| 1. |
- Bhunia, Ankan Kumar, et al.
(författare)
-
Handwriting Transformers
- 2021
-
Ingår i: 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021). - : IEEE. - 9781665428125 - 9781665428132 ; , s. 1066-1074
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- We propose a novel transformer-based styled handwritten text image generation approach, HWT, that strives to learn both style-content entanglement as well as global and local writing style patterns. The proposed HWT captures the long and short range relationships within the style examples through a self-attention mechanism, thereby encoding both global and local style patterns. Further, the proposed transformer-based HWT comprises an encoder-decoder attention that enables style-content entanglement by gathering the style representation of each query character. To the best of our knowledge, we are the first to introduce a transformer-based generative network for styled handwritten text generation. Our proposed HWT generates realistic styled handwritten text images and significantly outperforms the state-of-the-art demonstrated through extensive qualitative, quantitative and human-based evaluations. The proposed HWT can handle arbitrary length of text and any desired writing style in a few-shot setting. Further, our HWT generalizes well to the challenging scenario where both words and writing style are unseen during training, generating realistic styled handwritten text images.
|
|
| 2. |
- Joseph, KJ, et al.
(författare)
-
Towards Open World Object Detection
- 2021
-
Ingår i: 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021. - : IEEE COMPUTER SOC. - 9781665445092 ; , s. 5826-5836
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Humans have a natural instinct to identify unknown object instances in their environments. The intrinsic curiosityabout these unknown instances aids in learning about them,when the corresponding knowledge is eventually available.This motivates us to propose a novel computer vision problem called: ‘Open World Object Detection’, where a modelis tasked to: 1) identify objects that have not been introduced to it as ‘unknown’, without explicit supervision to doso, and 2) incrementally learn these identified unknown categories without forgetting previously learned classes, whenthe corresponding labels are progressively received. Weformulate the problem, introduce a strong evaluation protocol and provide a novel solution, which we call ORE:Open World Object Detector, based on contrastive clustering and energy based unknown identification. Our experimental evaluation and ablation studies analyse the efficacyof ORE in achieving Open World objectives. As an interesting by-product, we find that identifying and characterisingunknown instances helps to reduce confusion in an incremental object detection setting, where we achieve state-ofthe-art performance, with no extra methodological effort.We hope that our work will attract further research into thisnewly identified, yet crucial research direction.
|
|
| 3. |
- Khan, Fahad Shabhaz, et al.
(författare)
-
Data Mining in Oral Medicine Using Decision Trees
- 2008
-
Ingår i: Proceedings of the 5th International Conference on Computer, Electrical, and Systems Science, and Engineering (CESSE 2008), Cairo, Egypt, February 6–8, 2008. - : World Academy of Science Engineering and Technology - WASET. ; 27, s. 225-230
-
Konferensbidrag (refereegranskat)abstract
- Data mining has been used very frequently to extract hidden information from large databases. This paper suggests the use of decision trees for continuously extracting the clinical reasoning in the form of medical expert’s actions that is inherent in large number of EMRs (Electronic Medical records). In this way the extracted data could be used to teach students of oral medicine a number of orderly processes for dealing with patients who represent with different problems within the practice context over time.
|
|
| 4. |
- Khan, Rahat, et al.
(författare)
-
Discriminative Color Descriptors
- 2013
-
Ingår i: Computer Vision and Pattern Recognition (CVPR), 2013. - : IEEE Computer Society. ; , s. 2866-2873
-
Konferensbidrag (refereegranskat)abstract
- Color description is a challenging task because of large variations in RGB values which occur due to scene accidental events, such as shadows, shading, specularities, illuminant color changes, and changes in viewing geometry. Traditionally, this challenge has been addressed by capturing the variations in physics-based models, and deriving invariants for the undesired variations. The drawback of this approach is that sets of distinguishable colors in the original color space are mapped to the same value in the photometric invariant space. This results in a drop of discriminative power of the color description. In this paper we take an information theoretic approach to color description. We cluster color values together based on their discriminative power in a classification problem. The clustering has the explicit objective to minimize the drop of mutual information of the final representation. We show that such a color description automatically learns a certain degree of photometric invariance. We also show that a universal color representation, which is based on other data sets than the one at hand, can obtain competing performance. Experiments show that the proposed descriptor outperforms existing photometric invariants. Furthermore, we show that combined with shape description these color descriptors obtain excellent results on four challenging datasets, namely, PASCAL VOC 2007, Flowers-102, Stanford dogs-120 and Birds-200.
|
|
| 5. |
- Narayan, Sanath, et al.
(författare)
-
Discriminative Region-based Multi-Label Zero-Shot Learning
- 2021
-
Ingår i: 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021). - : IEEE. - 9781665428125 ; , s. 8711-8720
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Multi-label zero-shot learning (ZSL) is a more realistic counter-part of standard single-label ZSL since several objects can co-exist in a natural image. However, the occurrence of multiple objects complicates the reasoning and requires region-specific processing of visual features to preserve their contextual cues. We note that the best existing multi-label ZSL method takes a shared approach towards attending to region features with a common set of attention maps for all the classes. Such shared maps lead to diffused attention, which does not discriminatively focus on relevant locations when the number of classes are large. Moreover, mapping spatially-pooled visual features to the class semantics leads to inter-class feature entanglement, thus hampering the classification. Here, we propose an alternate approach towards region-based discriminability-preserving multi-label zero-shot classification. Our approach maintains the spatial resolution to preserve region-level characteristics and utilizes a bi-level attention module (BiAM) to enrich the features by incorporating both region and scene context information. The enriched region-level features are then mapped to the class semantics and only their class predictions are spatially pooled to obtain image-level predictions, thereby keeping the multi-class features disentangled. Our approach sets a new state of the art on two large-scale multi-label zero-shot benchmarks: NUS-WIDE and Open Images. On NUS-WIDE, our approach achieves an absolute gain of 6.9% mAP for ZSL, compared to the best published results.
|
|
| 6. |
- Naseer, M., et al.
