UM-Net: 閲嶆柊鎬濊€冪敤浜庢伅鑲夊垎鍓茬殑ICGNet锛岀粨鍚堜笉纭畾鎬у缓妯鏂囩尞閫熼€?鍩轰簬澶氭ā鎬?鍗婄洃鐫f繁搴﹀涔犵殑鐥呯悊瀛﹁瘖鏂笌鐥呯伓鍒嗗壊

Title

棰樼洰

UM-Net: Rethinking ICGNet for polyp segmentation with uncertainty modeling

UM-Net: 閲嶆柊鎬濊€冪敤浜庢伅鑲夊垎鍓茬殑ICGNet锛岀粨鍚堜笉纭畾鎬у缓妯?/p>

01

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Abatract

鎽樿

Automatic segmentation of polyps from colonoscopy images plays a critical role in the early diagnosis andtreatment of colorectal cancer. Nevertheless, some bottlenecks still exist. In our previous work, we mainlyfocused on polyps with intra-class inconsistency and low contrast, using ICGNet to solve them. Due to thedifferent equipment, specific locations and properties of polyps, the color distribution of the collected images isinconsistent. ICGNet was designed primarily with reverse-contour guide information and local鈥揼lobal contextinformation, ignoring this inconsistent color distribution, which leads to overfitting problems and makes itdifficult to focus only on beneficial image content. In addition, a trustworthy segmentation model should notonly produce high-precision results but also provide a measure of uncertainty to accompany its predictionsso that physicians can make informed decisions. However, ICGNet only gives the segmentation result andlacks the uncertainty measure. To cope with these novel bottlenecks, we further extend the original ICGNetto a comprehensive and effective network (UM-Net) with two main contributions that have been proved byexperiments to have substantial practical value. Firstly, we employ a color transfer operation to weaken therelationship between color and polyps, making the model more concerned with the shape of the polyps.Secondly, we provide the uncertainty to represent the reliability of the segmentation results and use varianceto rectify uncertainty. Our improved method is evaluated on five polyp datasets, which shows competitiveresults compared to other advanced methods in both learning ability and generalization capability.

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Method

鏂规硶

3.1. Problem definition

Let 饾憞 = {(饾憢饾憱 , 饾憣饾憱 )}饾憗饾憱=1 represent the 饾憗 labeled set, where each pair( 饾憢饾憱 , 饾憣饾憱 ) consists of an image 饾憢饾憱 鈭?R饾惗脳饾惢脳饾憡 and its correspondingground truth 饾憣饾憱 鈭?{0, 1} 饾惢脳饾憡 , where 饾惢 脳 饾憡 are spatial dimensions and饾惗 is the number of channels. As discussed in the introduction, the aim isto train a segmentation network 饾惞饾憼饾憭饾憯 under solving the polyp color anduncertainty problem to obtain good performance on the test data. Inthis work, given two inputs 饾憢1 饾憥 and 饾憢2 饾憦 , the color 饾憦 of 饾憢2 饾憦 is transferredto 饾憢1 饾憥 to get the new input 饾憢1 饾憦 , which constitute the segmentationnetwork 饾惞饾憼饾憭饾憯 ( 饾憢1 饾憦 ) . We also model uncertainty in the prediction results饾憟饾憼饾憥饾懀饾憯 ( 饾惞饾憼饾憭饾憯 ( 饾憢1 饾憦 )), with 饾憼 鈭?[0, 4], while minimizing the prediction bias饾憠 饾憥饾憻 ( 饾惞饾憼饾憭饾憯 ( 饾憢1 饾憦 ) , 饾憣 1 )

