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The ninth annual Spivey Hill Challenge is approaching in November honoring Dr. Jamie Epperson, Bill Spivey and Spencer Street III. The 5K race benefits two local organizations each year which give back to the community. The race, which includes a 5K (virtual and in-person), 1K Fun Run and Walk &amp…

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A pair of suspects recently went on a shopping spree at the Target located at the Spectrum in Irvine. They walked out of the store with over $1,000 in merchandise recently including a Pokémon

The Irvine Police are searching for a couple that stole over $1K in merchandise from a Target store

A Class-A office building in Schaumburg sold to a buyer with creative plans to convert the space. Lee & Associates negotiated the $3.52M sale of a 106.3K SF, five-story office building at 930 National Parkway in Schaumburg to National India Hub Foundation.

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Get the most recent info and news about 1k on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

1K is a hyperlocal omnichannel grocery chain focused on fulfilling the aspirations of 'Real Bharat'. We believe that to create wow micro-experiences for the consumers, kirana entrepreneurs need the means to use technology as an enabler. With the changing consumer aspirations, the kirana entrepreneurs also aspire for more. Fuelled by the ambition of building ‘Bharat's’ first omnichannel distribution network that brings consumers' aspirations closer to them, 1K is blazing the path to modernizing the traditional Kirana stores.

I recently launched Postiz, an open-source social media scheduling tool. After four months, it's making $2.1k per month! Thanks to open-source software in one of the most competitive markets, you can do the same. Here is how. Find an Idea 💡 You don't have to have an original idea; you can take something already existing and open-source it (of course, make it good). Why? Because once you have open-sourced it, you turn to a new audience that was never served before - Developers and companies that want to self-host your product (primarily because of privacy.) I prefer to find big markets. For example, Postiz is a social media scheduling tool. It's one of the most saturated markets, meaning many people and underserved audiences are looking for it. You also get tremendous credibility by showing all your code and get more credibility by getting stars. Market It 🏪 Every product needs marketing; you can imagine from this post that I have some sense of it (Squid game picture + title); attracting more people is essential to get the initial visibility. Open source gets some superpowers because it allows you to promote it in channels usually unavailable in typical startups. You can find a lot of content about it on my website Gitroom. In short: Post your repository every time you have a new version on Reddit /r/selfhosted
If you have an AI startup, post it on /r/LocalLLaMA
Post it in Lemmy's self-hosted community
Post it on DEV.to/HackerNoon/Medium/Hashnode every week
Post it on Hackernews: "Show HN: product name"
