wanyu/IteraTeR_full_sent huggingface dataset:
Original:  including manual-based and NAS-based, as well as CNN-based and Transformer-based ones . Particularly, with single-model and single-scale testing, our HTC Dual-Swin-B achieves 58.6\% box AP and 51.1\% mask AP on COCO test-dev, which is significantly better than the state-of-the-art result (i.e., 57.7\% box AP and 50.2\% mask AP) achieved by a stronger baseline HTC++ with a larger backbone Swin-L.
Revised:  as well as CNN-based and Transformer-based ones . Particularly, with single-model and single-scale testing, our HTC Dual-Swin-B achieves 58.6\% box AP and 51.1\% mask AP on COCO test-dev, which is significantly better than the state-of-the-art result (i.e., 57.7\% box AP and 50.2\% mask AP) achieved by a stronger baseline HTC++ with a larger backbone Swin-L.
Edit intent: coherence
Original:  including manual-based and NAS-based, as well as CNN-based and Transformer-based ones . Particularly, with single-model and single-scale testing, our HTC Dual-Swin-B achieves 58.6\% box AP and 51.1\% mask AP on COCO test-dev, which is significantly better than the state-of-the-art result (i.e., 57.7\% box AP and 50.2\% mask AP) achieved by a stronger baseline HTC++ with a larger backbone Swin-L.
Revised:  including manual-based and NAS-based, as well as anchor-based and anchor-free-based ones, and significantly improve their performance by more than 3.0\% AP over the baseline on COCO . Particularly, with single-model and single-scale testing, our HTC Dual-Swin-B achieves 58.6\% box AP and 51.1\% mask AP on COCO test-dev, which is significantly better than the state-of-the-art result (i.e., 57.7\% box AP and 50.2\% mask AP) achieved by a stronger baseline HTC++ with a larger backbone Swin-L.
Edit intent: meaning-changed
Original:  including manual-based and NAS-based, as well as CNN-based and Transformer-based ones . Particularly, with single-model and single-scale testing, our HTC Dual-Swin-B achieves 58.6\% box AP and 51.1\% mask AP on COCO test-dev, which is significantly better than the state-of-the-art result (i.e., 57.7\% box AP and 50.2\% mask AP) achieved by a stronger baseline HTC++ with a larger backbone Swin-L.
Revised:  including manual-based and NAS-based, as well as CNN-based and Transformer-based ones . Particularly, with single-model and single-scale testing, our HTC Dual-Swin-B achieves 58.6\% box AP and 51.1\% mask AP on COCO test-dev, which is significantly better than the state-of-the-art result (i.e., 57.7\% box AP and 50.2\% mask AP) achieved by HTC++ with a larger backbone Swin-L.
Edit intent: clarity
Original:  including manual-based and NAS-based, as well as CNN-based and Transformer-based ones . Particularly, with single-model and single-scale testing, our HTC Dual-Swin-B achieves 58.6\% box AP and 51.1\% mask AP on COCO test-dev, which is significantly better than the state-of-the-art result (i.e., 57.7\% box AP and 50.2\% mask AP) achieved by a stronger baseline HTC++ with a larger backbone Swin-L.
Revised:  including manual-based and NAS-based, as well as CNN-based and Transformer-based ones . Particularly, with single-model and single-scale testing, our HTC Dual-Swin-B achieves 58.6\% box AP and 51.1\% mask AP on COCO test-dev, which is significantly better than the state-of-the-art result (i.e., 57.7\% box AP and 50.2\% mask AP) achieved by a stronger baseline HTC++ with Swin-L.
Edit intent: clarity
Original:   Without additional pre-training, CBNetV2 can be adapt to various backbones, including manual-based and NAS-based, as well as CNN-based and Transformer-based ones.
Revised:  CBNetV2 has strong generalization capabilities for different backbones and head designs of the detector architecture. Without additional pre-training, CBNetV2 can be adapt to various backbones, including manual-based and NAS-based, as well as CNN-based and Transformer-based ones.