3.1 闂瀹氫箟

璁?饾憞 = {(饾憢饾憱 , 饾憣饾憱 )}饾憗饾憱=1 浠h〃 饾憗 涓凡鏍囨敞鐨勬暟鎹泦锛屽叾涓瘡涓€瀵?( 饾憢饾憱 , 饾憣饾憱 ) 鍖呭惈涓€涓浘鍍?饾憢饾憱 鈭?R饾惗脳饾惢脳饾憡 鍙婂叾瀵瑰簲鐨勭湡瀹炴爣绛?饾憣饾憱 鈭?{0, 1} 饾惢脳饾憡锛屽叾涓?饾惢 脳 饾憡 涓虹┖闂寸淮搴︼紝饾惗 琛ㄧず閫氶亾鏁般€傚鍓嶆枃鎵€杩帮紝鎴戜滑鐨勭洰鏍囨槸鍦ㄨВ鍐虫伅鑲夐鑹插拰涓嶇‘瀹氭€ч棶棰樼殑鍚屾椂锛岃缁冧竴涓垎鍓茬綉缁?饾惞饾憼饾憭饾憯锛屼互鍦ㄦ祴璇曟暟鎹笂鑾峰緱鑹ソ鐨勮〃鐜般€傚湪鏈伐浣滀腑锛岀粰瀹氫袱涓緭鍏?饾憢1 饾憥 鍜?饾憢2 饾憦锛屽皢 饾憢2 饾憦 鐨勯鑹?饾憦 杞崲鍒?饾憢1 饾憥锛屽緱鍒版柊鐨勮緭鍏?饾憢1 饾憦锛岀劧鍚庤緭鍏ュ埌鍒嗗壊缃戠粶 饾惞饾憼饾憭饾憯 ( 饾憢1 饾憦 )銆傛垜浠繕瀵归娴嬬粨鏋滅殑涓嶇‘瀹氭€?饾憟饾憼饾憥饾懀饾憯 ( 饾惞饾憼饾憭饾憯 ( 饾憢1 饾憦 )) 杩涜寤烘ā锛岎潙?鈭?[0, 4]锛屽苟涓斿湪鏈€灏忓寲棰勬祴鍋忓樊 饾憠饾憥饾憻 ( 饾惞饾憼饾憭饾憯 ( 饾憢1 饾憦 ), 饾憣* 1 ) 鐨勫悓鏃惰繘琛屼紭鍖栥€?/p>

Results

缁撴灉

5.1. Quantitative evaluation for metric superiority

5.1.1. Learning ability

In this section, we perform the learning ability of our approachon two datasets, and the quantitative results are shown in Tables 2and 3. Compared with ICGNet, UM-Net has improved the Dice andmIoU metrics from 87.93%, 89.56% to 89.26%, and 90.33% respectively on the EndoScene dataset, and from 92.35%, 91.99% to 93.04%,and 92.54% respectively on the Kvasir-SEG dataset. Similarly, ourmethod is superior to other advanced approaches and achieves the bestperformance, further demonstrating good model learning ability.In addition, we also conduct the complexity analysis comparingour method with other advanced methods. The indicators we compare include floating point operations (FLOPs), network parameters(Params), and frames per second (FPS). On the EndoScene dataset,the FLOPs, Params, and FPS of the UM-Net are 16.87G, 22.75M, and46 respectively, meanwhile achieving 15.62G, 22.75M, and 50 on theKvasir-SEG dataset. Although Polyp-PVT obtains the minimum valuein FLOPs, our method only increases 8.28G, and 7.66G on the twodatasets, respectively. In terms of Params, our model has fewer networkparameters than most advanced methods. Since the accuracy of polypsegmentation is crucial for physicians to produce accurate diagnosticresults, we pay more attention to the accuracy of segmentation withlittle difference in model computational complexity. Therefore, UM-Netis still considered to be the optimal model with reasonable efficiency.It is worth noting that the inference speed of our model can reachan average of 48 FPS, which can be used as an auxiliary system fordiagnosis to satisfy real-time prediction.

5.1. 瀹氶噺璇勪及鎸囨爣鐨勪紭瓒婃€?/p>

5.1.1. 瀛︿範鑳藉姏

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Figure

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UM-Net: 閲嶆柊鎬濊€冪敤浜庢伅鑲夊垎鍓茬殑ICGNet锛岀粨鍚堜笉纭畾鎬у缓妯鏂囩尞閫熼€?鍩轰簬澶氭ā鎬?鍗婄洃鐫f繁搴﹀涔犵殑鐥呯悊瀛﹁瘖鏂笌鐥呯伓鍒嗗壊

Fig. 1. Challenges and method of our framework to handle the polyps segmentation via using the colonoscopy images. From (a) to (b), they are preliminary work ICGNet and improved method UM-Net, the new challenges of our tasks, respectively