Post it on LinkedIn/X as much as you can. Use Postiz for that 😉 If you do it often, you might be trending on GitHub, increasing your visibility by 10x. Then, use your audience to post it on Product Hunt and get upvotes from the community members. Postiz finished 1st of the day/week/month. Collect Feedback 💭 Using Discord and GitHub issues, I am collecting tons of feedback on what to implement and what not. With over 800k downloads from Docker, tons of people are always playing with it and giving feedback. When you implement feedback, people trust you more, and you learn faster about your product. Just Do It ✔ Stop looking for excuses. Just write some code - the nice thing about open-source is that you don't even have to start by making the main website - build the open-source! And of course! Help me out if you can ❤️ I'm happy to get a star to produce more features for the open-source! https://github.com/gitroomhq/postiz-app/ I recently launched Postiz , an open-source social media scheduling tool. After four months, it's making $2.1k per month! Thanks to open-source software in one of the most competitive markets, you can do the same. Postiz Postiz Here is how. Find an Idea 💡 You don't have to have an original idea; you can take something already existing and open-source it (of course, make it good). Why? Because once you have open-sourced it, you turn to a new audience that was never served before - Developers and companies that want to self-host your product (primarily because of privacy.) I prefer to find big markets. For example, Postiz is a social media scheduling tool. It's one of the most saturated markets, meaning many people and underserved audiences are looking for it. You also get tremendous credibility by showing all your code and get more credibility by getting stars. Market It 🏪 Every product needs marketing; you can imagine from this post that I have some sense of it (Squid game picture + title); attracting more people is essential to get the initial visibility. Open source gets some superpowers because it allows you to promote it in channels usually unavailable in typical startups. You can find a lot of content about it on my website Gitroom . Gitroom Gitroom In short: Post your repository every time you have a new version on Reddit /r/selfhosted If you have an AI startup, post it on /r/LocalLLaMA Post it in Lemmy's self-hosted community Post it on DEV.to/HackerNoon/Medium/Hashnode every week Post it on Hackernews: "Show HN: product name" Post it on LinkedIn/X as much as you can. Use Postiz for that 😉 Post your repository every time you have a new version on Reddit /r/selfhosted /r/selfhosted /r/selfhosted If you have an AI startup, post it on /r/LocalLLaMA /r/LocalLLaMA /r/LocalLLaMA Post it in Lemmy's self-hosted community self-hosted community self-hosted community Post it on DEV.to/HackerNoon/Medium/Hashnode every week Post it on Hackernews : "Show HN: product name" Hackernews Hackernews Post it on LinkedIn/X as much as you can. Use Postiz for that 😉 Postiz Postiz If you do it often, you might be trending on GitHub , increasing your visibility by 10x. trending on GitHub trending on GitHub Then, use your audience to post it on Product Hunt and get upvotes from the community members. Postiz finished 1st of the day/week/month. Product Hunt Product Hunt Collect Feedback 💭 Using Discord and GitHub issues, I am collecting tons of feedback on what to implement and what not. With over 800k downloads from Docker, tons of people are always playing with it and giving feedback. When you implement feedback, people trust you more, and you learn faster about your product. Just Do It ✔ Stop looking for excuses. Just write some code - the nice thing about open-source is that you don't even have to start by making the main website - build the open-source! And of course! Help me out if you can ❤️ I'm happy to get a star to produce more features for the open-source! https://github.com/gitroomhq/postiz-app/ https://github.com/gitroomhq/postiz-app/ https://github.com/gitroomhq/postiz-app/

