Coherent Temporal Synthesis for Incremental Action Segmentation

Data replay is a successful incremental learning technique for images. It prevents catastrophic forgetting by keeping a reservoir of previous data, original or synthesized, to ensure the model retains past knowledge while adapting to novel concepts. However, its application in the video domain is rudimentary, as it simply stores frame exemplars for action recognition. This paper presents the first exploration of video data replay techniques for incremental action segmentation, focusing on action temporal modeling. We propose a Temporally Coherent Action (TCA) model, which represents actions using a generative model instead of storing individual frames. The integration of a conditioning variable that captures temporal coherence allows our model to understand the evolution of action features over time. Therefore, action segments generated by TCA for replay are diverse and temporally coherent. In a 10-task incremental setup on the Breakfast dataset, our approach achieves significant increases in accuracy for up to 22% compared to the baselines.Comment: 10 pages, 6 figures, 5 tables, accepted to CVPR 202

Temporal Action Segmentation: An Analysis of Modern Techniques

Temporal action segmentation (TAS) in videos aims at densely identifying video frames in minutes-long videos with multiple action classes. As a long-range video understanding task, researchers have developed an extended collection of methods and examined their performance using various benchmarks. Despite the rapid growth of TAS techniques in recent years, no systematic survey has been conducted in these sectors. This survey analyzes and summarizes the most significant contributions and trends. In particular, we first examine the task definition, common benchmarks, types of supervision, and prevalent evaluation measures. In addition, we systematically investigate two essential techniques of this topic, i.e., frame representation and temporal modeling, which have been studied extensively in the literature. We then conduct a thorough review of existing TAS works categorized by their levels of supervision and conclude our survey by identifying and emphasizing several research gaps. In addition, we have curated a list of TAS resources, which is available at https://github.com/nus-cvml/awesome-temporal-action-segmentation.Comment: 19 pages, 9 figures, 8 table

Characterizing higher Auslander(-Gorenstein) Algebras

It is well known that for Auslander algebras, the category of all (finitely generated) projective modules is an abelian category and this property of abelianness characterizes Auslander algebras by Tachikawa theorem in 1974. Let $n$ be a positive integer. In this paper, by using torsion theoretic methods, we show that $n$-Auslander algebras can be characterized by the abelianness of the category of modules with projective dimension less than $n$ and a certain additional property, extending the classical Auslander-Tachikawa theorem. By Auslander-Iyama correspondence a categorical characterization of the class of Artin algebras having $n$-cluster tilting modules is obtained. Since higher Auslander algebras are a special case of higher Auslander-Gorenstein algebras, the results are given in the general setting as extending previous results of Kong. Moreover, as an application of some results, we give categorical descriptions for the semisimplicity and selfinjectivity of an Artin algebra. Higher Auslander-Gorenstein Algebras are also studied from the viewpoint of cotorsion pairs and, as application, we show that they satisfy in two nice equivalences

Relationships Between Key Dryland Ecosystem Services: A Case Study in Ordos, China

Dryland ecosystem services (ESs) have been severely harmed by global environmental changes and increased human activities. To improve ESs, it is necessary to understand how they interact in drylands. In this study, we selected Ordos dryland, which is situated in northern China, as the study area to assess its four key ESs—food supply (FS), carbon storage (CS), water yield (WY), and habitat quality (HQ)—and to identify the hotspots of multiple ES supply. Furthermore, we studied the constraint effects between ESs in Ordos in 2000, 2010, and 2020 and used a spatial trade-off model to map the trade-off and synergy areas of ESs from 2000 to 2010 and from 2010 to 2020. The results indicated that all four ESs in Ordos increased significantly over the study period. The hotspots for the supply of multiple ESs also increased in areal extent during this period, and the state of the regional ecological environment continued to improve. The constraint effect between ESs showed that as the CS increased, its constraint effect on WY and FS decreased and then increased, whereas its constraint effect on HQ only decreased; as the WY increased, its constraint effect on HQ decreased and then increased, and its constraint effect on FS continued to decrease; as the FS increased, its constraint effect on HQ continued to increase. From the change in the area of ESs trade-offs and synergies, there was an increase in the area of positive synergy for four pairs of ESs in Ordos, which were CS-WY, CS-HQ, WY-HQ, and FS-HQ. These findings help in establishing a scientific foundation for the management and optimization of ESs in drylands

