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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. 

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2. Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization Without Accessing Target Domain Data

   https://doi.org/10.1109/ICCV.2019.00219  

   Yue, Xiangyu ; Zhang, Yang ; Zhao, Sicheng ; Sangiovanni-Vincentelli, Alberto ; Keutzer, Kurt ; Gong, Boqing ( October 2019 , IEEE International Conference on Computer Vision)

   We propose to harness the potential of simulation for the semantic segmentation of real-world self-driving scenes in a domain generalization fashion. The segmentation network is trained without any data of target domains and tested on the unseen target domains. To this end, we propose a new approach of domain randomization and pyramid consistency to learn a model with high generalizability. First, we propose to randomize the synthetic images with the styles of real images in terms of visual appearances using auxiliary datasets, in order to effectively learn domain-invariant representations. Second, we further enforce pyramid consistency across different “stylized” images and within an image, in order to learn domaininvariant and scale-invariant features, respectively. Extensive experiments are conducted on the generalization from GTA and SYNTHIA to Cityscapes, BDDS and Mapillary; and our method achieves superior results over the stateof- the-art techniques. Remarkably, our generalization results are on par with or even better than those obtained by state-of-the-art simulation-to-real domain adaptation methods, which access the target domain data at training time. 

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3. Object Pose Estimation using Mid-level Visual Representations

   Negar Nejatishahidin ; Pooya Fayyazsanavi ; Jana Košecka ( October 2022 , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   This work proposes a novel pose estimation model for object categories that can be effectively transferred to pre-viously unseen environments. The deep convolutional network models (CNN) for pose estimation are typically trained and evaluated on datasets specifically curated for object detection, pose estimation, or 3D reconstruction, which requires large amounts of training data. In this work, we propose a model for pose estimation that can be trained with small amount of data and is built on the top of generic mid-level represen-tations [33] (e.g. surface normal estimation and re-shading). These representations are trained on a large dataset without requiring pose and object annotations. Later on, the predictions are refined with a small CNN neural network that exploits object masks and silhouette retrieval. The presented approach achieves superior performance on the Pix3D dataset [26] and shows nearly 35 % improvement over the existing models when only 25 % of the training data is available. We show that the approach is favorable when it comes to generalization and transfer to novel environments. Towards this end, we introduce a new pose estimation benchmark for commonly encountered furniture categories on challenging Active Vision Dataset [1] and evaluated the models trained on the Pix3D dataset. 

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4. DANCE: DAta-Network Co-optimization for Efficient Segmentation Model Training and Inference

   https://doi.org/10.1145/3510835  

   Li, Chaojian ; Chen, Wuyang ; Gu, Yuchen ; Chen, Tianlong ; Fu, Yonggan ; Wang, Zhangyang ; Lin, Yingyan ( September 2022 , ACM Transactions on Design Automation of Electronic Systems)

   Semantic segmentation for scene understanding is nowadays widely demanded, raising significant challenges for the algorithm efficiency, especially its applications on resource-limited platforms. Current segmentation models are trained and evaluated on massive high-resolution scene images (“data-level”) and suffer from the expensive computation arising from the required multi-scale aggregation (“network level”). In both folds, the computational and energy costs in training and inference are notable due to the often desired large input resolutions and heavy computational burden of segmentation models. To this end, we propose DANCE, general automated DA ta- N etwork C o-optimization for E fficient segmentation model training and inference . Distinct from existing efficient segmentation approaches that focus merely on light-weight network design, DANCE distinguishes itself as an automated simultaneous data-network co-optimization via both input data manipulation and network architecture slimming. Specifically, DANCE integrates automated data slimming which adaptively downsamples/drops input images and controls their corresponding contribution to the training loss guided by the images’ spatial complexity. Such a downsampling operation, in addition to slimming down the cost associated with the input size directly, also shrinks the dynamic range of input object and context scales, therefore motivating us to also adaptively slim the network to match the downsampled data. Extensive experiments and ablating studies (on four SOTA segmentation models with three popular segmentation datasets under two training settings) demonstrate that DANCE can achieve “all-win” towards efficient segmentation (reduced training cost, less expensive inference, and better mean Intersection-over-Union (mIoU)). Specifically, DANCE can reduce ↓25%–↓77% energy consumption in training, ↓31%–↓56% in inference, while boosting the mIoU by ↓0.71%–↑ 13.34%. 

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5. GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features with Spatial and Channel Attention

   https://doi.org/10.3390/s21227504  

   Sharma, Udit ; Artacho, Bruno ; Savakis, Andreas ( November 2021 , Sensors)

   We propose GourmetNet, a single-pass, end-to-end trainable network for food segmentation that achieves state-of-the-art performance. Food segmentation is an important problem as the first step for nutrition monitoring, food volume and calorie estimation. Our novel architecture incorporates both channel attention and spatial attention information in an expanded multi-scale feature representation using our advanced Waterfall Atrous Spatial Pooling module. GourmetNet refines the feature extraction process by merging features from multiple levels of the backbone through the two attention modules. The refined features are processed with the advanced multi-scale waterfall module that combines the benefits of cascade filtering and pyramid representations without requiring a separate decoder or post-processing. Our experiments on two food datasets show that GourmetNet significantly outperforms existing current state-of-the-art methods. 

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