4 Months, $2.1k Per Month - All Open-Source Here's How

How to use idb, a 1kb package that makes IndexedDB easy

Why The Hell is Observability So Darn Expensive!?

¿Por qué carajo es la observabilidad tan cara?

Pourquoi diable l'observabilité est-elle si chère !?

Poukisa lanfè a se Obsèvasyon tèlman chè!?

Perché diavolo l'osservabilità è così dannatamente costosa!?

למה לעזאזל יכולת התצפית כל כך יקרה!?

一体なぜ、Observability はこんなにも高価なのか?

Kāpēc pie velna novērojamība ir tik dārga!?

Waarom is observeerbaarheid zo ontzettend duur!?

Kuki Ikuzimu ari Indorerezi kuburyo Darn ihenze!?

Prečo je do pekla pozorovateľnosť taká drahá!?

Waa maxay sababta Jahannamo u fiirsigu u yahay Darn qaali!?

ஏன் நரகம் கவனிக்கத்தக்கது மிகவும் விலை உயர்ந்தது!?

Authors:
(1) Hyeongjun Kwon, Yonsei University;
(2) Jinhyun Jang, Yonsei University;
(3) Jin Kim, Yonsei University;
(4) Kwonyoung Kim, Yonsei University;
(5) Kwanghoon Sohn, Yonsei University and Korea Institute of Science and Technology (KIST). Table of Links Abstract and 1 Introduction 2. Related Work 3. Hyperbolic Geometry 4. Method 4.1. Overview 4.2. Probabilistic hierarchy tree 4.3. Visual hierarchy decomposition 4.4. Learning hierarchy in hyperbolic space 4.5. Visual hierarchy encoding 5. Experiments and 5.1. Image classification 5.2. Object detection and Instance segmentation 5.3. Semantic segmentation 5.4. Visualization 6. Ablation studies and discussion 7. Conclusion and References A. Network Architecture B. Theoretical Baseline C. Additional Results D. Additional visualization C. Additional Results C.1. Fine-tuning vs. full-training. We also investigate the effectiveness of our proposed method when it is applied to training the model from scratch. For fair comparisons, we evaluate the classification performance of Hi-Mapper trained with the full-training scheme (350 epochs) and fine-tuning scheme (baseline + 50 epochs) of the same learning objectives on ImageNet-1K [36]. As shown in Tab. 6, the experimental results demonstrate that the finetuning scheme is better-suitable than full-training in terms of understanding the structural organization of visual scenes. D. Additional visualization For a more comprehensive understanding, we will provide additional visualization results that are included in the main paper and also examine the visual hierarchy in CNNs [59], as shown in Figure 7, 8. This will offer insights into the feature representation aspects in transformer structures and CNNs, as well as the benefits of applying our method. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Hyeongjun Kwon, Yonsei University; (2) Jinhyun Jang, Yonsei University; (3) Jin Kim, Yonsei University; (4) Kwonyoung Kim, Yonsei University; (5) Kwanghoon Sohn, Yonsei University and Korea Institute of Science and Technology (KIST). Authors: Authors: (1) Hyeongjun Kwon, Yonsei University; (2) Jinhyun Jang, Yonsei University; (3) Jin Kim, Yonsei University; (4) Kwonyoung Kim, Yonsei University; (5) Kwanghoon Sohn, Yonsei University and Korea Institute of Science and Technology (KIST). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Work 2. Related Work 3. Hyperbolic Geometry 3. Hyperbolic Geometry 4. Method 4.1. Overview 4.1. Overview 4.2. Probabilistic hierarchy tree 4.2. Probabilistic hierarchy tree 4.3. Visual hierarchy decomposition 4.3. Visual hierarchy decomposition 4.4. Learning hierarchy in hyperbolic space 4.4. Learning hierarchy in hyperbolic space 4.5. Visual hierarchy encoding 4.5. Visual hierarchy encoding 5. Experiments and 5.1. Image classification 5. Experiments and 5.1. Image classification 5.2. Object detection and Instance segmentation 5.2. Object detection and Instance segmentation 5.3. Semantic segmentation 5.3. Semantic segmentation 5.4. Visualization 5.4. Visualization 6. Ablation studies and discussion 6. Ablation studies and discussion 7. Conclusion and References 7. Conclusion and References A. Network Architecture A. Network Architecture B. Theoretical Baseline B. Theoretical Baseline C. Additional Results C. Additional Results D. Additional visualization D. Additional visualization C. Additional Results C.1. Fine-tuning vs. full-training. We also investigate the effectiveness of our proposed method when it is applied to training the model from scratch. For fair comparisons, we evaluate the classification performance of Hi-Mapper trained with the full-training scheme (350 epochs) and fine-tuning scheme (baseline + 50 epochs) of the same learning objectives on ImageNet-1K [36]. As shown in Tab. 6, the experimental results demonstrate that the finetuning scheme is better-suitable than full-training in terms of understanding the structural organization of visual scenes. full-training fine-tuning finetuning full-training D. Additional visualization For a more comprehensive understanding, we will provide additional visualization results that are included in the main paper and also examine the visual hierarchy in CNNs [59], as shown in Figure 7, 8. This will offer insights into the feature representation aspects in transformer structures and CNNs, as well as the benefits of applying our method. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

What is the Best Way to Train AI Models? 

