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Sparse transfer learning for interactive video search reranking
2012
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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However, existing reranking algorithms can only achieve limited improvement because of the well-known semantic gap between low level visual features and high level semantic concepts. ...
Technically, it a) considers the pair-wise discriminative information to maximally separate labeled query relevant samples from labeled query irrelevant ones, b) achieves a sparse representation for the ...
The dimension reduction, which aims to find a compact representation for the samples in a low dimensional subspace, is a suitable candidate. ...
doi:10.1145/2240136.2240139
fatcat:qvnayxhxnfcwtgu6di5j2suwna
Sparse Transfer Learning for Interactive Video Search Reranking
[article]
2011
arXiv
pre-print
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However, existing reranking algorithms can only achieve limited improvement because of the well-known semantic gap between low level visual features and high level semantic concepts. ...
Technically, it a) considers the pair-wise discriminative information to maximally separate labeled query relevant samples from labeled query irrelevant ones, b) achieves a sparse representation for the ...
N
w
and the corresponding low
Sparse Transfer Learning for Interactive Video Search Reranking
•
20: 7
dimensional feature matrix is given by
11
[ , , ...
arXiv:1103.2756v2
fatcat:ixdqa7ltkbhfdccz5vgihz7nqe
Attribute Based Image Search Re-Ranking
2015
International Journal of Science and Research (IJSR)
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Image search reranking is an effective approach to refine the text-based image search result. ...
In this paper, reranking methods are suggested address this problem in scalable fashion. ...
Finally, obtain a 9,744-dimensional feature for each image, consisting of a 1, 392 × 6-dimensional feature from the grids and a 1,392-dimensional feature from the image. ...
doi:10.21275/v4i11.nov151336
fatcat:j6lepns43zf3xb3vnt6q245kba
Improving person re-identification by soft biometrics based reranking
2013
2013 Seventh International Conference on Distributed Smart Cameras (ICDSC)
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These distance scores are used for reranking the initially returned matches. ...
During reranking, the soft biometric attributes are detected and attribute-based distance scores are calculated between pairs of images by using a regression model. ...
Since the feature dimension (5 + 5 = 10 in our case) is significantly smaller than the number of samples, we use Radial Basis Function (RBF) kernel to map the lower dimensional features into higher dimensional ...
doi:10.1109/icdsc.2013.6778216
dblp:conf/icdsc/AnCKYB13
fatcat:jx3tnjplhratdc3h6t4472hskm
Segmentation-free word spotting with exemplar SVMs
2014
Pattern Recognition
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Then, we use a more discriminative representation based on Fisher Vector to rerank the best regions retrieved, and the most promising ones are used to expand the Exemplar SVM training set and improve the ...
First, they can be improved in the choice of low level features. The features of [6, 7, 8, 14] produce sequence representations, which are usually slower to compare than fixed-length representations. ...
Unfortunately, vector quantization is not possible when the dimensionality of the vectors is not trivially low: as noted in [19] , to encode a descriptor of 128 dimensions using only 0.5 bits per dimension ...
doi:10.1016/j.patcog.2014.06.005
fatcat:7r4a6krnubdujmjspw2g34onxi
Semi-supervised training for statistical word alignment
2006
Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06
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We introduce a semi-supervised approach to training for statistical machine translation that alternates the traditional Expectation Maximization step that is applied on a large training corpus with a discriminative ...
Discriminative Reranking with Improved Search. ...
The low F-measure scores of the baselines motivate our work. ...
doi:10.3115/1220175.1220272
dblp:conf/acl/FraserM06
fatcat:62dmq3ptebcixpjmjjfx3kvq7u
Content-Based Video Search over 1 Million Videos with 1 Core in 1 Second
2015
Proceedings of the 5th ACM on International Conference on Multimedia Retrieval - ICMR '15
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accuracy trade-off of reranking. ...
Directions investigated include exploring different low-level and semanticsbased features, testing different compression factors and approximations during video search, and understanding the time v.s. ...
Effective low-level visual features include the Improved Dense Trajectories Fisher Vectors (IDT FV, 110592 dimensional) from [27, 4] . ...
doi:10.1145/2671188.2749398
dblp:conf/mir/YuJXYH15
fatcat:p2ios43a75anjjkuswkrrfuk44
Efficient Stacked Dependency Parsing by Forest Reranking
2013
Transactions of the Association for Computational Linguistics
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This paper proposes a discriminative forest reranking algorithm for dependency parsing that can be seen as a form of efficient stacked parsing. ...
A dynamic programming shift-reduce parser produces a packed derivation forest which is then scored by a discriminative reranker, using the 1-best tree output by the shift-reduce parser as guide features ...
