Progressive Conservative Adaptation for Evolving Target Domains

Conventional domain adaptation typically transfers knowledge from a source domain to a stationary target domain. However, in many real-world cases, target data usually emerge sequentially and have continuously evolving distributions. Restoring and adapting to such target data results in escalating computational and resource consumption over time. Hence, it is vital to devise algorithms to address the evolving domain adaptation (EDA) problem, \emph{i.e.,} adapting models to evolving target domains without access to historic target domains. To achieve this goal, we propose a simple yet effective approach, termed progressive conservative adaptation (PCAda). To manage new target data that diverges from previous distributions, we fine-tune the classifier head based on the progressively updated class prototypes. Moreover, as adjusting to the most recent target domain can interfere with the features learned from previous target domains, we develop a conservative sparse attention mechanism. This mechanism restricts feature adaptation within essential dimensions, thus easing the inference related to historical knowledge. The proposed PCAda is implemented with a meta-learning framework, which achieves the fast adaptation of the classifier with the help of the progressively updated class prototypes in the inner loop and learns a generalized feature without severely interfering with the historic knowledge via the conservative sparse attention in the outer loop. Experiments on Rotated MNIST, Caltran, and Portraits datasets demonstrate the effectiveness of our method.Comment: 7 pages, 5 figure

Electrochemical nitrate removal by magnetically immobilized nZVI anode on ammonia-oxidizing plate of RuO2–IrO2/Ti

Ammonium as the major reduction intermediate has always been the limitation of nitrate reduction by cathodic reduction or nano zero-valent iron (nZVI). In this work, we report the electrochemical nitrate removal by magnetically immobilized nZVI anode on RuO2–IrO2/Ti plate with ammonia-oxidizing function. This system shows maximum nitrate removal efficiency of 94.6% and nitrogen selectivity up to 72.8% at pH of 3.0, and it has also high nitrate removal efficiency (90.2%) and nitrogen selectivity (70.6%) near neutral medium (pH = 6). As the increase of the applied anodic potentials, both nitrate removal efficiency (from 27.2% to 94.6%) and nitrogen selectivity (70.4%–72.8%) increase. The incorpration of RuO2–IrO2/Ti plate with ammonia-oxidizing function on the nZVI anode enhances the nitrate reduction. The dosage of nZVI on RuO2–IrO2/Ti plate (from 0.2 g to 0.6 g) has a slight effect (the variance is no more than 10.0%) on the removal performance. Cyclic voltammetry, Tafel analysis and electrochemical impedance spectroscopy (EIS) were further used to investigate the reaction mechanisms occurring on the nZVI surfaces in terms of CV curve area, corrosion voltage, corrosion current density and charge-transfer resistance. In conclusion, high nitrate removal performance of magnetically immobilized nZVI anode coupled with RuO2–IrO2/Ti plate may guide the design of improved electrochemical reduction by nZVI-based anode for practical nitrate remediation

Identity-Preserving Talking Face Generation with Landmark and Appearance Priors

Generating talking face videos from audio attracts lots of research interest. A few person-specific methods can generate vivid videos but require the target speaker's videos for training or fine-tuning. Existing person-generic methods have difficulty in generating realistic and lip-synced videos while preserving identity information. To tackle this problem, we propose a two-stage framework consisting of audio-to-landmark generation and landmark-to-video rendering procedures. First, we devise a novel Transformer-based landmark generator to infer lip and jaw landmarks from the audio. Prior landmark characteristics of the speaker's face are employed to make the generated landmarks coincide with the facial outline of the speaker. Then, a video rendering model is built to translate the generated landmarks into face images. During this stage, prior appearance information is extracted from the lower-half occluded target face and static reference images, which helps generate realistic and identity-preserving visual content. For effectively exploring the prior information of static reference images, we align static reference images with the target face's pose and expression based on motion fields. Moreover, auditory features are reused to guarantee that the generated face images are well synchronized with the audio. Extensive experiments demonstrate that our method can produce more realistic, lip-synced, and identity-preserving videos than existing person-generic talking face generation methods.Comment: CVPR2023, Code: https://github.com/Weizhi-Zhong/IP_LA