鍥?1. 鎴戜滑妗嗘灦澶勭悊閫氳繃缁撹偁闀滃浘鍍忚繘琛屾伅鑲夊垎鍓茬殑鎸戞垬涓庢柟娉曘€備粠 (a) 鍒?(b)锛屽垎鍒槸鍒濇宸ヤ綔ICGNet鍜屾敼杩涙柟娉昒M-Net锛屼互鍙婃垜浠换鍔¢潰涓寸殑鏂版寫鎴樸€?/p>

UM-Net: 閲嶆柊鎬濊€冪敤浜庢伅鑲夊垎鍓茬殑ICGNet锛岀粨鍚堜笉纭畾鎬у缓妯鏂囩尞閫熼€?鍩轰簬澶氭ā鎬?鍗婄洃鐫f繁搴﹀涔犵殑鐥呯悊瀛﹁瘖鏂笌鐥呯伓鍒嗗壊

Fig. 2. Overview of the improved UM-Net. It segments the polyps and consists of three stages. Stage1 Input: By using the new polyp images after the color transfer operationas input. Stage2 Feature extraction. Stage3 Outputs: Output segmentation mask as well as corresponding uncertainty. Specifically, the RCG, ALGM, and聽HPPF modules refer toICGNet (Du et al., 2022)

鍥?2. 鏀硅繘鍚庣殑UM-Net姒傝堪銆傝缃戠粶鐢ㄤ簬鎭倝鍒嗗壊锛屽寘鍚笁涓樁娈点€傞樁娈? 杈撳叆锛氶€氳繃棰滆壊杞崲鎿嶄綔鍚庣殑鏂版伅鑲夊浘鍍忎綔涓鸿緭鍏ャ€傞樁娈? 鐗瑰緛鎻愬彇銆傞樁娈? 杈撳嚭锛氳緭鍑哄垎鍓叉帺鐮佸強鐩稿簲鐨勪笉纭畾鎬с€傜壒鍒湴锛孯CG銆丄LGM鍜孒PPF妯″潡鍙傝€冧簡ICGNet锛圖u绛夛紝2022锛夈€?/p>

UM-Net: 閲嶆柊鎬濊€冪敤浜庢伅鑲夊垎鍓茬殑ICGNet锛岀粨鍚堜笉纭畾鎬у缓妯鏂囩尞閫熼€?鍩轰簬澶氭ā鎬?鍗婄洃鐫f繁搴﹀涔犵殑鐥呯悊瀛﹁瘖鏂笌鐥呯伓鍒嗗壊

Fig. 3. One iteration of the color transfer operation.

鍥?3. 棰滆壊杞崲鎿嶄綔鐨勪竴娆¤凯浠f祦绋嬨€?/p>

UM-Net: 閲嶆柊鎬濊€冪敤浜庢伅鑲夊垎鍓茬殑ICGNet锛岀粨鍚堜笉纭畾鎬у缓妯鏂囩尞閫熼€?鍩轰簬澶氭ā鎬?鍗婄洃鐫f繁搴﹀涔犵殑鐥呯悊瀛﹁瘖鏂笌鐥呯伓鍒嗗壊

Fig. 4. Qualitative results of different methods on Kvasir-SEG and EndoScene datasets. The segmentation results are converted to contours and shown in the last column (groundtruth in red, PraNet in cyan, ACSNet in yellow, CCBANet in black, SANet in white, ICGNet in blue, UM-Net in green). In addition, the red dashed boxes indicate the misseddiagnosis area, the red arrows indicate areas that are larger than the ground truth, and the white dashed boxes show the difference between ICGNet and UM-Net predictions.

鍥?4. 涓嶅悓鏂规硶鍦↘vasir-SEG鍜孍ndoScene鏁版嵁闆嗕笂鐨勫畾鎬х粨鏋溿€傚垎鍓茬粨鏋滆杞崲涓鸿疆寤撳苟鏄剧ず鍦ㄦ渶鍚庝竴鍒楋紙绾㈣壊涓虹湡瀹炲€硷紝闈掕壊涓篜raNet锛岄粍鑹蹭负ACSNet锛岄粦鑹蹭负CCBANet锛岀櫧鑹蹭负SANet锛岃摑鑹蹭负ICGNet锛岀豢鑹蹭负UM-Net锛夈€傛澶栵紝绾㈣壊铏氱嚎妗嗚〃绀烘紡璇婂尯鍩燂紝绾㈣壊绠ご鎸囩ず澶т簬鐪熷疄鍊肩殑鍖哄煙锛岀櫧鑹茶櫄绾挎鏄剧ずICGNet鍜孶M-Net棰勬祴缁撴灉涔嬮棿鐨勫樊寮傘€?/p>