The MERT algorithm is suited to tune low-dimensional parameters. The β was set to about 1.2 in case of local reranking, and to about 1.5 in case of non-local reranking. ...
doi:10.1162/tacl_a_00216
fatcat:nmtftcqitzgvbkfuisuazcsviy
Accessible image search
2009
Proceedings of the seventeen ACM international conference on Multimedia - MM '09
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Based on the measured accessibility scores, different reranking methods can be performed to prioritize the images with high accessibilities. ...
Rasche et al. formulate the recoloring task as a dimensionality reduction problem, i.e., how to map the colors in a 3-dimensional space into a 2dimensional space that can be recognized by colorblind viewers ...
Protanopes and deuteranopes have difficulty in discriminating red from green, whereas tritanopes have difficulty in discriminating blue from yellow. ...
doi:10.1145/1631272.1631314
dblp:conf/mm/WangLH09
fatcat:osshwy7fefckfmtzhr7alpbxeu
AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2003.11941v4 fatcat:tikxglq625eldnmkxynae3ke5yOther Versions
The framework consists of an evaluator that generalizes to evaluate recommendations involving the context, and a generator that maximizes the evaluator score by reinforcement learning, and a discriminator ...
• We present the EG-Rerank and EG-Rerank+ as standard approaches of the evaluator-generator framework. ...
EG-Rerank+ consistently improves the conversion rate by 2% over the fine-tuned industrial-level re-ranking model in online A/B tests, which is a significant improvement in a large-scale e-commerce platform ...
arXiv:2003.11941v5
fatcat:rwtqltj3xnfirj6r7dzrwi2wly
Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline
[article]
2021
arXiv
pre-print
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Rerankers fine-tuned from deep LM estimates candidate relevance based on rich contextualized matching signals. ...
In this paper, we discover otherwise and that popular reranker cannot fully exploit the improved retrieval result. ...
A discriminative reranker should be able to handle the
top portion of retriever results and avoid relying on those confounding features. ...
arXiv:2101.08751v1
fatcat:vq6xunkgz5gcjlcgffktbvfquu
Immediate, Scalable Object Category Detection
2014
2014 IEEE Conference on Computer Vision and Pattern Recognition
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indexing; (ii) a sparse representation of a HOG classifier using a set of mid-level discriminative classifier patches; and (iii) a fast method for spatial reranking images on their detections. ...
We evaluate the detection method on the standard PAS-CAL VOC 2007 dataset, together with a 100K image subset of ImageNet, and demonstrate near state of the art detection performance at low ranks whilst ...
Again, the FS with reranking performance at low ranks is quite good -though not at the same level as for the DPM trained templates of table 2. ...
doi:10.1109/cvpr.2014.305
dblp:conf/cvpr/AytarZ14
fatcat:dzk5z5uisrgsbd6ls45oc5jkdq
Where to Focus: Query Adaptive Matching for Instance Retrieval Using Convolutional Feature Maps
[article]
2016
arXiv
pre-print
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In this article, we alleviate this drawback by proposing a novel reranking algorithm using CFMs to refine the retrieval result obtained by existing methods. ...
Through extensive experiments, we show that our reranking approaches bring substantial performance improvement and by applying them we can outperform the state of the art on several instance retrieval ...
dimensionality settings. ...
arXiv:1606.06811v1
fatcat:tm56q4sptrg3tlflydmhlrqely
Forest Reranking: Discriminative Parsing with Non-Local Features
2008
Annual Meeting of the Association for Computational Linguistics
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We instead propose forest reranking, a method that reranks a packed forest of exponentially many parses. ...
Since exact inference is intractable with non-local features, we present an approximate algorithm inspired by forest rescoring that makes discriminative training practical over the whole Treebank. ...
This method can thus be viewed as a step towards the integration of discriminative reranking with traditional chart parsing. ...
dblp:conf/acl/Huang08
fatcat:kygyhok6njagjgdge5wrbq7fcq
Groupwise learning for ASR k-best list reranking in spoken language translation
2016
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Groupwise learning is robust to sentences with different ASR-confidence, meaning that the confidence threshold heuristics in reranking are no longer needed. ...
This technique is also complementary to linear discriminant analysis feature projection. Altogether a BLEU score improvement of 0.80 was achieved in an indomain English-to-French SLT task. ...
LDA aims to find a projection of the feature vector to a low dimensional space subject to the Fisher criterion, and was shown to give an extra 0.04-0.11 BLEU score increase in previous SLT enhancement ...
doi:10.1109/icassp.2016.7472853
dblp:conf/icassp/NgSSH16
fatcat:aqrrg7tij5bxhf36kautkcqlha
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