Edit intent: meaning-changed
Original:  CBNetV2 is compatible with  most mainstream detectors, including one-stage and two-stage detectors, as well as anchor-based and anchor-free-based ones, and significantly improve their performance by more than 3.0\% AP over the baseline on COCO.
Revised:  CBNetV2 is compatible with the head designs of most mainstream detectors, including one-stage and two-stage detectors, as well as anchor-based and anchor-free-based ones, and significantly improve their performance by more than 3.0\% AP over the baseline on COCO.
Edit intent: meaning-changed
Original:  CBNetV2 is compatible with  most mainstream detectors, including one-stage and two-stage detectors, as well as anchor-based and anchor-free-based ones, and significantly improve their performance by more than 3.0\% AP over the baseline on COCO.
Revised:  CBNetV2 is compatible with most mainstream detectors, including one-stage and two-stage detectors, as well as anchor-based and anchor-free-based ones, and significantly improve their performances by more than 3.0\% AP over the baseline on COCO.
Edit intent: fluency
Original:  Particularly, with single-model and single-scale testing , our HTC Dual-Swin-B achieves 58.6 \% box AP and 51.1 \% mask AP on COCO test-dev, which is significantly better than the state-of-the-art result (i.e., 57.7\% box AP and 50.2\% mask AP) achieved by HTC++ with Swin-L . Code is released at URL
Revised:  Particularly, under the single-model and single-scale testing , our HTC Dual-Swin-B achieves 58.6 \% box AP and 51.1 \% mask AP on COCO test-dev, which is significantly better than the state-of-the-art result (i.e., 57.7\% box AP and 50.2\% mask AP) achieved by HTC++ with Swin-L . Code is released at URL
Edit intent: clarity
Original:  Particularly, with single-model and single-scale testing , our HTC Dual-Swin-B achieves 58.6 \% box AP and 51.1 \% mask AP on COCO test-dev, which is significantly better than the state-of-the-art result (i.e., 57.7\% box AP and 50.2\% mask AP) achieved by HTC++ with Swin-L . Code is released at URL
Revised:  Particularly, with single-model and single-scale testing protocol, our Dual-Swin-L achieves 59.4 \% box AP and 51.1 \% mask AP on COCO test-dev, which is significantly better than the state-of-the-art result (i.e., 57.7\% box AP and 50.2\% mask AP) achieved by HTC++ with Swin-L . Code is released at URL
Edit intent: meaning-changed
Original:  Particularly, with single-model and single-scale testing , our HTC Dual-Swin-B achieves 58.6 \% box AP and 51.1 \% mask AP on COCO test-dev, which is significantly better than the state-of-the-art result (i.e., 57.7\% box AP and 50.2\% mask AP) achieved by HTC++ with Swin-L . Code is released at URL
Revised:  Particularly, with single-model and single-scale testing , our HTC Dual-Swin-B achieves 58.6 \% box AP and 51.6 \% mask AP on COCO test-dev, which is significantly better than the state-of-the-art result (i.e., 57.7\% box AP and 50.2\% mask AP) achieved by HTC++ with Swin-L . Code is released at URL
Edit intent: meaning-changed
Original:  Particularly, with single-model and single-scale testing , our HTC Dual-Swin-B achieves 58.6 \% box AP and 51.1 \% mask AP on COCO test-dev, which is significantly better than the state-of-the-art result (i.e., 57.7\% box AP and 50.2\% mask AP) achieved by HTC++ with Swin-L . Code is released at URL
Revised:  Particularly, with single-model and single-scale testing , our HTC Dual-Swin-B achieves 58.6 \% box AP and 51.1 \% mask AP on COCO test-dev, which is significantly better than the state-of-the-art result (i.e., 57.7\% box AP and 50.2\% mask AP) . Code is released at URL
Edit intent: meaning-changed
Original:  Particularly, with single-model and single-scale testing , our HTC Dual-Swin-B achieves 58.6 \% box AP and 51.1 \% mask AP on COCO test-dev, which is significantly better than the state-of-the-art result (i.e., 57.7\% box AP and 50.2\% mask AP) achieved by HTC++ with Swin-L . Code is released at URL
Revised:  Particularly, with single-model and single-scale testing , our HTC Dual-Swin-B achieves 58.6 \% box AP and 51.1 \% mask AP on COCO test-dev, which is significantly better than the state-of-the-art result (i.e., 57.7\% box AP and 50.2\% mask AP) achieved by HTC++ with Swin-L . Code is available at URL
Edit intent: clarity
Original:  In this paper, we propose a novel and flexible backbone framework, namely CBNetV2, to better train existing open-sourced pre-trained backbones under the pre-training fine-tuning protocol .