UM-Net: 閲嶆柊鎬濊€冪敤浜庢伅鑲夊垎鍓茬殑ICGNet锛岀粨鍚堜笉纭畾鎬у缓妯鏂囩尞閫熼€?鍩轰簬澶氭ā鎬?鍗婄洃鐫f繁搴﹀涔犵殑鐥呯悊瀛﹁瘖鏂笌鐥呯伓鍒嗗壊

Fig. 5. Forest plot of ablation study on the EndoScene test set. Listed on the leftside are the submodules of the ablation study. On the right side are the submodulescorresponding Dice scores and 95% confidence intervals, and in the middle are theirvisual results, where diamond represents the Dice score of each submodule, and thehorizontal line connecting the diamond represents the upper and lower limits of thescore confidence interval

鍥?5. EndoScene娴嬭瘯闆嗕笂娑堣瀺瀹為獙鐨勬.鏋楀浘銆傚乏渚у垪鍑烘秷铻嶅疄楠岀殑鍚勪釜瀛愭ā鍧楋紝鍙充晶涓哄悇瀛愭ā鍧楀搴旂殑Dice鍒嗘暟鍙?5%缃俊鍖洪棿锛屼腑闂翠负瀹冧滑鐨勫彲瑙嗗寲缁撴灉锛屽叾涓彵褰唬琛ㄦ瘡涓瓙妯″潡鐨凞ice鍒嗘暟锛岃繛鎺ヨ彵褰㈢殑姘村钩绾胯〃绀鸿鍒嗘暟缃俊鍖洪棿鐨勪笂涓嬮檺銆?/p>

UM-Net: 閲嶆柊鎬濊€冪敤浜庢伅鑲夊垎鍓茬殑ICGNet锛岀粨鍚堜笉纭畾鎬у缓妯鏂囩尞閫熼€?鍩轰簬澶氭ā鎬?鍗婄洃鐫f繁搴﹀涔犵殑鐥呯悊瀛﹁瘖鏂笌鐥呯伓鍒嗗壊

Fig. 6. Feature visualization examples of the UM-Net鈥檚 second layer. From left to right are input images (the green curve represents the outline of ground truth), the E-Block 2feature, the RCG module feature, and the ALGM module feature, respectively. After applying two modules, the network well captured missing object parts and details near theboundary, and achieved feature representation.

鍥?6. UM-Net绗簩灞傜壒寰佸彲瑙嗗寲绀轰緥銆備粠宸﹀埌鍙冲垎鍒负杈撳叆鍥惧儚锛堢豢鑹叉洸绾胯〃绀虹湡瀹炶疆寤擄級銆丒-Block 2鐗瑰緛銆丷CG妯″潡鐗瑰緛鍜孉LGM妯″潡鐗瑰緛銆傚湪搴旂敤杩欎袱涓ā鍧楀悗锛岀綉缁滃緢濂藉湴鎹曟崏鍒颁簡缂哄け鐨勭墿浣撻儴鍒嗗拰杈圭晫闄勮繎鐨勭粏鑺傦紝骞跺疄鐜颁簡鐗瑰緛琛ㄨ揪銆?/p>

UM-Net: 閲嶆柊鎬濊€冪敤浜庢伅鑲夊垎鍓茬殑ICGNet锛岀粨鍚堜笉纭畾鎬у缓妯鏂囩尞閫熼€?鍩轰簬澶氭ā鎬?鍗婄洃鐫f繁搴﹀涔犵殑鐥呯悊瀛﹁瘖鏂笌鐥呯伓鍒嗗壊

Fig. 7. Shows the variation of UM-Net modeling uncertainty as the number of training iterations continues to increase. From top to bottom on the left are the input images,the ground truth, and the corresponding uncertainty. Row (a) denotes the uncertainty output without variance rectification. Row (b) denotes the uncertainty results of variancerectification. Row (c) denotes the variance calculated between the prediction masks and the ground truth

