Cascade-DETR: Delving into High-Quality Universal Object Detection

Object localization in general environments is a fundamental part of vision systems. While dominating on the COCO benchmark, recent Transformer-based detection methods are not competitive in diverse domains. Moreover, these methods still struggle to very accurately estimate the object bounding boxes in complex environments. We introduce Cascade-DETR for high-quality universal object detection. We jointly tackle the generalization to diverse domains and localization accuracy by proposing the Cascade Attention layer, which explicitly integrates object-centric information into the detection decoder by limiting the attention to the previous box prediction. To further enhance accuracy, we also revisit the scoring of queries. Instead of relying on classification scores, we predict the expected IoU of the query, leading to substantially more well-calibrated confidences. Lastly, we introduce a universal object detection benchmark, UDB10, that contains 10 datasets from diverse domains. While also advancing the state-of-the-art on COCO, Cascade-DETR substantially improves DETR-based detectors on all datasets in UDB10, even by over 10 mAP in some cases. The improvements under stringent quality requirements are even more pronounced. Our code and models will be released at https://github.com/SysCV/cascade-detr.Comment: Accepted in ICCV 2023. Our code and models will be released at https://github.com/SysCV/cascade-det

Segment Anything in High Quality

The recent Segment Anything Model (SAM) represents a big leap in scaling up segmentation models, allowing for powerful zero-shot capabilities and flexible prompting. Despite being trained with 1.1 billion masks, SAM's mask prediction quality falls short in many cases, particularly when dealing with objects that have intricate structures. We propose HQ-SAM, equipping SAM with the ability to accurately segment any object, while maintaining SAM's original promptable design, efficiency, and zero-shot generalizability. Our careful design reuses and preserves the pre-trained model weights of SAM, while only introducing minimal additional parameters and computation. We design a learnable High-Quality Output Token, which is injected into SAM's mask decoder and is responsible for predicting the high-quality mask. Instead of only applying it on mask-decoder features, we first fuse them with early and final ViT features for improved mask details. To train our introduced learnable parameters, we compose a dataset of 44K fine-grained masks from several sources. HQ-SAM is only trained on the introduced detaset of 44k masks, which takes only 4 hours on 8 GPUs. We show the efficacy of HQ-SAM in a suite of 9 diverse segmentation datasets across different downstream tasks, where 7 out of them are evaluated in a zero-shot transfer protocol. Our code and models will be released at https://github.com/SysCV/SAM-HQ.Comment: We propose HQ-SAM to upgrade SAM for high-quality zero-shot segmentation. Github: https://github.com/SysCV/SAM-H

Effect of Geometry on Performance of Interlacer

Interlacer is the key part of interlacing technology that is adopted to improve the cohesion between loose multifilaments. Aimed at finding the interlacers with better performance, the present research designed five interlacers that can be classified into round type and cornered type. These five interlacers are different in cross-sectional shapes of yarn channel but are the same in the cross-sectional area. The evaluation of the performance of the interlacer includes the number and the strength of the tangles of the interlaced yarn it produces. Experiments are carried out at various supplied air pressures, yarn speeds and feed ratios. It was found that the interlacer with round cross-sectional shape of yarn channel is capable of producing an interlaced yarn with a large number of tangles and the cornered cross-sectional shape is effective in improving the strength of tangles. Among these five interlacers, the interlacer with an elliptical or an inverse-triangular shape has the best processing performanc

Extraction of Nitrogen Compounds from Tobacco Waste via Thermal Treatment

Alkaloids, typical nitrogen compounds, were found to be abundant in tobacco waste. The recovery of alkaloids from tobacco waste for biological pesticides could reduce the use of traditional chemical pesticides and avoid the pollution of farmland by the leaching of alkaloids from tobacco waste. Considering the fact that alkaloids can easily volatilize, thermal treatment is expected to be a potential technology to achieve the release and recovery of alkaloids from tobacco waste. For better understanding of conversion behavior of nitrogen-containing compounds in tobacco waste during thermal treatment, purge/trap-GC/MS (gas chromatography mass spectrometry), PY-GC/MS (pyrolysis-gas chromatography mass spectrometry), and fixed-bed/ATD-GC/MS (auto-thermal desorption gas chromatography mass spectrometry) were adopted to detect the ingredients and concentration of nitrogen-containing compounds in tobacco waste and/or volatiles. The results of purge/trap-GC/MS showed that nitrogen-containing compounds in tobacco waste could be effectively evaporated at 180 °C in the forms of N-benzyl-N-ethyl-P-isopropyl benzamide, 2-Amino-4-methylphenol, or N-butyl-tert-butylamine. Specifically, N-benzyl-N-ethyl-P-isopropyl benzamide was the main nitrogenous compound in the volatiles of tobacco wastes accordingly. (S)-3-(1-Methyl-2-pyrrolidinyl) pyridine was dominant in N-compounds in pyrolysis condition according to the results of Py-GC/MS. In air atmosphere, with the heating temperature increasing, the concentration of main (S)-3-(1-Methyl-2-pyrrolidinyl) pyridine was firstly increased and then decreased. Besides, the interactions between the released volatiles could be accelerated at a high temperature. Accordingly, these findings suggested that pyrolysis under proper conditions could effectively promote the extraction of alkaloids from tobacco waste