Revised:  In this paper, we propose a novel and flexible backbone framework, namely CBNetV2, to construct high-performance detectors using existing open-sourced pre-trained backbones under the pre-training fine-tuning protocol .
Edit intent: clarity
Original:  In this paper, we propose a novel and flexible backbone framework, namely CBNetV2, to better train existing open-sourced pre-trained backbones under the pre-training fine-tuning protocol .
Revised:  In this paper, we propose a novel and flexible backbone framework, namely CBNetV2, to better train existing open-sourced pre-trained backbones under the pre-training fine-tuning paradigm .
Edit intent: clarity
Original:  Without additional pre-training  , CBNetV2 can be adapted to various backbones , including manual-based and NAS-based, as well as CNN-based and Transformer-based ones.
Revised:  Without additional pre-training of the composite backbone , CBNetV2 can be adapted to various backbones , including manual-based and NAS-based, as well as CNN-based and Transformer-based ones.
Edit intent: meaning-changed
Original:  Without additional pre-training  , CBNetV2 can be adapted to various backbones , including manual-based and NAS-based, as well as CNN-based and Transformer-based ones.
Revised:  Without additional pre-training , CBNetV2 can be adapted to various backbones , including manual-based and NAS-based, as well as CNN-based vs. Transformer-based ones.
Edit intent: coherence
Original:  Without additional pre-training  , CBNetV2 can be adapted to various backbones , including manual-based and NAS-based, as well as CNN-based and Transformer-based ones. Experiments provide strong evidence showing that composite backbones are more efficient, effective, and resource-friendly than wider and deeper networks. CBNetV2 is compatible with the head designs of most mainstream detectors ,
Revised:  Without additional pre-training , CBNetV2 can be adapted to various backbones , including manual-based and NAS-based, as well as CNN-based and Transformer-based ) and head designs of most mainstream detectors ,
Edit intent: coherence
Original:  CBNetV2 is compatible with the head designs of most mainstream detectors , including one-stage and two-stage detectors, as well as anchor-based and anchor-free-based ones, and significantly improve their performances by more than 3.0\% AP over the baseline on COCO . Particularly, under the single-model and single-scale testing protocol, our Dual-Swin-L achieves 59.4\% box AP and 51.6\% mask AP on COCO test-dev  , which is significantly better than the state-of-the-art result ( i.e., 57.7\% box AP and 50.2\% mask AP)  . Code is available at URL
Revised:  CBNetV2 is compatible with the head designs of most mainstream detectors ( one-stage and two-stage detectors, as well as anchor-based and anchor-free-based ones, and significantly improve their performances by more than 3.0\% AP over the baseline on COCO . Particularly, under the single-model and single-scale testing protocol, our Dual-Swin-L achieves 59.4\% box AP and 51.6\% mask AP on COCO test-dev , which is significantly better than the state-of-the-art result ( i.e., 57.7\% box AP and 50.2\% mask AP) . Code is available at URL
Edit intent: clarity
Original:  including one-stage and two-stage detectors, as well as anchor-based and anchor-free-based ones, and significantly improve their performances by more than 3.0\% AP over the baseline on COCO . Particularly, under the single-model and single-scale testing protocol, our Dual-Swin-L achieves 59.4\% box AP and 51.6\% mask AP on COCO test-dev  , which is significantly better than the state-of-the-art result ( i.e., 57.7\% box AP and 50.2\% mask AP)  . Code is available at URL