鍥?7. 鏄剧ず浜嗛殢鐫€璁粌杩唬娆℃暟鐨勫鍔狅紝UM-Net寤烘ā涓嶇‘瀹氭€х殑鍙樺寲鎯呭喌銆傚乏渚т粠涓婂埌涓嬪垎鍒负杈撳叆鍥惧儚銆佺湡瀹炲€煎強鍏跺搴旂殑涓嶇‘瀹氭€с€?a)琛岃〃绀烘湭缁忚繃鏂瑰樊淇鐨勪笉纭畾鎬ц緭鍑恒€?b)琛岃〃绀虹粡杩囨柟宸慨姝g殑涓嶇‘瀹氭€х粨鏋溿€?c)琛岃〃绀洪娴嬫帺鐮佷笌鐪熷疄鍊间箣闂磋绠楃殑鏂瑰樊銆?/p>

UM-Net: 閲嶆柊鎬濊€冪敤浜庢伅鑲夊垎鍓茬殑ICGNet锛岀粨鍚堜笉纭畾鎬у缓妯鏂囩尞閫熼€?鍩轰簬澶氭ā鎬?鍗婄洃鐫f繁搴﹀涔犵殑鐥呯悊瀛﹁瘖鏂笌鐥呯伓鍒嗗壊

Fig. 8. Provide an evaluation of the reliability degree of the result of two cases in the test set. For each case, from left to right, the first column is the input image and itscorresponding ground truth. The second column displays the prediction for the ICGNet and UM-Net. The third column displays the uncertainty map associated with the predictionfor both models. The last column displays the variance.

鍥?8. 瀵规祴璇曢泦涓袱涓渚嬬殑缁撴灉鍙潬鎬ц繘琛岃瘎浼般€傚浜庢瘡涓渚嬶紝浠庡乏鍒板彸锛岀涓€鍒楁槸杈撳叆鍥惧儚鍙婂叾瀵瑰簲鐨勭湡瀹炲€笺€傜浜屽垪鏄剧ずICGNet鍜孶M-Net鐨勯娴嬬粨鏋溿€傜涓夊垪鏄剧ず涓庤繖涓ょ妯″瀷棰勬祴鐩稿叧鐨勪笉纭畾鎬у浘銆傛渶鍚庝竴鍒楁樉绀烘柟宸浘銆?/p>

UM-Net: 閲嶆柊鎬濊€冪敤浜庢伅鑲夊垎鍓茬殑ICGNet锛岀粨鍚堜笉纭畾鎬у缓妯鏂囩尞閫熼€?鍩轰簬澶氭ā鎬?鍗婄洃鐫f繁搴﹀涔犵殑鐥呯悊瀛﹁瘖鏂笌鐥呯伓鍒嗗壊

Fig. 9. Failure cases in EndoScene (a, b) and Kvasir-SEG (c, d) datasets. Green and red contours outline our prediction and ground truth of the polyp boundary

鍥?9. EndoScene (a, b) 鍜?Kvasir-SEG (c, d) 鏁版嵁闆嗕腑鐨勫け璐ユ渚嬨€傜豢鑹插拰绾㈣壊杞粨鍒嗗埆鍕惧嫆鍑烘垜浠殑棰勬祴缁撴灉鍜屾伅鑲夎竟鐣岀殑鐪熷疄鍊笺€?/p>

Table

琛?/strong>

UM-Net: 閲嶆柊鎬濊€冪敤浜庢伅鑲夊垎鍓茬殑ICGNet锛岀粨鍚堜笉纭畾鎬у缓妯鏂囩尞閫熼€?鍩轰簬澶氭ā鎬?鍗婄洃鐫f繁搴﹀涔犵殑鐥呯悊瀛﹁瘖鏂笌鐥呯伓鍒嗗壊

Table 1Details of the datasets for training, validation and testing.

琛?1鐢ㄤ簬璁粌銆侀獙璇佸拰娴嬭瘯鐨勬暟鎹泦璇︽儏銆?/p>

UM-Net: 閲嶆柊鎬濊€冪敤浜庢伅鑲夊垎鍓茬殑ICGNet锛岀粨鍚堜笉纭畾鎬у缓妯鏂囩尞閫熼€?鍩轰簬澶氭ā鎬?鍗婄洃鐫f繁搴﹀涔犵殑鐥呯悊瀛﹁瘖鏂笌鐥呯伓鍒嗗壊

Table 2Quantitative results of the EndoScene test datasets. 鈥榥/a鈥?denotes that the results are not available.

