|
|
|
|
| LEADER |
01000caa a22002652 4500 |
| 001 |
NLM365425648 |
| 003 |
DE-627 |
| 005 |
20240404234442.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
231226s2024 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TPAMI.2023.3339628
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1364.xml
|
| 035 |
|
|
|a (DE-627)NLM365425648
|
| 035 |
|
|
|a (NLM)38051621
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Tang, Wei
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a Context Disentangling and Prototype Inheriting for Robust Visual Grounding
|
| 264 |
|
1 |
|c 2024
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Revised 03.04.2024
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status PubMed-not-MEDLINE
|
| 520 |
|
|
|a Visual grounding (VG) aims to locate a specific target in an image based on a given language query. The discriminative information from context is important for distinguishing the target from other objects, particularly for the targets that have the same category as others. However, most previous methods underestimate such information. Moreover, they are usually designed for the standard scene (without any novel object), which limits their generalization to the open-vocabulary scene. In this paper, we propose a novel framework with context disentangling and prototype inheriting for robust visual grounding to handle both scenes. Specifically, the context disentangling disentangles the referent and context features, which achieves better discrimination between them. The prototype inheriting inherits the prototypes discovered from the disentangled visual features by a prototype bank to fully utilize the seen data, especially for the open-vocabulary scene. The fused features, obtained by leveraging Hadamard product on disentangled linguistic and visual features of prototypes to avoid sharp adjusting the importance between the two types of features, are then attached with a special token and feed to a vision Transformer encoder for bounding box regression. Extensive experiments are conducted on both standard and open-vocabulary scenes. The performance comparisons indicate that our method outperforms the state-of-the-art methods in both scenarios
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Li, Liang
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Liu, Xuejing
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Jin, Lu
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Tang, Jinhui
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Li, Zechao
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 46(2024), 5 vom: 25. Apr., Seite 3213-3229
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
|
| 773 |
1 |
8 |
|g volume:46
|g year:2024
|g number:5
|g day:25
|g month:04
|g pages:3213-3229
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TPAMI.2023.3339628
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 46
|j 2024
|e 5
|b 25
|c 04
|h 3213-3229
|