Human-Object Interaction Detection:A Quick Survey and Examination of Methods

Human-object interaction detection is a relatively new task in the world of computer vision and visual semantic information extraction. With the goal of machines identifying interactions that humans perform on objects, there are many real-world use cases for the research in this field. To our knowledge, this is the first general survey of the state-of-the-art and milestone works in this field. We provide a basic survey of the developments in the field of human-object interaction detection. Many works in this field use multi-stream convolutional neural network architectures, which combine features from multiple sources in the input image. Most commonly these are the humans and objects in question, as well as the spatial quality of the two. As far as we are aware, there have not been in-depth studies performed that look into the performance of each component individually. In order to provide insight to future researchers, we perform an individualized study that examines the performance of each component of a multi-stream convolutional neural network architecture for human-object interaction detection. Specifically, we examine the HORCNN architecture as it is a foundational work in the field. In addition, we provide an in-depth look at the HICO-DET dataset, a popular benchmark in the field of human-object interaction detection. Code and papers can be found at https://github.com/SHI-Labs/Human-Object-Interaction-Detection.Comment: Published at The 1st International Workshop On Human-Centric Multimedia Analysis, at ACM Multimedia Conference 202

Closing the Gap Between the Upper Bound and the Lower Bound of Adam's Iteration Complexity

Recently, Arjevani et al. [1] established a lower bound of iteration complexity for the first-order optimization under an $L$-smooth condition and a bounded noise variance assumption. However, a thorough review of existing literature on Adam's convergence reveals a noticeable gap: none of them meet the above lower bound. In this paper, we close the gap by deriving a new convergence guarantee of Adam, with only an $L$-smooth condition and a bounded noise variance assumption. Our results remain valid across a broad spectrum of hyperparameters. Especially with properly chosen hyperparameters, we derive an upper bound of the iteration complexity of Adam and show that it meets the lower bound for first-order optimizers. To the best of our knowledge, this is the first to establish such a tight upper bound for Adam's convergence. Our proof utilizes novel techniques to handle the entanglement between momentum and adaptive learning rate and to convert the first-order term in the Descent Lemma to the gradient norm, which may be of independent interest.Comment: NeurIPS 2023 Accep

Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar Datasets

Active learning improves the performance of machine learning methods by judiciously selecting a limited number of unlabeled data points to query for labels, with the aim of maximally improving the underlying classifier's performance. Recent gains have been made using sequential active learning for synthetic aperture radar (SAR) data arXiv:2204.00005. In each iteration, sequential active learning selects a query set of size one while batch active learning selects a query set of multiple datapoints. While batch active learning methods exhibit greater efficiency, the challenge lies in maintaining model accuracy relative to sequential active learning methods. We developed a novel, two-part approach for batch active learning: Dijkstra's Annulus Core-Set (DAC) for core-set generation and LocalMax for batch sampling. The batch active learning process that combines DAC and LocalMax achieves nearly identical accuracy as sequential active learning but is more efficient, proportional to the batch size. As an application, a pipeline is built based on transfer learning feature embedding, graph learning, DAC, and LocalMax to classify the FUSAR-Ship and OpenSARShip datasets. Our pipeline outperforms the state-of-the-art CNN-based methods.Comment: 16 pages, 7 figures, Preprin

One at A Time: Multi-step Volumetric Probability Distribution Diffusion for Depth Estimation

Recent works have explored the fundamental role of depth estimation in multi-view stereo (MVS) and semantic scene completion (SSC). They generally construct 3D cost volumes to explore geometric correspondence in depth, and estimate such volumes in a single step relying directly on the ground truth approximation. However, such problem cannot be thoroughly handled in one step due to complex empirical distributions, especially in challenging regions like occlusions, reflections, etc. In this paper, we formulate the depth estimation task as a multi-step distribution approximation process, and introduce a new paradigm of modeling the Volumetric Probability Distribution progressively (step-by-step) following a Markov chain with Diffusion models (VPDD). Specifically, to constrain the multi-step generation of volume in VPDD, we construct a meta volume guidance and a confidence-aware contextual guidance as conditional geometry priors to facilitate the distribution approximation. For the sampling process, we further investigate an online filtering strategy to maintain consistency in volume representations for stable training. Experiments demonstrate that our plug-and-play VPDD outperforms the state-of-the-arts for tasks of MVS and SSC, and can also be easily extended to different baselines to get improvement. It is worth mentioning that we are the first camera-based work that surpasses LiDAR-based methods on the SemanticKITTI dataset