Stabilizing polar phases in binary metal oxides by hole doping

The recent observation of ferroelectricity in the metastable phases of binary metal oxides, such as HfO2, ZrO2, Hf0.5Zr0.5O2, and Ga2O3, has garnered a lot of attention. These metastable ferroelectric phases are typically stabilized using epitaxial strain, alloying, or defect engineering. Here, we propose that hole doping plays a key role in the stabilization of polar phases in binary metal oxides. Using first-principles density-functional-theory calculations, we show that holes in these oxides mainly occupy one of the two oxygen sublattices. This hole localization, which is more pronounced in the polar phase than in the nonpolar phase, lowers the electrostatic energy of the system, and makes the polar phase more stable at sufficiently large concentrations.We demonstrate that this electrostatic mechanism is responsible for stabilization of the ferroelectric phase of HfO2 aliovalently doped with elements that introduce holes to the system, such as La and N. Finally, we show that spontaneous polarization in HfO2 is robust to hole doping, and a large polarization persists even under a high concentration of holes

Aggregation signature for small object tracking

Small object tracking becomes an increasingly important task, which however has been largely unexplored in computer vision. The great challenges stem from the facts that: 1) small objects show extreme vague and variable appearances, and 2) they tend to be lost easier as compared to normal-sized ones due to the shaking of lens. In this paper, we propose a novel aggregation signature suitable for small object tracking, especially aiming for the challenge of sudden and large drift. We make three-fold contributions in this work. First, technically, we propose a new descriptor, named aggregation signature, based on saliency, able to represent highly distinctive features for small objects. Second, theoretically, we prove that the proposed signature matches the foreground object more accurately with a high probability. Third, experimentally, the aggregation signature achieves a high performance on multiple datasets, outperforming the state-of-the-art methods by large margins. Moreover, we contribute with two newly collected benchmark datasets, i.e., small90 and small112, for visually small object tracking. The datasets will be available in https://github.com/bczhangbczhang/.Comment: IEEE Transactions on Image Processing, 201

Tissue specific induction of p62/sqstm1 by farnesoid X receptor

Background: Farnesoid X Receptor (FXR) is a member of the nuclear receptor superfamily and is a ligand-activated transcription factor essential for maintaining liver and intestinal homeostasis. FXR is protective against carcinogenesis and inflammation in liver and intestine as demonstrated by the development of inflammation and tumors in the liver and intestine of FXR knock-out mice. However, mechanisms for the protective effects of FXR are not completely understood. This study reports a novel role of FXR in regulating expression of Sqstm1, which encodes for p62 protein. p62 plays an important role in maintaining cellular homeostasis through selective autophagy and activating signal transduction pathways, such as NF-κB to support cell survival and caspase-8 to initiate apoptosis. FXR regulation of Sqstm1 may serve as a protective mechanism. Methods and Results: This study showed that FXR bound to the Sqstm1 gene in both mouse livers and ileums as determined by chromatin immunoprecipitation. In addition, FXR activation enhanced transcriptional activation of Sqstm1 in vitro. However, wild-type mice treated with GW4064, a synthetic FXR ligand, showed that FXR activation induced mRNA and protein expression of Sqstm1/p62 in ileum, but not in liver. Interestingly, FXR-transgenic mice showed induced mRNA expression of Sqstm1 in both liver and ileum compared to wild-type mice. Conclusions: Our current study has identified a novel role of FXR in regulating the expression of p62, a key factor in protein degradation and cell signaling. Regulation of p62 by FXR indicates tissue-specific and gene-dosage effects. Furthermore, FXR-mediated induction of p62 may implicate a protective mechanism of FXR. © 2012 Williams et al

Finger Vein Image Deblurring Using Neighbors-Based Binary-GAN (NB-GAN)

Vein contraction and venous compression typically caused by low temperature and excessive placement pressure can blur the captured finger vein images and severely impair the quality of extracted features. To improve the quality of captured finger vein image, this paper proposes a 26-layer generator network constrained by Neighbors-based Binary Patterns (NBP) texture loss to recover the clear image (guessing the original clear image). Firstly, by analyzing various types and degrees of blurred finger vein images captured in real application scenarios, a method to mathematically model the local and global blurriness using a pair of defocused and mean blur kernels is proposed. By iteratively and alternatively convoluting clear images with both kernels in a multi-scale window, a polymorphic blur training set is constructed for network training. Then, NBP texture loss is used for training the generator to enhance the deblurring ability of the network on images. Lastly, a novel network structure is proposed to retain more vein texture feature information, and two residual connections are added on both sides of the residual module of the 26-layer generator network to prevent degradation and overfitting. Theoretical analysis and simulation results show that the proposed neighbors-based binary-GAN (NB-GAN) can achieve better deblurring performance than the the-state-of-the-art approaches