Revised:  including one-stage vs. two-stage detectors, as well as anchor-based and anchor-free-based ones, and significantly improve their performances by more than 3.0\% AP over the baseline on COCO . Particularly, under the single-model and single-scale testing protocol, our Dual-Swin-L achieves 59.4\% box AP and 51.6\% mask AP on COCO test-dev , which is significantly better than the state-of-the-art result ( i.e., 57.7\% box AP and 50.2\% mask AP) . Code is available at URL
Edit intent: meaning-changed
Original:  including one-stage and two-stage detectors, as well as anchor-based and anchor-free-based ones, and significantly improve their performances by more than 3.0\% AP over the baseline on COCO . Particularly, under the single-model and single-scale testing protocol, our Dual-Swin-L achieves 59.4\% box AP and 51.6\% mask AP on COCO test-dev  , which is significantly better than the state-of-the-art result ( i.e., 57.7\% box AP and 50.2\% mask AP)  . Code is available at URL
Revised:  including one-stage and two-stage , anchor-based and anchor-free-based ones, and significantly improve their performances by more than 3.0\% AP over the baseline on COCO . Particularly, under the single-model and single-scale testing protocol, our Dual-Swin-L achieves 59.4\% box AP and 51.6\% mask AP on COCO test-dev , which is significantly better than the state-of-the-art result ( i.e., 57.7\% box AP and 50.2\% mask AP) . Code is available at URL
Edit intent: clarity
Original:  including one-stage and two-stage detectors, as well as anchor-based and anchor-free-based ones, and significantly improve their performances by more than 3.0\% AP over the baseline on COCO . Particularly, under the single-model and single-scale testing protocol, our Dual-Swin-L achieves 59.4\% box AP and 51.6\% mask AP on COCO test-dev  , which is significantly better than the state-of-the-art result ( i.e., 57.7\% box AP and 50.2\% mask AP)  . Code is available at URL
Revised:  including one-stage and two-stage detectors, as well as anchor-based vs. anchor-free-based ones, and significantly improve their performances by more than 3.0\% AP over the baseline on COCO . Particularly, under the single-model and single-scale testing protocol, our Dual-Swin-L achieves 59.4\% box AP and 51.6\% mask AP on COCO test-dev , which is significantly better than the state-of-the-art result ( i.e., 57.7\% box AP and 50.2\% mask AP) . Code is available at URL
Edit intent: clarity
Original:  including one-stage and two-stage detectors, as well as anchor-based and anchor-free-based ones, and significantly improve their performances by more than 3.0\% AP over the baseline on COCO . Particularly, under the single-model and single-scale testing protocol, our Dual-Swin-L achieves 59.4\% box AP and 51.6\% mask AP on COCO test-dev  , which is significantly better than the state-of-the-art result ( i.e., 57.7\% box AP and 50.2\% mask AP)  . Code is available at URL
Revised:  including one-stage and two-stage detectors, as well as anchor-based and anchor-free-based ). Experiments provide strong evidence that, compared with simply increasing the depth and width of the network, CBNetV2 introduces a more efficient, effective, and resource-friendly way to build high-performance backbone networks . Particularly, under the single-model and single-scale testing protocol, our Dual-Swin-L achieves 59.4\% box AP and 51.6\% mask AP on COCO test-dev , which is significantly better than the state-of-the-art result ( i.e., 57.7\% box AP and 50.2\% mask AP) . Code is available at URL
Edit intent: meaning-changed
Original:  including one-stage and two-stage detectors, as well as anchor-based and anchor-free-based ones, and significantly improve their performances by more than 3.0\% AP over the baseline on COCO . Particularly, under the single-model and single-scale testing protocol, our Dual-Swin-L achieves 59.4\% box AP and 51.6\% mask AP on COCO test-dev  , which is significantly better than the state-of-the-art result ( i.e., 57.7\% box AP and 50.2\% mask AP)  . Code is available at URL
