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| 3. |
- Bravo, L, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 4. |
- Tabiri, S, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 5. |
- Glasbey, JC, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 6. |
- Khattak, Muhammad Uzair, et al.
(författare)
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Self-regulating Prompts: Foundational Model Adaptation without Forgetting
- 2023
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Ingår i: 2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2023). - : IEEE COMPUTER SOC. - 9798350307184 - 9798350307191 ; , s. 15144-15154
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Konferensbidrag (refereegranskat)abstract
- Prompt learning has emerged as an efficient alternative for fine-tuning foundational models, such as CLIP, for various downstream tasks. Conventionally trained using the task-specific objective, i.e., cross-entropy loss, prompts tend to overfit downstream data distributions and find it challenging to capture task-agnostic general features from the frozen CLIP. This leads to the loss of the model's original generalization capability. To address this issue, our work introduces a self-regularization framework for prompting called PromptSRC (Prompting with Self-regulating Constraints). PromptSRC guides the prompts to optimize for both task-specific and task-agnostic general representations using a three-pronged approach by: (a) regulating prompted representations via mutual agreement maximization with the frozen model, (b) regulating with selfensemble of prompts over the training trajectory to encode their complementary strengths, and (c) regulating with textual diversity to mitigate sample diversity imbalance with the visual branch. To the best of our knowledge, this is the first regularization framework for prompt learning that avoids overfitting by jointly attending to pre-trained model features, the training trajectory during prompting, and the textual diversity. PromptSRC explicitly steers the prompts to learn a representation space that maximizes performance on downstream tasks without compromising CLIP generalization. We perform extensive experiments on 4 benchmarks where PromptSRC overall performs favorably well compared to the existing methods. Our code and pre-trained models are publicly available at: https://github.com/muzairkhattak/PromptSRC.
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| 7. |
- Wasim, Syed Talal, et al.
(författare)
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Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition
- 2023
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Ingår i: 2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2023). - : IEEE COMPUTER SOC. - 9798350307184 - 9798350307191 ; , s. 13732-13743
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Konferensbidrag (refereegranskat)abstract
- Recent video recognition models utilize Transformer models for long-range spatio-temporal context modeling. Video transformer designs are based on self-attention that can model global context at a high computational cost. In comparison, convolutional designs for videos offer an efficient alternative but lack long-range dependency modeling. Towards achieving the best of both designs, this work proposes Video-FocalNet, an effective and efficient architecture for video recognition that models both local and global contexts. Video-FocalNet is based on a spatio-temporal focal modulation architecture that reverses the interaction and aggregation steps of self-attention for better efficiency. Further, the aggregation step and the interaction step are both implemented using efficient convolution and element-wise multiplication operations that are computationally less expensive than their self-attention counterparts on video representations. We extensively explore the design space of focal modulation-based spatio-temporal context modeling and demonstrate our parallel spatial and temporal encoding design to be the optimal choice. Video-FocalNets perform favorably well against the state-of-the-art transformer-based models for video recognition on five large-scale datasets (Kinetics-400, Kinetics-600, SS-v2, Diving-48, and ActivityNet-1.3) at a lower computational cost. Our code/models are released at https://github.com/TalalWasim/Video-FocalNets.
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| 8. |
- Wasim, Syed Talal, et al.
(författare)
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Vita-CLIP: Video and text adaptive CLIP via Multimodal Prompting
- 2023
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Ingår i: 2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR). - : IEEE COMPUTER SOC. - 9798350301298 - 9798350301304 ; , s. 23034-23044
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Konferensbidrag (refereegranskat)abstract
- Adopting contrastive image-text pretrained models like CLIP towards video classification has gained attention due to its cost-effectiveness and competitive performance. However, recent works in this area face a trade-off. Fine-tuning the pretrained model to achieve strong supervised performance results in low zero-shot generalization. Similarly, freezing the backbone to retain zero-shot capability causes significant drop in supervised accuracy. Because of this, recent works in literature typically train separate models for supervised and zero-shot action recognition. In this work, we propose a multimodal prompt learning scheme that works to balance the supervised and zero-shot performance under a single unified training. Our prompting approach on the vision side caters for three aspects: 1) Global video-level prompts to model the data distribution; 2) Local frame-level prompts to provide per-frame discriminative conditioning; and 3) a summary prompt to extract a condensed video representation. Additionally, we define a prompting scheme on the text side to augment the textual context. Through this prompting scheme, we can achieve state-of-the-art zero-shot performance on Kinetics-600, HMDB51 and UCF101 while remaining competitive in the supervised setting. By keeping the pretrained backbone frozen, we optimize a much lower number of parameters and retain the existing general representation which helps achieve the strong zero-shot performance. Our codes/models will be released at https://github.com/TalalWasim/Vita-CLIP..
