Modified cotton fabrics for improved ultraviolet protection performance and reactive dyeing property

Reactive quaternary ammonium cationic compound, [2,3-epoxypropyl-1-methy imidazole ammonium chloride (EPMI)] has been synthesized with 1-methylimidazole and epoxy chloropropane to improve the dyeing property of cotton fabrics and the cationic cotton fabrics combined Fe3+ to obtain UV protective property. The dye fixation (F%) of three reactive dyes is found to be 84.5-89.7% on modified cotton fabrics. Ultraviolet protection factor (UPF) value of modified cotton fabric is increased to 137.47 from 7.57 of untreated cotton fabric. The breaking strength of modified cotton fabrics is well retained. The hue (h) of dyed samples before and after modification shows a little change. The UPF of modified cotton fabric shows excellent durability. After 50 laundering cycles, the UPF values decrease to 12.47%. Scanning electron microscopy observations and XRD patterns show that the modified cotton fibres are left nearly undamaged by the modification. The FTIR spectra suggest that the EPMI and Fe3+ are combined on the cotton fibres

CLOP: Video-and-Language Pre-Training with Knowledge Regularizations

Video-and-language pre-training has shown promising results for learning generalizable representations. Most existing approaches usually model video and text in an implicit manner, without considering explicit structural representations of the multi-modal content. We denote such form of representations as structural knowledge, which express rich semantics of multiple granularities. There are related works that propose object-aware approaches to inject similar knowledge as inputs. However, the existing methods usually fail to effectively utilize such knowledge as regularizations to shape a superior cross-modal representation space. To this end, we propose a Cross-modaL knOwledge-enhanced Pre-training (CLOP) method with Knowledge Regularizations. There are two key designs of ours: 1) a simple yet effective Structural Knowledge Prediction (SKP) task to pull together the latent representations of similar videos; and 2) a novel Knowledge-guided sampling approach for Contrastive Learning (KCL) to push apart cross-modal hard negative samples. We evaluate our method on four text-video retrieval tasks and one multi-choice QA task. The experiments show clear improvements, outperforming prior works by a substantial margin. Besides, we provide ablations and insights of how our methods affect the latent representation space, demonstrating the value of incorporating knowledge regularizations into video-and-language pre-training.Comment: ACM Multimedia 2022 (MM'22