D3Net: A Unified Speaker-Listener Architecture for 3D Dense Captioning and Visual Grounding

Recent studies on dense captioning and visual grounding in 3D have achieved impressive results. Despite developments in both areas, the limited amount of available 3D vision-language data causes overfitting issues for 3D visual grounding and 3D dense captioning methods. Also, how to discriminatively describe objects in complex 3D environments is not fully studied yet. To address these challenges, we present D3Net, an end-to-end neural speaker-listener architecture that can detect, describe and discriminate. Our D3Net unifies dense captioning and visual grounding in 3D in a self-critical manner. This self-critical property of D3Net also introduces discriminability during object caption generation and enables semi-supervised training on ScanNet data with partially annotated descriptions. Our method outperforms SOTA methods in both tasks on the ScanRefer dataset, surpassing the SOTA 3D dense captioning method by a significant margin.Comment: Project website: https://daveredrum.github.io/D3Net

Dual Modality Prompt Tuning for Vision-Language Pre-Trained Model

With the emergence of large pre-trained vison-language model like CLIP, transferable representations can be adapted to a wide range of downstream tasks via prompt tuning. Prompt tuning tries to probe the beneficial information for downstream tasks from the general knowledge stored in the pre-trained model. A recently proposed method named Context Optimization (CoOp) introduces a set of learnable vectors as text prompt from the language side. However, tuning the text prompt alone can only adjust the synthesized "classifier", while the computed visual features of the image encoder can not be affected , thus leading to sub-optimal solutions. In this paper, we propose a novel Dual-modality Prompt Tuning (DPT) paradigm through learning text and visual prompts simultaneously. To make the final image feature concentrate more on the target visual concept, a Class-Aware Visual Prompt Tuning (CAVPT) scheme is further proposed in our DPT, where the class-aware visual prompt is generated dynamically by performing the cross attention between text prompts features and image patch token embeddings to encode both the downstream task-related information and visual instance information. Extensive experimental results on 11 datasets demonstrate the effectiveness and generalization ability of the proposed method. Our code is available in https://github.com/fanrena/DPT.Comment: 12 pages, 7 figure

Taxonomic and phylogenetic characterisations of six species of Pleosporales (in Didymosphaeriaceae, Roussoellaceae and Nigrogranaceae) from China

Pleosporales comprise a diverse group of fungi with a global distribution and significant ecological importance. A survey on Pleosporales (in Didymosphaeriaceae, Roussoellaceae and Nigrogranaceae) in Guizhou Province, China, was conducted. Specimens were identified, based on morphological characteristics and phylogenetic analyses using a dataset composed of ITS, LSU, SSU, tef1 and rpb2 loci. Maximum Likelihood (ML) and Bayesian analyses were performed. As a result, three new species (Neokalmusia karka, Nigrograna schinifolium and N. trachycarpus) have been discovered, along with two new records for China (Roussoella neopustulans and R. doimaesalongensis) and a known species (Roussoella pseudohysterioides). Morphologically similar species and phylogenetically close taxa are compared and discussed. This study provides detailed information and descriptions of all newly-identified taxa

Corrigendum: Hu H et al. (2023) Taxonomic and phylogenetic characterisations of six species of Pleosporales (in Didymosphaeriaceae, Roussoellaceae and Nigrogranaceae) from China. MycoKeys 100: 123–151. https://doi.org/10.3897/mycokeys.100.109423

Four new species, Xynobius azonius sp. nov., X. brevifemora sp. nov., X. duoferus sp. nov., and X. stipitoides sp. nov., are described and illustrated, and one species X. geniculatus (Thomson, 1895) is newly reported from South Korea. Xynobius geniculatus (Thomson, 1895) is redescribed and illustrated, and a new combination, Xynobius (Stigmatopoea) cubitalis (Fischer, 1959), comb. nov. is suggested. An identification key to the Xynobius species known from South Korea is provided

A Novel Sustainable Processing Mode for Burr Classified Prediction of Weak Rigid Drilling Process Using a Fusion Modeling Method

Weakly rigid drilling systems, such as the industrial robot, are widely used in aerospace, military, and other fields due to its good flexibility and large scope of operation. However, the weak rigidity can easily cause burrs, seriously affecting the precision of parts and product performance. To reduce the heavy deburring process and to improve continuous production and sustainable processing capacity, accurate prediction of burr quality is a prerequisite. Traditional burr forming theory cannot accurately predict the drilling defects. Data-driven approaches can be independent of prior knowledge and discover relationships between process parameters and machining precision directly from the data structure itself. Therefore, to take advantage of both approaches, a fusion model was established for burr classified prediction. On the one hand, the drilling and burr forming process was firstly modeled, and preliminary classification results for burrs were calculated. On the other hand, according to the measured data, the errors between initial calculation results and actual classification results were obtained and selected as the tag values of dataset, which served as inputs for the error compensation model of burrs. Finally, by training the network of TCN–DNN using the drilling data, the burr classified prediction in a weak rigid hole-making system was realized. Experimental results showed that compared with traditional drilling theory, the prediction accuracy of the proposed model improved by 25%, reaching 91.67%. The results can provide a basis for judging the process of burr post-treatment, which has practical guiding significance. This method is beneficial to reduce the heavy deburring process and to improve sustainable processing capacity