Authors:
(1) Hyeongjun Kwon, Yonsei University;
(2) Jinhyun Jang, Yonsei University;
(3) Jin Kim, Yonsei University;
(4) Kwonyoung Kim, Yonsei University;
(5) Kwanghoon Sohn, Yonsei University and Korea Institute of Science and Technology (KIST). Table of Links Abstract and 1 Introduction 2. Related Work 3. Hyperbolic Geometry 4. Method 4.1. Overview 4.2. Probabilistic hierarchy tree 4.3. Visual hierarchy decomposition 4.4. Learning hierarchy in hyperbolic space 4.5. Visual hierarchy encoding 5. Experiments and 5.1. Image classification 5.2. Object detection and Instance segmentation 5.3. Semantic segmentation 5.4. Visualization 6. Ablation studies and discussion 7. Conclusion and References A. Network Architecture B. Theoretical Baseline C. Additional Results D. Additional visualization 6. Ablation studies and discussion To further analyze and validate the components of our method, we conduct ablation studies on image classification. Effectiveness of hyperbolic manifolds. We first investigate the effectiveness of hyperbolic manifolds in our approach. As shown in Tab. 4, we report the impact according to image classification. In Euclidean space, the distance function between two vectors is the cosine similarity function. The results demonstrate that applying hierarchical contrastive loss in Euclidean space degrades performance. It indicates that hyperbolic space is more suitable for stabilizing hierarchical structures. Additionally, the application of a KL loss term shows further benefits derived from the semantic seed distribution. Impact of probabilistic modeling. In Tab. 5, we report the performance comparisons between the probabilistic hierarchy tree and the deterministic hierarchy tree. For constructing a hierarchy tree, probabilistic modeling defines every node via MoG of its child node distributions, while the deterministic approach determines each node the mean of its child nodes. The probabilistic hierarchy tree achieves significant improvement in performance compared to the deterministic approach. This result shows that probabilistic modeling is more effective in representing hierarchical structure than deterministic modeling, leading to improvement in recognition. Hierarchy width and depth. As shown in Fig. 5, we analyze the effect of the width N and depth L of the hierarchy tree on ImageNet-1K [36] with Hi-Mapper(DeiTS). These factors control the granularity of visual elements to be decomposed. While a small number of N degrades the fine-grained recognition capacity, an excess N hinders the optimization. Meanwhile, a large L may provide diverse granularity, however, it leads to entangled object-level representations. In all the cases, we report the best performance of 82.6% at N=32, L = 4. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Hyeongjun Kwon, Yonsei University; (2) Jinhyun Jang, Yonsei University; (3) Jin Kim, Yonsei University; (4) Kwonyoung Kim, Yonsei University; (5) Kwanghoon Sohn, Yonsei University and Korea Institute of Science and Technology (KIST). Authors: Authors: (1) Hyeongjun Kwon, Yonsei University; (2) Jinhyun Jang, Yonsei University; (3) Jin Kim, Yonsei University; (4) Kwonyoung Kim, Yonsei University; (5) Kwanghoon Sohn, Yonsei University and Korea Institute of Science and Technology (KIST). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Work 2. Related Work 3. Hyperbolic Geometry 3. Hyperbolic Geometry 4. Method 4.1. Overview 4.1. Overview 4.2. Probabilistic hierarchy tree 4.2. Probabilistic hierarchy tree 4.3. Visual hierarchy decomposition 4.3. Visual hierarchy decomposition 4.4. Learning hierarchy in hyperbolic space 4.4. Learning hierarchy in hyperbolic space 4.5. Visual hierarchy encoding 4.5. Visual hierarchy encoding 5. Experiments and 5.1. Image classification 5. Experiments and 5.1. Image classification 5.2. Object detection and Instance segmentation 5.2. Object detection and Instance segmentation 5.3. Semantic segmentation 5.3. Semantic segmentation 5.4. Visualization 5.4. Visualization 6. Ablation studies and discussion 6. Ablation studies and discussion 7. Conclusion and References 7. Conclusion and References A. Network Architecture A. Network Architecture B. Theoretical Baseline B. Theoretical Baseline C. Additional Results C. Additional Results D. Additional visualization D. Additional visualization 6. Ablation studies and discussion To further analyze and validate the components of our method, we conduct ablation studies on image classification. Effectiveness of hyperbolic manifolds. We first investigate the effectiveness of hyperbolic manifolds in our approach. As shown in Tab. 4, we report the impact according to image classification. In Euclidean space, the distance function between two vectors is the cosine similarity function. The results demonstrate that applying hierarchical contrastive loss in Euclidean space degrades performance. It indicates that hyperbolic space is more suitable for stabilizing hierarchical structures. Additionally, the application of a KL loss term shows further benefits derived from the semantic seed distribution. Effectiveness of hyperbolic manifolds. Impact of probabilistic modeling. In Tab. 5, we report the performance comparisons between the probabilistic hierarchy tree and the deterministic hierarchy tree. For constructing a hierarchy tree, probabilistic modeling defines every node via MoG of its child node distributions, while the deterministic approach determines each node the mean of its child nodes. The probabilistic hierarchy tree achieves significant improvement in performance compared to the deterministic approach. This result shows that probabilistic modeling is more effective in representing hierarchical structure than deterministic modeling, leading to improvement in recognition. Impact of probabilistic modeling. Hierarchy width and depth. As shown in Fig. 5, we analyze the effect of the width N and depth L of the hierarchy tree on ImageNet-1K [36] with Hi-Mapper(DeiTS). These factors control the granularity of visual elements to be decomposed. While a small number of N degrades the fine-grained recognition capacity, an excess N hinders the optimization. Meanwhile, a large L may provide diverse granularity, however, it leads to entangled object-level representations. In all the cases, we report the best performance of 82.6% at N=32, L = 4. Hierarchy width and depth. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

