Iterative Robust Visual Grounding with Masked Reference based Centerpoint Supervision

Visual Grounding (VG) aims at localizing target objects from an image based on given expressions and has made significant progress with the development of detection and vision transformer. However, existing VG methods tend to generate false-alarm objects when presented with inaccurate or irrelevant descriptions, which commonly occur in practical applications. Moreover, existing methods fail to capture fine-grained features, accurate localization, and sufficient context comprehension from the whole image and textual descriptions. To address both issues, we propose an Iterative Robust Visual Grounding (IR-VG) framework with Masked Reference based Centerpoint Supervision (MRCS). The framework introduces iterative multi-level vision-language fusion (IMVF) for better alignment. We use MRCS to ahieve more accurate localization with point-wised feature supervision. Then, to improve the robustness of VG, we also present a multi-stage false-alarm sensitive decoder (MFSD) to prevent the generation of false-alarm objects when presented with inaccurate expressions. The proposed framework is evaluated on five regular VG datasets and two newly constructed robust VG datasets. Extensive experiments demonstrate that IR-VG achieves new state-of-the-art (SOTA) results, with improvements of 25\% and 10\% compared to existing SOTA approaches on the two newly proposed robust VG datasets. Moreover, the proposed framework is also verified effective on five regular VG datasets. Codes and models will be publicly at https://github.com/cv516Buaa/IR-VG

Rethinking Mobile Block for Efficient Attention-based Models

This paper focuses on developing modern, efficient, lightweight models for dense predictions while trading off parameters, FLOPs, and performance. Inverted Residual Block (IRB) serves as the infrastructure for lightweight CNNs, but no counterpart has been recognized by attention-based studies. This work rethinks lightweight infrastructure from efficient IRB and effective components of Transformer from a unified perspective, extending CNN-based IRB to attention-based models and abstracting a one-residual Meta Mobile Block (MMB) for lightweight model design. Following simple but effective design criterion, we deduce a modern Inverted Residual Mobile Block (iRMB) and build a ResNet-like Efficient MOdel (EMO) with only iRMB for down-stream tasks. Extensive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, e.g., EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass equal-order CNN-/Attention-based models, while trading-off the parameter, efficiency, and accuracy well: running 2.8-4.0x faster than EdgeNeXt on iPhone14

Iterative Robust Visual Grounding with Masked Reference based Centerpoint Supervision

Visual Grounding (VG) aims at localizing target objects from an image based on given expressions and has made significant progress with the development of detection and vision transformer. However, existing VG methods tend to generate false-alarm objects when presented with inaccurate or irrelevant descriptions, which commonly occur in practical applications. Moreover, existing methods fail to capture fine-grained features, accurate localization, and sufficient context comprehension from the whole image and textual descriptions. To address both issues, we propose an Iterative Robust Visual Grounding (IRVG) framework with Masked Reference based Centerpoint Supervision (MRCS). The framework introduces iterative multi-level vision-language fusion (IMVF) for better alignment. We use MRCS to ahieve more accurate localization with point-wised feature supervision. Then, to improve the robustness of VG, we also present a multi-stage false-alarm sensitive decoder (MFSD) to prevent the generation of false-alarm objects when presented with inaccurate expressions. Extensive experiments demonstrate that IR-VG achieves new state-of-the-art (SOTA) results, with improvements of 25% and 10% compared to existing SOTA approaches on the two newly proposed robust VG datasets. Moreover, the proposed framework is also verified effective on five regular VG datasets. Codes and models will be publicly at https://github.com/cv516Buaa/IR-VG

Joint Generation Network Coding in Unreliable Wireless Networks

AbstractThis paper investigates the performance ofnetwork coding (NC) in unreliable wireless networks andthe integration with TCP protocol. As the wireless nodeshave limited processing capacity and energy, it will bedifficult for them to deal with complex problems. Codingand decoding with traditional NC will cause a largeoverhead for wireless nodes. It is necessary to improve theNC scheme for applying to wireless networks.Furthermore, unreliable wireless channels will result in alot of unnecessary retransmissions in wireless networks. Inthis paper, we propose a joint generation network codingscheme to improve the performance of NC in wirelessnetworks. We first analyze the impact of the probability ofdecoding under lossy wireless channels in the traditionalNC and joint generation NC. Then, we design a schemethat integrates the proposed network coding scheme withTCP. By adopting the joint generation NC, we could avoidunnecessary retransmissions in wireless networks due tothe loss of acknowledgment in TCP protocol. Oursimulation results demonstrate that joint generation NCcould greatly reduce retransmissions5 Halama