TensorIR: An Abstraction for Automatic Tensorized Program Optimization

Deploying deep learning models on various devices has become an important topic. The wave of hardware specialization brings a diverse set of acceleration primitives for multi-dimensional tensor computations. These new acceleration primitives, along with the emerging machine learning models, bring tremendous engineering challenges. In this paper, we present TensorIR, a compiler abstraction for optimizing programs with these tensor computation primitives. TensorIR generalizes the loop nest representation used in existing machine learning compilers to bring tensor computation as the first-class citizen. Finally, we build an end-to-end framework on top of our abstraction to automatically optimize deep learning models for given tensor computation primitives. Experimental results show that TensorIR compilation automatically uses the tensor computation primitives for given hardware backends and delivers performance that is competitive to state-of-art hand-optimized systems across platforms.Comment: Accepted to ASPLOS 202

Phylogenomics and morphological evolution of the mega-diverse genus Artemisia (Asteraceae: Anthemideae): implications for its circumscription and infrageneric taxonomy

Background and Aims Artemisia is a mega-diverse genus consisting of ~400 species. Despite its medicinal importance and ecological significance, a well-resolved phylogeny for global Artemisia, a natural generic delimitation and infrageneric taxonomy remain missing, owing to the obstructions from limited taxon sampling and insufficient information on DNA markers. Its morphological characters, such as capitulum, life form and leaf, show marked variations and are widely used in its infrageneric taxonomy. However, their evolution within Artemisia is poorly understood. Here, we aimed to reconstruct a well-resolved phylogeny for global Artemisia via a phylogenomic approach, to infer the evolutionary patterns of its key morphological characters and to update its circumscription and infrageneric taxonomy. Methods We sampled 228 species (258 samples) of Artemisia and its allies from both fresh and herbarium collections, covering all the subgenera and its main geographical areas, and conducted a phylogenomic analysis based on nuclear single nucleotide polymorphisms (SNPs) obtained from genome skimming data. Based on the phylogenetic framework, we inferred the possible evolutionary patterns of six key morphological characters widely used in its previous taxonomy. Key Results The genus Kaschgaria was revealed to be nested in Artemisia with strong support. A well-resolved phylogeny of Artemisia consisting of eight highly supported clades was recovered, two of which were identified for the first time. Most of the previously recognized subgenera were not supported as monophyletic. Evolutionary inferences based on the six morphological characters showed that different states of these characters originated independently more than once. Conclusions The circumscription of Artemisia is enlarged to include the genus Kaschgaria. The morphological characters traditionally used for the infrageneric taxonomy of Artemisia do not match the new phylogenetic tree. They experienced a more complex evolutionary history than previously thought. We propose a revised infrageneric taxonomy of the newly circumscribed Artemisia, with eight recognized subgenera to accommodate the new results.This work was supported by the National Natural Science Foundation of China (grant nos. 31870179, 31570204, 31270237 and J1310002), the International Partnership Program (grant no. 151853KYSB20190027), Sino-Africa Joint Research Center (grant no. SAJC201614), Key technology projects of Jiangxi Province's major scientific and technological research and development project (grant no. 20223AAF01007), Survey of Wildlife Resources in Key Areas of Tibet (grant no. ZL202203601) and National Plant Specimen Resource Center (grant no. E0117G1001) of the Chinese Academy of Sciences, Key Project at Central Government Level: The Ability Establishment of Sustainable Use of Valuable Chinese Medicine Resources (grant no. 2060302) and Project of the Central Siberian Botanical Garden of the Siberian Branch of the Russian Academy of Sciences (grant no. AAAA-A21-121011290024-5).Abstract INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION Conclusions SUPPLEMENTARY DATA FUNDING ACKNOWLEDGEMENTS CONFLICT OF INTEREST LITERATURE CITED Supplementary dat