Revised:  including one-stage and two-stage detectors, as well as anchor-based and anchor-free-based ones, and significantly improve their performances by more than 3.0\% AP over the baseline on COCO . Particularly, our Dual-Swin-L achieves 59.4\% box AP and 51.6\% mask AP on COCO test-dev , which is significantly better than the state-of-the-art result ( i.e., 57.7\% box AP and 50.2\% mask AP) . Code is available at URL
Edit intent: clarity
Original:  including one-stage and two-stage detectors, as well as anchor-based and anchor-free-based ones, and significantly improve their performances by more than 3.0\% AP over the baseline on COCO . Particularly, under the single-model and single-scale testing protocol, our Dual-Swin-L achieves 59.4\% box AP and 51.6\% mask AP on COCO test-dev  , which is significantly better than the state-of-the-art result ( i.e., 57.7\% box AP and 50.2\% mask AP)  . Code is available at URL
Revised:  including one-stage and two-stage detectors, as well as anchor-based and anchor-free-based ones, and significantly improve their performances by more than 3.0\% AP over the baseline on COCO . Particularly, under the single-model and single-scale testing protocol, our Dual-Swin-L achieves 59.4\% box AP and 51.6\% mask AP on COCO test-dev under the single-model and single-scale testing protocol , which is significantly better than the state-of-the-art result ( i.e., 57.7\% box AP and 50.2\% mask AP) . Code is available at URL
Edit intent: fluency
Original:  including one-stage and two-stage detectors, as well as anchor-based and anchor-free-based ones, and significantly improve their performances by more than 3.0\% AP over the baseline on COCO . Particularly, under the single-model and single-scale testing protocol, our Dual-Swin-L achieves 59.4\% box AP and 51.6\% mask AP on COCO test-dev  , which is significantly better than the state-of-the-art result ( i.e., 57.7\% box AP and 50.2\% mask AP)  . Code is available at URL
Revised:  including one-stage and two-stage detectors, as well as anchor-based and anchor-free-based ones, and significantly improve their performances by more than 3.0\% AP over the baseline on COCO . Particularly, under the single-model and single-scale testing protocol, our Dual-Swin-L achieves 59.4\% box AP and 51.6\% mask AP on COCO test-dev , which is significantly better than the state-of-the-art result ( 57.7\% box AP and 50.2\% mask AP) . Code is available at URL
Edit intent: coherence
Original:  including one-stage and two-stage detectors, as well as anchor-based and anchor-free-based ones, and significantly improve their performances by more than 3.0\% AP over the baseline on COCO . Particularly, under the single-model and single-scale testing protocol, our Dual-Swin-L achieves 59.4\% box AP and 51.6\% mask AP on COCO test-dev  , which is significantly better than the state-of-the-art result ( i.e., 57.7\% box AP and 50.2\% mask AP)  . Code is available at URL
Revised:  including one-stage and two-stage detectors, as well as anchor-based and anchor-free-based ones, and significantly improve their performances by more than 3.0\% AP over the baseline on COCO . Particularly, under the single-model and single-scale testing protocol, our Dual-Swin-L achieves 59.4\% box AP and 51.6\% mask AP on COCO test-dev , which is significantly better than the state-of-the-art result ( i.e., 57.7\% box AP and 50.2\% mask AP) achieved by Swin-L, while the training schedule is reduced by 6\times. With multi-scale testing, we push the current best single model result to a new record of 60.1\% box AP and 52.3\% mask AP without using extra training data . Code is available at URL
Edit intent: meaning-changed
Original:  Al-though imbalance problem has been extensivelystudied , the impact of imbalanced data on GCN-based linkage prediction task is quite different, which would cause problems in two aspects: im-balanced linkage labels and biased graph represen-tations .