Evaluation of the Antifungal Activity of Polysubstituted Cyclic 1,2-Diketones against <i>Colletotrichum gloeosporioides</i>

(1) Anthracnose caused by Colletotrichum damages crops, ornamentals, and forest trees severely, resulting in enormous economic losses to agricultural and forestry systems. Overusing traditional chemical fungicides leads to fungicide resistance, environmental pollution, and potential risks to public health. Therefore, priorities should be given to developing efficient and environmentally friendly approaches to phytopathogens management, including anthracnose. (2) In this study, the antifungal activity of botanical derivative polysubstituted cyclic 1,2-diketones (FPL001) against C. gloeosporioides was examined. (3) FPL001 significantly inhibited the vegetative growth of C. gloeosporioides with an EC50 of 160.23 µg/mL. When the concentration of FPL001 reached 30 µg/mL, the conidial germination and appressorium formation of C. gloeosporioides were completely inhibited. FPL001 also significantly suppressed the invasive hyphae development and plant infection of C. gloeosporioides. FPL001 did not exhibit toxicity to model organisms such as alfalfa and silkworm larvae. (4) These results indicate that compound FPL001 is a potential and efficient agent for green control of C. gloeosporioides

Data from: Phenotypic effects of the nurse Thylacospermum caespitosum on dependent plant species along regional climate stress gradients

Contrasting phenotypes of alpine cushion species have been recurrently described in several mountain ranges along small-scale topography gradients, with tight competitive phenotypes in stressful convex topography and loose facilitative phenotypes in sheltered concave topography. The consistency of phenotypic effects along large-scale climate stress gradients have been proposed as a test of the likely genetic bases of the differences observed at small-scale. Inversely, plastic phenotypic effects are more likely to vanish at some points along climate stress gradients. We tested this hypothesis for two phenotypes of the alpine cushion species Thylacospermum caespitosum at four points along regional gradients of cold and drought stress in northwest China. We measured the traits of the two cushion phenotypes and quantified their associated plant communities and environmental variables along the regional temperature and aridity gradients. Cushion height, convexity and stem density overall showed significant effect of phenotypes. Difference in tightness of cushions between phenotypes was consistent across climate conditions, whereas differences in cushion convexity and height between phenotypes increased with increasing cold stress. Phenotypic effects on species richness and abundance were consistent along both climate gradients but not effects on species composition, while there were no phenotypic effects on environmental variables. Additionally, RII (relative interaction index) curves were linear along the drought gradient but unimodal along the temperature gradient, likely due to the occurrence of contrasting species pools at the different sites. We conclude that the consistency of phenotypic effects of T. caespitosum was high for species richness and abundance and mainly explained by differences in interference mediated by likely heritable differences in cushion tightness. Additionally, our study shows that the shapes of the relationship between plant responses to neighbours and environmental stresses are not necessarily driven by niche-based deterministic factors

Direct and indirect facilitation affect community productivity through changes in functional diversity in an alpine system

International audienceAbstract Background and Aims Facilitation is an important ecological process for plant community structure and functional composition. Although direct facilitation has accrued most of the evidence so far, indirect facilitation is ubiquitous in nature and it has an enormous potential to explain community structuring. In this study, we assess the effect of direct and indirect facilitation on community productivity via taxonomic and functional diversity. Methods In an alpine community on the Tibetan Plateau, we manipulated the presence of the shrub Dasiphora fruticosa and graminoids in a fenced meadow and a grazed meadow to quantify the effects of direct and indirect facilitation. We measured four plant traits: height, lateral spread, specific leaf area (SLA) and leaf dry matter content (LDMC) of forbs; calculated two metrics of functional diversity [range of trait and community-weighted mean (CWM) of trait]; and assessed the responses of functional diversity to shrub facilitation. We used structural equation modelling to explore how shrubs directly and indirectly drove community productivity via taxonomic diversity and functional diversity. Key Results We found stronger effects from herbivore-mediated indirect facilitation than direct facilitation on productivity and taxonomic diversity, regardless of the presence of graminoids. For functional diversity, the range and CWM of height and SLA, rather than lateral spread and LDMC, generally increased due to direct and indirect facilitation. Moreover, we found that the range of traits played a primary role over taxonomic diversity and CWM of traits in terms of shrub effects on community productivity. Conclusions Our study reveals that the mechanism of shrub direct and indirect facilitation of community productivity in this alpine community is expanding the realized niche (i.e. expanding range of traits). Our findings indicate that facilitators might increase trait dispersion in the local community, which could alleviate the effect of environmental filters on trait values in harsh environments, thereby contributing to ecosystem functioning