PPDM: Parallel Point Detection and Matching for Real-time Human-Object Interaction Detection

Liao, Yue; Liu, Si; Wang, Fei; Chen, Yanjie; Qian, Chen; Feng, Jiashi

doi:10.48550/arxiv.1912.12898

Cited by 4 publications

(1 citation statement)

References 34 publications

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“…By dint of automatic feature engineering, deep neural networks (DNNs) have achieved remarkable success in various computer vision tasks, such as image classification [37,36,41,33,32,15,39], visual generation [34,35], image retrieval [40,7,8,12] and semantic comprehension [18,17]. In contrast, neural architecture search (NAS) aims at automatically learning the network architecture to further boost the performance for target tasks [10,20,43,2,19].…”

Section: Introductionmentioning

confidence: 99%

GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet

You

Huang

Yang

et al. 2020

Preprint

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Training a supernet matters for one-shot neural architecture search (NAS) methods since it serves as a basic performance estimator for different architectures (paths). Current methods mainly hold the assumption that a supernet should give a reasonable ranking over all paths. They thus treat all paths equally, and spare much effort to train paths. However, it is harsh for a single supernet to evaluate accurately on such a huge-scale search space (e.g., 7 21 ). In this paper, instead of covering all paths, we ease the burden of supernet by encouraging it to focus more on evaluation of those potentially-good ones, which are identified using a surrogate portion of validation data. Concretely, during training, we propose a multi-path sampling strategy with rejection, and greedily filter the weak paths. The training efficiency is thus boosted since the training space has been greedily shrunk from all paths to those potentiallygood ones. Moreover, we further adopt an exploration and exploitation policy by introducing an empirical candidate path pool. Our proposed method GreedyNAS is easy-tofollow, and experimental results on ImageNet dataset indicate that it can achieve better Top-1 accuracy under same search space and FLOPs or latency level, but with only ∼60% of supernet training cost. By searching on a larger space, our GreedyNAS can also obtain new state-of-the-art architectures.

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Section: Introductionmentioning

confidence: 99%

GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet

You

Huang

Yang

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Referring Image Segmentation via Cross-Modal Progressive Comprehension

Huang

Hui

Liu

et al. 2020

Preprint

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Referring image segmentation aims at segmenting the foreground masks of the entities that can well match the description given in the natural language expression. Previous approaches tackle this problem using implicit feature interaction and fusion between visual and linguistic modalities, but usually fail to explore informative words of the expression to well align features from the two modalities for accurately identifying the referred entity. In this paper, we propose a Cross-Modal Progressive Comprehension (CMPC) module and a Text-Guided Feature Exchange (TGFE) module to effectively address the challenging task. Concretely, the CMPC module first employs entity and attribute words to perceive all the related entities that might be considered by the expression. Then, the relational words are adopted to highlight the correct entity as well as suppress other irrelevant ones by multimodal graph reasoning. In addition to the CMPC module, we further leverage a simple yet effective TGFE module to integrate the reasoned multimodal features from different levels with the guidance of textual information. In this way, features from multi-levels could communicate with each other and be refined based on the textual context. We conduct extensive experiments on four popular referring segmentation benchmarks and achieve new state-of-the-art performances. Code is available at https://github.com/ spyflying/CMPC-Refseg.

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Prioritized Architecture Sampling with Monto-Carlo Tree Search

Su¹,

Huang²,

Li³

et al. 2021

Preprint

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One-shot neural architecture search (NAS) methods significantly reduce the search cost by considering the whole search space as one network, which only needs to be trained once. However, current methods select each operation independently without considering previous layers. Besides, the historical information obtained with huge computation cost is usually used only once and then discarded. In this paper, we introduce a sampling strategy based on Monte Carlo tree search (MCTS) with the search space modeled as a Monte Carlo tree (MCT), which captures the dependency among layers. Furthermore, intermediate results are stored in the MCT for future decisions and a better explorationexploitation balance. Concretely, MCT is updated using the training loss as a reward to the architecture performance; for accurately evaluating the numerous nodes, we propose node communication and hierarchical node selection methods in the training and search stages, respectively, which make better uses of the operation rewards and hierarchical information. Moreover, for a fair comparison of different NAS methods, we construct an open-source NAS benchmark of a macro search space evaluated on CIFAR-10, namely NAS-Bench-Macro. Extensive experiments on NAS-Bench-Macro and ImageNet demonstrate that our method significantly improves search efficiency and performance. For example, by only searching 20 architectures, our obtained architecture achieves 78.0% top-1 accuracy with 442M FLOPs on ImageNet. Code (Benchmark) is available at: https://github.com/xiusu/NAS-Bench-Macro.

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PPDM: Parallel Point Detection and Matching for Real-time Human-Object Interaction Detection

Cited by 4 publications

References 34 publications

GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet

GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernet

Referring Image Segmentation via Cross-Modal Progressive Comprehension

Prioritized Architecture Sampling with Monto-Carlo Tree Search

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