Long-term nitrogen deposition linked to reduced water use efficiency in forests with low phosphorus availability

1. The impact of long-term nitrogen (N) deposition is under-studied in phosphorus (P)-limited subtropical forests. We exploited historically collected herbarium specimens to investigate potential physiological responses of trees in three subtropical forests representing an urban-to-rural gradient, across which N deposition has probably varied over the past six decades. We measured foliar [N] and [P] and stable carbon (δ¹³C), oxygen (δ¹⁸O) and nitrogen (δ¹⁵N) isotopic compositions in tissue from herbarium specimens of plant species collected from 1947 to 2014. - 2. Foliar [N] and N : P increased, and (δ¹⁵N and [P] decreased in the two forests close to urban centers. Consistent with recent studies demonstrating that N deposition in the region is 15N-depleted, these data suggest that the increased foliar [N] and N : P, and decreased [P], may be attributable to atmospheric deposition and associated enhancement of P limitation. - 3. Estimates of intrinsic water use efficiency calculated from foliar (δ¹³C decreased by c. 30% from the 1950s to 2014, contrasting with multiple studies investigating similar parameters in N-limited forests. This effect may reflect decreased photosynthesis, as suggested by a conceptual model of foliar (δ¹³C and δ¹⁸O. - 4.Long-term N deposition may exacerbate P limitation and mitigate projected increases in carbon stocks driven by elevated CO₂ in forests on P-limited soils

Linkage Mapping of Stem Saccharification Digestibility in Rice

Rice is the staple food of almost half of the world population, and in excess 90% of it is grown and consumed in Asia, but the disposal of rice straw poses a problem for farmers, who often burn it in the fields, causing health and environmental problems. However, with increased focus on the development of sustainable biofuel production, rice straw has been recognized as a potential feedstock for non-food derived biofuel production. Currently, the commercial realization of rice as a biofuel feedstock is constrained by the high cost of industrial saccharification processes needed to release sugar for fermentation. This study is focused on the alteration of lignin content, and cell wall chemotypes and structures, and their effects on the saccharification potential of rice lignocellulosic biomass. A recombinant inbred lines (RILs) population derived from a cross between the lowland rice variety IR1552 and the upland rice variety Azucena with 271 molecular markers for quantitative trait SNP (QTS) analyses was used. After association analysis of 271 markers for saccharification potential, 1 locus and 4 pairs of epistatic loci were found to contribute to the enzymatic digestibility phenotype, and an inverse relationship between reducing sugar and lignin content in these recombinant inbred lines was identified. As a result of QTS analyses, several cell-wall associated candidate genes are proposed that may be useful for marker-assisted breeding and may aid breeders to produce potential high saccharification rice varieties

Forecasting of Industrial Water Demand Using Case-Based Reasoning—A Case Study in Zhangye City, China

Forecasting the industrial water demand accurately is crucial for sustainable water resource management. This study investigates industrial water demand forecasting by case-based reasoning (CBR) in an arid area, with a case study of Zhangye, China. We constructed a case base with 420 original cases of 28 cities in China, extracted six attributes of the industrial water demand, and employed a back propagation neural network (BPN) to weight each attribute, as well as the grey incidence analysis (GIA) to calculate the similarities between target case and original cases. The forecasting values were calculated by weighted similarities. The results show that the industrial water demand of Zhangye in 2030, which is the t arget case, will reach 11.9 million tons. There are 10 original cases which have relatively high similarities to the target case. Furthermore, the case of Yinchuan, 2010, has the largest similarity, followed by Yinchuan, 2009, and Urumqi, 2009. We also made a comparison experiment in which case-based reasoning is more accurate than the grey forecast model and BPN in water demand forecasting. It is expected that the results of this study will provide references to water resources management and planning