What AI ‘Sees’ and Why It Matters

Authors:
(1) Hyeongjun Kwon, Yonsei University;
(2) Jinhyun Jang, Yonsei University;
(3) Jin Kim, Yonsei University;
(4) Kwonyoung Kim, Yonsei University;
(5) Kwanghoon Sohn, Yonsei University and Korea Institute of Science and Technology (KIST). Table of Links Abstract and 1 Introduction 2. Related Work 3. Hyperbolic Geometry 4. Method 4.1. Overview 4.2. Probabilistic hierarchy tree 4.3. Visual hierarchy decomposition 4.4. Learning hierarchy in hyperbolic space 4.5. Visual hierarchy encoding 5. Experiments and 5.1. Image classification 5.2. Object detection and Instance segmentation 5.3. Semantic segmentation 5.4. Visualization 6. Ablation studies and discussion 7. Conclusion and References A. Network Architecture B. Theoretical Baseline C. Additional Results D. Additional visualization 5. Experiments In this section, we conduct extensive experiments to show the effectiveness of our proposed Hi-Mapper on image classification (Sec. 5.1), object detection and instance segmentation (Sec. 5.2), and semantic segmentation (Sec. 5.3). We apply our Hi-Mapper on both CNNbased [34, 59] and ViT-based [35, 60] backbone networks and compare the performance with the concurrent hierarchyaware baselines [16, 19]. Lastly, we provide ablation studies (Sec. 6) to demonstrate the effectiveness of our contributions. 5.1. Image classification Settings. We apply our Hi-Mapper on the state-of-theart backbone networks [34, 35, 59, 60] and benchmark on ImageNet-1k [36] dataset. Following [12, 60, 62–64], we use the identical combinations for data augmentation and regularization strategies [35] after excluding repeated augmentation [65]. We train our model with batch size 1024 for 50 epochs using AdamW [66] with weight decay 1e-4. The initial learning rate is set to 1e-4 and a cosine learning rate scheduler is applied following [35]. Results. Tab. 1 presents classification performance. We report the original performance without the fine-tuning schemes for the plain backbone networks since we observed degradation in performance after fine-tuning the models. Our proposed method consistently achieves better performances than the baseline models with only a slight increase in parameters. Specifically, Hi-Mapper surpasses ResNet50 [34] and EfficientNet-B4 [59] by margins of 2.0% and 1.2%, respectively. Additionally, it achieves improvement on DeiT’s performance by 2.6%/2.8%/1.6%, and Swin [60] by 2.2%/1.1% across various model sizes. The experiments on image classification demonstrate not only the importance of understanding the structured organization of visual scenes, but also the scalability of our method. 5.2. Object detection and Instance segmentation Settings. We experiment on the COCO [37] dataset. We use the Hi-Mapper backbones derived from Sec. 5.1. Then, we deploy our pre-trained backbone into Mask R-CNN [67]. All models are trained on COCO 2017, including 118k train images and 5k validation images. We follow the standard learning protocols [67], 1\times schedule with 12 epochs. Note that we reproduce DeiT-T with Mask R-CNN based on [68]. Results. In Table 2, we present a comparison of our results with baseline models, such as DeiT and Swin, on the COCO dataset, which demands a higher capacity for fine representation recognition. Our Hi-Mapper consistently boosts all baseline models capability with only a small increase in parameters. Notably, Hi-Mapper significantly improves DeiT-T and -S by margins of 6.8 and 8.3 on the object detection task. Meanwhile, in the instance segmentation, it also improves DeiT-T and -S by margins of 8.6 and 7.2, respectively. This result shows that the visual hierarchy facilitates complex visual scene recognition. 5.3. Semantic segmentation Settings. We further experiment on the ADE20K [38] dataset for semantic segmentation. ADE20K contains Results. We present the performance comparisons on ADE20K in Tab. 3. The results show that our HiMapper achieves comparable or better performance than the baseline models, including DeiT-T, -S, and Swin-T, requiring a smaller increase in the number of parameters and GFLOPs. Specifically, Hi-Mapper on DeiT-T, -S, and Swin-T achieves a performance improvement of 2.0%, 3.3%, and 2.3%. Additionally, as model sizes increase, Hi-Mapper fully capitalizes fine-grained representations for semantic segmentation with a slight increase in computation. 5.4. Visualization As shown in Fig. 4, we demonstrate our visual hierarchy on images from ImageNet-1K [36]. This confirms that our approach can successfully uncover the inherent hierarchy among visual components without the need for hierarchy or part-level annotations. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Hyeongjun Kwon, Yonsei University; (2) Jinhyun Jang, Yonsei University; (3) Jin Kim, Yonsei University; (4) Kwonyoung Kim, Yonsei University; (5) Kwanghoon Sohn, Yonsei University and Korea Institute of Science and Technology (KIST). Authors: Authors: (1) Hyeongjun Kwon, Yonsei University; (2) Jinhyun Jang, Yonsei University; (3) Jin Kim, Yonsei University; (4) Kwonyoung Kim, Yonsei University; (5) Kwanghoon Sohn, Yonsei University and Korea Institute of Science and Technology (KIST). