Keyword-Based Diverse Image Retrieval With Variational Multiple Instance Graph.

The task of cross-modal image retrieval has recently attracted considerable research attention. In real-world scenarios, keyword-based queries issued by users are usually short and have broad semantics. Therefore, semantic diversity is as important as retrieval accuracy in such user-oriented services, which improves user experience. However, most typical cross-modal image retrieval methods based on single point query embedding inevitably result in low semantic diversity, while existing diverse retrieval approaches frequently lead to low accuracy due to a lack of cross-modal understanding. To address this challenge, we introduce an end-to-end solution termed variational multiple instance graph (VMIG), in which a continuous semantic space is learned to capture diverse query semantics, and the retrieval task is formulated as a multiple instance learning problems to connect diverse features across modalities. Specifically, a query-guided variational autoencoder is employed to model the continuous semantic space instead of learning a single-point embedding. Afterward, multiple instances of the image and query are obtained by sampling in the continuous semantic space and applying multihead attention, respectively. Thereafter, an instance graph is constructed to remove noisy instances and align cross-modal semantics. Finally, heterogeneous modalities are robustly fused under multiple losses. Extensive experiments on two real-world datasets have well verified the effectiveness of our proposed solution in both retrieval accuracy and semantic diversity.

Modified Camellia oleifera Shell Carbon with Enhanced Performance for the Adsorption of Cooking Fumes.

Using Camellia oleifera shell (COS) as a raw material and phosphoric acid as the activator, activated Camellia oleifera shell carbon (COSC-0) was prepared and then modified by Fenton's reagent (named as COSC-1). SEM, GC-MS, FTIR, and specific surface area and pore analyzers were used to study the adsorption performance of COS, COSC-0, and COSC-1 on cooking fumes. Results showed that COSC-1 was the best adsorbent compared with COS and COSC-0. The adsorption quantity and penetrating time of COSC-1 were 44.04 mg/g and 4.1 h, respectively. Most aldehydes could be adsorbed by COSC-1, which was due to the large number of carbonyl and carboxyl groups generated on the surface of COSC-1 from the action of Fenton's reagent. The adsorption effect of COSC-1 on different types of pollutants in cooking fumes was analyzed based on the similar compatibility principle. COSC-1 showed a much higher adsorption effect on the strong polarity functional groups than on weak polar groups. The results provide a theoretical basis for the application of Camellia oleifera shell carbon adsorption technology in the treatment of cooking fumes.