With photo-realistic image generation, Neural Radiance Field (NeRF) is widely used for large-scale dynamic scene reconstruction as autonomous driving simulator. However, large-scale scene reconstruction still suffers from extremely long training time and rendering time. Low-resolution (L-R) rendering combined with upsampling can alleviate this problem but it degrades image quality. In this paper, we design a lightweight reference decoder which exploits prior information from known views to improve image reconstruction quality of new views. In addition, to speed up prior information search, we propose an optical flow and structural similarity based prior information search method. Results on KITTI and VKITTI2 datasets show that our method significantly outperforms the baseline method in terms of training speed, rendering speed and rendering quality.

In this paper, we propose a similarity-aware deep adversarial learning (SADAL) approach for facial age estimation. Instead of making full access to the limited training samples which likely leads to bias age prediction, our SADAL aims to seek batches of unobserved hard-negative samples based on existing training samples, which typically reinforces the discriminativeness of the learned feature representation for facial ages. Motivated by the fact that age labels are usually correlated in real-world scenarios, we carefully develop a similarity-aware function to well measure the distance of each face pair based on the age value gaps. Consequently, the age-difference information is exploited in the synthetic feature space for robust age estimation. During the learning process, we jointly optimize both procedures of generating hard negatives and learning discriminative age ranker via a sequence of adversarial-game iterations. Another major issue lies on that existing methods only enforce the indiscriminativeness within each class, which is probably trapped into model overfitting and thus the generation capacity is limited particularly on unseen age classes with many individuals. To circumvent this problem, we propose a variational deep adversarial learning (VDAL) paradigm, which learns to encode each face sample in two factorized parts, i.e., the intra-class variance distribution and the intra-class invariant class center. Moreover, our VDAL principally optimizes the variational confidence lower bound on the variational factorized feature representation. To better enhance the discriminativeness of the age representation, our VDAL further learns to encode the ordinal relationship among age labels in the reconstructed subspace. Experimental results on folds of widely-evaluated benchmarking datasets demonstrate that our approach achieves promising performance in contrast to most state-of-the-art age estimation methods.

In this paper, we propose a similarity-aware deep adversarial learning (SADAL) approach for facial age estimation. Instead of making access to limited training samples which likely leads to sub-optima, our SADAL seeks sets of unobserved and plausible hard-examples based on existing training samples, which typically reinforces the discriminativeness of the learned feature descriptor for ages. Motivated by the fact that age labels are usually correlated in the real-world applications, we carefully develop a similarity-aware function in our approach, which dynamically measures each face pair with different weights based on different age value gaps. During the learning process, we jointly optimize both procedures of generating hard-examples and learning age estimator via a sequence of adversarial-game iterations. As a result, the smoothing aging pattern is exploited in the reconstructed hard-example space for robust age estimation. Experimental results on two standard benchmarking datasets show that our approach achieves superior performance compared with most state-of-the-art age estimation methods.

Chemical Structural Formula(CSF) recognition plays an important role in the molecular design and component retrieval. However, sketch-based CSF recognition remains an obstacle in current retrieval systems. This paper introduces a system for sketch CSF recognition on smart mobile devices. A dual-mode-based method is proposed to distinguish the gestures for character inputs and non-character inputs instead of the ordinary segmentation approaches. An attribute graph model is established to describe effectively all necessary information of a sketched CSF. Chemical knowledge is adopted to refine the candidates of structure relationship among elements. The experiments results demonstrate that the proposed method outperforms the existing methods for free-sketch CSFs on effectiveness and flexibility.