Revised:  Although imbalance problem has been extensivelystudied , the impact of imbalanced data on GCN-based linkage prediction task is quite different, which would cause problems in two aspects: im-balanced linkage labels and biased graph represen-tations .
Edit intent: fluency
Original:  Al-though imbalance problem has been extensivelystudied , the impact of imbalanced data on GCN-based linkage prediction task is quite different, which would cause problems in two aspects: im-balanced linkage labels and biased graph represen-tations .
Revised:  Al-though imbalance problem has been extensively studied , the impact of imbalanced data on GCN-based linkage prediction task is quite different, which would cause problems in two aspects: im-balanced linkage labels and biased graph represen-tations .
Edit intent: fluency
Original:  Al-though imbalance problem has been extensivelystudied , the impact of imbalanced data on GCN-based linkage prediction task is quite different, which would cause problems in two aspects: im-balanced linkage labels and biased graph represen-tations .
Revised:  Al-though imbalance problem has been extensivelystudied , the impact of imbalanced data on GCN-based linkage prediction task is quite different, which would cause problems in two aspects: imbalanced linkage labels and biased graph represen-tations .
Edit intent: fluency
Original:  Al-though imbalance problem has been extensivelystudied , the impact of imbalanced data on GCN-based linkage prediction task is quite different, which would cause problems in two aspects: im-balanced linkage labels and biased graph represen-tations .
Revised:  Al-though imbalance problem has been extensivelystudied , the impact of imbalanced data on GCN-based linkage prediction task is quite different, which would cause problems in two aspects: im-balanced linkage labels and biased graph representations .
Edit intent: fluency
Original:  To tacklethese problems, we evaluate the feasibility of thoseexisting methods for imbalanced image classifica-tion problem on graphs with extensive experiments, and present a new method to alleviate the imbal-anced labels and also augment graph representa-tions using a Reverse-Imbalance Weighted Sam-pling (RIWS) strategy, followed with insightfulanalyses and discussions.
Revised:  To tackle these problems, we evaluate the feasibility of thoseexisting methods for imbalanced image classifica-tion problem on graphs with extensive experiments, and present a new method to alleviate the imbal-anced labels and also augment graph representa-tions using a Reverse-Imbalance Weighted Sam-pling (RIWS) strategy, followed with insightfulanalyses and discussions.
Edit intent: fluency
Original:  To tacklethese problems, we evaluate the feasibility of thoseexisting methods for imbalanced image classifica-tion problem on graphs with extensive experiments, and present a new method to alleviate the imbal-anced labels and also augment graph representa-tions using a Reverse-Imbalance Weighted Sam-pling (RIWS) strategy, followed with insightfulanalyses and discussions.
Revised:  To tacklethese problems, we evaluate the feasibility of those existing methods for imbalanced image classifica-tion problem on graphs with extensive experiments, and present a new method to alleviate the imbal-anced labels and also augment graph representa-tions using a Reverse-Imbalance Weighted Sam-pling (RIWS) strategy, followed with insightfulanalyses and discussions.
Edit intent: fluency
Original:  To tacklethese problems, we evaluate the feasibility of thoseexisting methods for imbalanced image classifica-tion problem on graphs with extensive experiments, and present a new method to alleviate the imbal-anced labels and also augment graph representa-tions using a Reverse-Imbalance Weighted Sam-pling (RIWS) strategy, followed with insightfulanalyses and discussions.
Revised:  To tacklethese problems, we evaluate the feasibility of thoseexisting methods for imbalanced image classification problem on graphs with extensive experiments, and present a new method to alleviate the imbal-anced labels and also augment graph representa-tions using a Reverse-Imbalance Weighted Sam-pling (RIWS) strategy, followed with insightfulanalyses and discussions.
Edit intent: fluency