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| 9. |
- Basavoju, Srinivas, et al.
(författare)
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Pharmaceutical Co-crystallization of Norfloxacin
- 2006
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Ingår i: 2006 AAPS Annual Meeting and Exposition. - : American Association of Pharmaceutical Scientists.
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Konferensbidrag (refereegranskat)abstract
- Description: Aim: The objective of the study was to prepare co-crystal and salts of norfloxacin and to investigate their structural and pharmaceutical properties. Methods: Norfloxacin was crystallized in a series of solvents in an effort to investigate the polymorphism. Norfloxacin was also co-crystallized with isonicotinamide and succinic acid in different solvents. We have characterised these materials using DSC, IR, Raman and PXRD. The single crystal X-ray diffraction data was obtained and crystal structures were solved. The solubility and moisture sorption behaviour (0-90%RH) of these materials were determined. Results: Norfloxacin Anhydrate, 1 crystallizes in the triclinic P-1 space group with one neutral molecule in the asymmetric unit. The carboxylic acid group participates in the intramolecular O-HO (D=2.525 Å) hydrogen bonding with carbonyl group of the quinolone moiety. NorfloxacinIsonicotinamideCHCl3, 2 crystallizes in the centrosymmetric C2/c space group with one molecule of norfloxacin, one molecule of isonicotinamide and one molecule of CHCl3.in the asymmetric unit. Four molecules of norfloxacin generate a rectangular host type network with N-HO (D=2.668 Å) and N-HO (D=2.657 Å) interactions. Two isonicotinamide molecules form robust amideamide (N-HO, D=2.889 Å) homodimer synthon and fits into the rectangular grid (N-HO, D=2.929 Å). The CHCl3 molecules lie in the channels of the host frame work. Norfloxacin (Succinate)0.5 Hydrate, 3 crystallizes in the triclinic P-1 space group with one norfloxacin cation, half molecule of succinate dianion and one H2O molecule in the asymmetric unit. The two succinate anions and two norfloxacin cations form a cyclic tetramer synthon (N-HO, D=2.726 Å) and extends with H2O molecules through the hydrophilic channel generated by quinolone stacked layers (ππ, 4.041 Å) along the a-axis via O-HO (D=2.928 Å) interactions. The rank order of the solubility of these materials was 1<2
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| 10. |
- Hameed, Khurram, et al.
(författare)
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Far-Field DOA Estimation of Uncorrelated RADAR Signals through Coprime Arrays in Low SNR Regime by Implementing Cuckoo Search Algorithm
- 2022
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Ingår i: Electronics. - : MDPI AG. - 2079-9292. ; 11:4
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Tidskriftsartikel (refereegranskat)abstract
- For the purpose of attaining a high degree of freedom (DOF) for the direction of arrival (DOA) estimations in radar technology, coprime sensor arrays (CSAs) are evaluated in this paper. In addition, the global and local minima of extremely non-linear functions are investigated, aiming to improve DOF. The optimization features of the cuckoo search (CS) algorithm are utilized for DOA estimation of far-field sources in a low signal-to-noise ratio (SNR) environment. The analytical approach of the proposed CSAs, CS and global and local minima in terms of cumulative distribution function (CDF), fitness function and SNR for DOA accuracy are presented. The parameters like root mean square error (RMSE) for frequency distribution, RMSE variability analysis, estimation accuracy, RMSE for CDF, robustness against snapshots and noise and RMSE for Monte Carlo simulation runs are explored for proposed model performance estimation. In conclusion, the proposed DOA estimation in radar technology through CS and CSA achievements are contrasted with existing tools such as particle swarm optimization (PSO).
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