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Work 2. Related Work 3. Hyperbolic Geometry 3. Hyperbolic Geometry 4. Method 4.1. Overview 4.1. Overview 4.2. Probabilistic hierarchy tree 4.2. Probabilistic hierarchy tree 4.3. Visual hierarchy decomposition 4.3. Visual hierarchy decomposition 4.4. Learning hierarchy in hyperbolic space 4.4. Learning hierarchy in hyperbolic space 4.5. Visual hierarchy encoding 4.5. Visual hierarchy encoding 5. Experiments and 5.1. Image classification 5. Experiments and 5.1. Image classification 5.2. Object detection and Instance segmentation 5.2. Object detection and Instance segmentation 5.3. Semantic segmentation 5.3. Semantic segmentation 5.4. Visualization 5.4. Visualization 6. Ablation studies and discussion 6. Ablation studies and discussion 7. Conclusion and References 7. Conclusion and References A. Network Architecture A. Network Architecture B. Theoretical Baseline B. Theoretical Baseline C. Additional Results C. Additional Results D. Additional visualization D. Additional visualization 5. Experiments In this section, we conduct extensive experiments to show the effectiveness of our proposed Hi-Mapper on image classification (Sec. 5.1), object detection and instance segmentation (Sec. 5.2), and semantic segmentation (Sec. 5.3). We apply our Hi-Mapper on both CNNbased [34, 59] and ViT-based [35, 60] backbone networks and compare the performance with the concurrent hierarchyaware baselines [16, 19]. Lastly, we provide ablation studies (Sec. 6) to demonstrate the effectiveness of our contributions. 5.1. Image classification Settings . We apply our Hi-Mapper on the state-of-theart backbone networks [34, 35, 59, 60] and benchmark on ImageNet-1k [36] dataset. Following [12, 60, 62–64], we use the identical combinations for data augmentation Settings and regularization strategies [35] after excluding repeated augmentation [65]. We train our model with batch size 1024 for 50 epochs using AdamW [66] with weight decay 1e-4. The initial learning rate is set to 1e-4 and a cosine learning rate scheduler is applied following [35]. Results . Tab. 1 presents classification performance. We report the original performance without the fine-tuning schemes for the plain backbone networks since we observed degradation in performance after fine-tuning the models. Our proposed method consistently achieves better performances than the baseline models with only a slight increase in parameters. Specifically, Hi-Mapper surpasses ResNet50 [34] and EfficientNet-B4 [59] by margins of 2.0% and 1.2%, respectively. Additionally, it achieves improvement on DeiT’s performance by 2.6%/2.8%/1.6%, and Swin [60] by 2.2%/1.1% across various model sizes. The experiments on image classification demonstrate not only the importance of understanding the structured organization of visual scenes, but also the scalability of our method. Results 5.2. Object detection and Instance segmentation Settings . We experiment on the COCO [37] dataset. We use the Hi-Mapper backbones derived from Sec. 5.1. Then, Settings we deploy our pre-trained backbone into Mask R-CNN [67]. All models are trained on COCO 2017, including 118k train images and 5k validation images. We follow the standard learning protocols [67], 1\times schedule with 12 epochs. Note that we reproduce DeiT-T with Mask R-CNN based on [68]. Results . In Table 2, we present a comparison of our results with baseline models, such as DeiT and Swin, on the COCO dataset, which demands a higher capacity for fine representation recognition. Our Hi-Mapper consistently boosts all baseline models capability with only a small increase in parameters. Notably, Hi-Mapper significantly improves DeiT-T and -S by margins of 6.8 and 8.3 on the object detection task. Meanwhile, in the instance segmentation, it also improves DeiT-T and -S by margins of 8.6 and 7.2, respectively. This result shows that the visual hierarchy facilitates complex visual scene recognition. Results 5.3. Semantic segmentation Settings . We further experiment on the ADE20K [38] dataset for semantic segmentation. ADE20K contains Settings Results . We present the performance comparisons on ADE20K in Tab. 3. The results show that our HiMapper achieves comparable or better performance than the baseline models, including DeiT-T, -S, and Swin-T, requiring a smaller increase in the number of parameters and GFLOPs. Specifically, Hi-Mapper on DeiT-T, -S, and Swin-T achieves a performance improvement of 2.0%, 3.3%, and 2.3%. Additionally, as model sizes increase, Hi-Mapper fully capitalizes fine-grained representations for semantic segmentation with a slight increase in computation. Results 5.4. Visualization As shown in Fig. 4, we demonstrate our visual hierarchy on images from ImageNet-1K [36]. This confirms that our approach can successfully uncover the inherent hierarchy among visual components without the need for hierarchy or part-level annotations. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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