琛?2EndoScene娴嬭瘯鏁版嵁闆嗙殑瀹氶噺缁撴灉銆傗€渘/a鈥濊〃绀虹粨鏋滀笉鍙敤銆?/p>

UM-Net: 閲嶆柊鎬濊€冪敤浜庢伅鑲夊垎鍓茬殑ICGNet锛岀粨鍚堜笉纭畾鎬у缓妯鏂囩尞閫熼€?鍩轰簬澶氭ā鎬?鍗婄洃鐫f繁搴﹀涔犵殑鐥呯悊瀛﹁瘖鏂笌鐥呯伓鍒嗗壊

Table 3Quantitative results of the Kvasir-SEG test datasets. 鈥榥/a鈥?denotes that the results are not available.

琛?3Kvasir-SEG娴嬭瘯鏁版嵁闆嗙殑瀹氶噺缁撴灉銆傗€渘/a鈥濊〃绀虹粨鏋滀笉鍙敤銆?/p>

UM-Net: 閲嶆柊鎬濊€冪敤浜庢伅鑲夊垎鍓茬殑ICGNet锛岀粨鍚堜笉纭畾鎬у缓妯鏂囩尞閫熼€?鍩轰簬澶氭ā鎬?鍗婄洃鐫f繁搴﹀涔犵殑鐥呯悊瀛﹁瘖鏂笌鐥呯伓鍒嗗壊

Table 4Quantitative results of the test datasets ColonDB, ETIS and CVC300.

琛?4ColonDB銆丒TIS鍜孋VC300娴嬭瘯鏁版嵁闆嗙殑瀹氶噺缁撴灉銆?/p>

UM-Net: 閲嶆柊鎬濊€冪敤浜庢伅鑲夊垎鍓茬殑ICGNet锛岀粨鍚堜笉纭畾鎬у缓妯鏂囩尞閫熼€?鍩轰簬澶氭ā鎬?鍗婄洃鐫f繁搴﹀涔犵殑鐥呯悊瀛﹁瘖鏂笌鐥呯伓鍒嗗壊

Table 5The quantitative evaluation of the ablation studies on the EndoScene test set.

琛?5EndoScene娴嬭瘯闆嗕笂娑堣瀺瀹為獙鐨勫畾閲忚瘎浼扮粨鏋溿€?/p>

UM-Net: 閲嶆柊鎬濊€冪敤浜庢伅鑲夊垎鍓茬殑ICGNet锛岀粨鍚堜笉纭畾鎬у缓妯鏂囩尞閫熼€?鍩轰簬澶氭ā鎬?鍗婄洃鐫f繁搴﹀涔犵殑鐥呯悊瀛﹁瘖鏂笌鐥呯伓鍒嗗壊

Table 695% confidence intervals for all metrics.

琛?6鎵€鏈夋寚鏍囩殑95%缃俊鍖洪棿銆?/p>

UM-Net: 閲嶆柊鎬濊€冪敤浜庢伅鑲夊垎鍓茬殑ICGNet锛岀粨鍚堜笉纭畾鎬у缓妯鏂囩尞閫熼€?鍩轰簬澶氭ā鎬?鍗婄洃鐫f繁搴﹀涔犵殑鐥呯悊瀛﹁瘖鏂笌鐥呯伓鍒嗗壊

Table 7Quantitative results for a subset (C6) of the PolypGen dataset.

琛?7PolypGen鏁版嵁闆嗗瓙闆嗭紙C6锛夌殑瀹氶噺缁撴灉銆?/p>

UM-Net: 閲嶆柊鎬濊€冪敤浜庢伅鑲夊垎鍓茬殑ICGNet锛岀粨鍚堜笉纭畾鎬у缓妯鏂囩尞閫熼€?鍩轰簬澶氭ā鎬?鍗婄洃鐫f繁搴﹀涔犵殑鐥呯悊瀛﹁瘖鏂笌鐥呯伓鍒嗗壊

Table 8Quantitative results on CVC-300-TV dataset

琛?8CVC-300-TV鏁版嵁闆嗙殑瀹氶噺缁撴灉銆?/p>

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