into the network and trained end-to-end in a weakly-supervised fashion without extra hu- man annotation. We incorporate this design into a modified encoder-decoder network to present a powerful and efficient framework for few-shot semantic segmentation. Experimen- tal results on the Pascal-VOC and MS-COCO datasets show that our approach achieves better performance compared to existing methods under both one-shot and five-shot settings. 

   more » « less
2. Attentive Relational Networks for Mapping Images to Scene Graphs

   Qi, Mengshi ; Li, Weijian Li ; Yang, Zhengyuan ; Wang, Yunhong ; Luo, Jiebo ( January 2019 , Proceedings of IEEE Conference on Computer Vision and Pattern Recognition)

   Scene graph generation refers to the task of automatically mapping an image into a semantic structural graph, which requires correctly labeling each extracted object and their interaction relationships. Despite the recent success in object detection using deep learning techniques, inferring complex contextual relationships and structured graph representations from visual data remains a challenging topic. In this study, we propose a novel Attentive Relational Network that consists of two key modules with an object detection backbone to approach this problem. The first module is a semantic transformation module utilized to capture semantic embedded relation features, by translating visual features and linguistic features into a common semantic space. The other module is a graph self-attention module introduced to embed a joint graph representation through assigning various importance weights to neighboring nodes. Finally, accurate scene graphs are produced by the relation inference module to recognize all entities and the corresponding relations. We evaluate our proposed method on the widely-adopted Visual Genome dataset, and the results demonstrate the effectiveness and superiority of our model. 

   more » « less
3. Hyperbolic Contrastive Learning for Visual Representations beyond Objects

   https://doi.org/10.1109/CVPR52729.2023.00661  

   Ge, Songwei ; Mishra, Shlok ; Kornblith, Simon ; Li, Chun-Liang ; Jacobs, David ( June 2023 , IEEE)

   Although self-/un-supervised methods have led to rapid progress in visual representation learning, these methods generally treat objects and scenes using the same lens. In this paper, we focus on learning representations for objects and scenes that preserve the structure among them. Motivated by the observation that visually similar objects are close in the representation space, we argue that the scenes and objects should instead follow a hierarchical structure based on their compositionality. To exploit such a structure, we propose a contrastive learning framework where a Euclidean loss is used to learn object representations and a hyperbolic loss is used to encourage representations of scenes to lie close to representations of their constituent objects in a hyperbolic space. This novel hyperbolic objective encourages the scene-object hypernymy among the representations by optimizing the magnitude of their norms. We show that when pretraining on the COCO and OpenImages datasets, the hyperbolic loss improves downstream performance of several baselines across multiple datasets and tasks, including image classification, object detection, and semantic segmentation. We also show that the properties of the learned representations allow us to solve various vision tasks that involve the interaction between scenes and objects in a zero-shot fashion. 

   more » « less
4. Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

   Ruining Deng*, Can Cui* ( July 2023 , Medical Imaging with Deep Learning (MIDL))

   The segment anything model (SAM) was released as a foundation model for image segmentation. The promptable segmentation model was trained by over 1 billion masks on 11M licensed and privacy-respecting images. The model supports zero-shot image segmentation with various seg- mentation prompts (e.g., points, boxes, masks). It makes the SAM attractive for medical image analysis, especially for digital pathology where the training data are rare. In this study, we eval- uate the zero-shot segmentation performance of SAM model on representative segmentation tasks on whole slide imaging (WSI), including (1) tumor segmentation, (2) non-tumor tissue segmen- tation, (3) cell nuclei segmentation. Core Results: The results suggest that the zero-shot SAM model achieves remarkable segmentation performance for large connected objects. However, it does not consistently achieve satisfying performance for dense instance object segmentation, even with 20 prompts (clicks/boxes) on each image. We also summarized the identified limitations for digital pathology: (1) image resolution, (2) multiple scales, (3) prompt selection, and (4) model fine-tuning. In the future, the few-shot fine-tuning with images from downstream pathological seg- mentation tasks might help the model to achieve better performance in dense object segmentation. 

   more » « less
5. Uncertainty Aware Proposal Segmentation for Unknown Object Detection

   Yimeng Li and Jana Kosecka ( January 2022 , Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision)

   Recent efforts in deploying Deep Neural Networks for object detection in real world applications, such as autonomous driving, assume that all relevant object classes have been observed during training. Quantifying the performance of these models in settings when the test data is not represented in the training set has mostly focused on pixel-level uncertainty estimation techniques of models trained for semantic segmentation. This paper proposes to exploit additional predictions of semantic segmentation models and quantifying its confidences, followed by classification of object hypotheses as known vs. unknown, out of distribution objects. We use object proposals generated by Region Proposal Network (RPN) and adapt distance aware uncertainty estimation of semantic segmentation using Radial Basis Functions Networks (RBFN) for class agnostic object mask prediction. The augmented object proposals are then used to train a classifier for known vs. unknown objects categories. Experimental results demonstrate that the proposed method achieves parallel performance to state of the art methods for unknown object detection and can also be used effectively for reducing object detectors' false positive rate. Our method is well suited for applications where prediction of non-object background categories obtained by semantic segmentation is reliable. 

   more » « less