(författare)
-
A Self-supervised Approach for Adversarial Robustness
- 2020
-
Ingår i: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). - : IEEE. - 9781728171685 ; , s. 259-268
-
Konferensbidrag (refereegranskat)abstract
- Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems e.g., for classification, segmentation and object detection. The vulnerability of DNNs against such attacks can prove a major roadblock towards their real-world deployment. Transferability of adversarial examples demand generalizable defenses that can provide cross-task protection. Adversarial training that enhances robustness by modifying target model’s parameters lacks such generalizability. On the other hand, different input processing based defenses fall short in the face of continuously evolving attacks. In this paper, we take the first step to combine the benefits of both approaches and propose a self-supervised adversarial training mechanism in the input space. By design, our defense is a generalizable approach and provides significant robustness against the unseen adversarial attacks (\eg by reducing the success rate of translation-invariant ensemble attack from 82.6% to 31.9% in comparison to previous state-of-the-art). It can be deployed as a plug-and-play solution to protect a variety of vision systems, as we demonstrate for the case of classification, segmentation and detection.
|
|
| 7. |
- Naseer, Muzammal, et al.
(författare)
-
On Generating Transferable Targeted Perturbations
- 2021
-
Ingår i: 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021). - : IEEE. - 9781665428125 - 9781665428132 ; , s. 7688-7697
-
Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- While the untargeted black-box transferability of adversarial perturbations has been extensively studied before, changing an unseen model's decisions to a specific `targeted' class remains a challenging feat. In this paper, we propose a new generative approach for highly transferable targeted perturbations (\ours). We note that the existing methods are less suitable for this task due to their reliance on class-boundary information that changes from one model to another, thus reducing transferability. In contrast, our approach matches the perturbed image `distribution' with that of the target class, leading to high targeted transferability rates. To this end, we propose a new objective function that not only aligns the global distributions of source and target images, but also matches the local neighbourhood structure between the two domains. Based on the proposed objective, we train a generator function that can adaptively synthesize perturbations specific to a given input. Our generative approach is independent of the source or target domain labels, while consistently performs well against state-of-the-art methods on a wide range of attack settings. As an example, we achieve 32.63% target transferability from (an adversarially weak) VGG19BN to (a strong) WideResNet on ImageNet val. set, which is 4× higher than the previous best generative attack and 16× better than instance-specific iterative attack.
|
|
| 8. |
|
|
| 9. |
- Pang, Yanwei, et al.
(författare)
-
Mask-Guided Attention Network for Occluded Pedestrian Detection
- 2019
-
Ingår i: 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019). - : IEEE COMPUTER SOC. - 9781728148038 ; , s. 4966-4974
-
Konferensbidrag (refereegranskat)abstract
- Pedestrian detection relying on deep convolution neural networks has made significant progress. Though promising results have been achieved on standard pedestrians, the performance on heavily occluded pedestrians remains far from satisfactory. The main culprits are intra-class occlusions involving other pedestrians and inter-class occlusions caused by other objects, such as cars and bicycles. These result in a multitude of occlusion patterns. We propose an approach for occluded pedestrian detection with the following contributions. First, we introduce a novel mask-guided attention network that fits naturally into popular pedestrian detection pipelines. Our attention network emphasizes on visible pedestrian regions while suppressing the occluded ones by modulating full body features. Second, we empirically demonstrate that coarse-level segmentation annotations provide reasonable approximation to their dense pixel-wise counterparts. Experiments are performed on CityPersons and Caltech datasets. Our approach sets a new state-of-the-art on both datasets. Our approach obtains an absolute gain of 9.5% in log-average miss rate, compared to the best reported results [31] on the heavily occluded HO pedestrian set of CityPersons test set. Further, on the HO pedestrian set of Caltech dataset, our method achieves an absolute gain of 5.0% in log-average miss rate, compared to the best reported results [13]. Code and models are available at: https://github.com/Leotju/MGAN.
|
|
| 10. |
- Rajasegaran, J., et al.
(författare)
-
iTAML : An Incremental Task-Agnostic Meta-learning Approach
- 2020
-
Ingår i: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). - : IEEE. - 9781728171685 ; , s. 13585-13594
-
Konferensbidrag (refereegranskat)abstract
- Humans can continuously learn new knowledge as their experience grows. In contrast, previous learning in deep neural networks can quickly fade out when they are trained on a new task. In this paper, we hypothesize this problem can be avoided by learning a set of generalized parameters, that are neither specific to old nor new tasks. In this pursuit, we introduce a novel meta-learning approach that seeks to maintain an equilibrium between all the encountered tasks. This is ensured by a new meta-update rule which avoids catastrophic forgetting. In comparison to previous meta-learning techniques, our approach is task-agnostic. When presented with a continuum of data, our model automatically identifies the task and quickly adapts to it with just a single update. We perform extensive experiments on five datasets in a class-incremental setting, leading to significant improvements over the state of the art methods (e.g., a 21.3% boost on CIFAR100 with 10 incremental tasks). Specifically, on large-scale datasets that generally prove difficult cases for incremental learning, our approach delivers absolute gains as high as 19.1% and 7.4% on ImageNet and MS-Celeb datasets, respectively.
|
|
