Motion-state Alignment for Video Semantic Segmentation

In recent years, video semantic segmentation has made great progress with advanced deep neural networks. However, there still exist two main challenges \ie, information inconsistency and computation cost. To deal with the two difficulties, we propose a novel motion-state alignment framework for video semantic segmentation to keep both motion and state consistency. In the framework, we first construct a motion alignment branch armed with an efficient decoupled transformer to capture dynamic semantics, guaranteeing region-level temporal consistency. Then, a state alignment branch composed of a stage transformer is designed to enrich feature spaces for the current frame to extract static semantics and achieve pixel-level state consistency. Next, by a semantic assignment mechanism, the region descriptor of each semantic category is gained from dynamic semantics and linked with pixel descriptors from static semantics. Benefiting from the alignment of these two kinds of effective information, the proposed method picks up dynamic and static semantics in a targeted way, so that video semantic regions are consistently segmented to obtain precise locations with low computational complexity. Extensive experiments on Cityscapes and CamVid datasets show that the proposed approach outperforms state-of-the-art methods and validates the effectiveness of the motion-state alignment framework.Comment: Accepted by CVPR Workshops 202

Perceive, Excavate and Purify: A Novel Object Mining Framework for Instance Segmentation

Recently, instance segmentation has made great progress with the rapid development of deep neural networks. However, there still exist two main challenges including discovering indistinguishable objects and modeling the relationship between instances. To deal with these difficulties, we propose a novel object mining framework for instance segmentation. In this framework, we first introduce the semantics perceiving subnetwork to capture pixels that may belong to an obvious instance from the bottom up. Then, we propose an object excavating mechanism to discover indistinguishable objects. In the mechanism, preliminary perceived semantics are regarded as original instances with classifications and locations, and then indistinguishable objects around these original instances are mined, which ensures that hard objects are fully excavated. Next, an instance purifying strategy is put forward to model the relationship between instances, which pulls the similar instances close and pushes away different instances to keep intra-instance similarity and inter-instance discrimination. In this manner, the same objects are combined as the one instance and different objects are distinguished as independent instances. Extensive experiments on the COCO dataset show that the proposed approach outperforms state-of-the-art methods, which validates the effectiveness of the proposed object mining framework.Comment: Accepted by CVPR Workshops 202

Revisiting Non-Autoregressive Translation at Scale

In real-world systems, scaling has been critical for improving the translation quality in autoregressive translation (AT), which however has not been well studied for non-autoregressive translation (NAT). In this work, we bridge the gap by systematically studying the impact of scaling on NAT behaviors. Extensive experiments on six WMT benchmarks over two advanced NAT models show that scaling can alleviate the commonly-cited weaknesses of NAT models, resulting in better translation performance. To reduce the side-effect of scaling on decoding speed, we empirically investigate the impact of NAT encoder and decoder on the translation performance. Experimental results on the large-scale WMT20 En-De show that the asymmetric architecture (e.g. bigger encoder and smaller decoder) can achieve comparable performance with the scaling model, while maintaining the superiority of decoding speed with standard NAT models. To this end, we establish a new benchmark by validating scaled NAT models on the scaled dataset, which can be regarded as a strong baseline for future works. We release code and system outputs at https://github.com/DeepLearnXMU/Scaling4NAT.Comment: 13 pages, Findings of ACL 202

Changes in interleukin-27 levels in patients with acute coronary syndrome and their clinical significance

Background This study evaluated changes in interleukin (IL)-27 levels in patients with acute coronary syndrome (ACS) and their influence on Th1, Th2, and Th17 cells. Methods Serum levels of IL-27, IL-4, IL-17, and interferon (IFN)-γ in healthy subjects as well as patients with ACS, including stable angina pectoris (SA), unstable angina pectoris (UA), and acute myocardial infarction (AMI), were determined using an enzyme-linked immunosorbent assay. The proportions of Th1, Th2, and Th17 cells among peripheral blood mononuclear cells (PBMCs), were measured using flow cytometry, after incubation with phorbol myristate acetate (PMA) for 4 h. The proportions of Th1 and Th17 cells among PBMCs in AMI and UA were detected after stimulation with IL-27 or PMA + IL-27 for 4, 8, and 12 h. Results Serum levels of IL-27 in patients with AMI and UA were significantly lower than those in SA and control groups, while serum levels of IL-17 and IFN-γ in AMI and UA groups were dramatically increased compared to those in SA and healthy control groups. However, there were no statistically significant differences in serum IL-4. The proportions of Th1 and Th17 cells among PBMCs were statistically significantly higher in the AMI and UA groups than those in the SA and control groups, while there was no statistically significant difference in the proportion of Th2 cells among different groups. For patients with AMI and UA, the effect of co-stimulation of PBMCs with PMA and IL-27 was not significantly different from that of PMA single stimulation, while PMA + IL-27 co-stimulation lowered the Th17 cell proportion significantly compared to PMA single stimulation. Discussion Compared to SA patients and healthy controls, patients with ACS (AMI + UA) had lower serum levels of IL-27 and higher proportions of PBMC Th1 and Th17 cells, which could be attributed to the inhibitory effects of IL-27 on the proliferation of Th17 cells. These results indicated that IL-27 could be a novel therapeutic target in ACS patients

Metallic surface states in a correlated d-electron topological Kondo insulator candidate FeSb2

The resistance of a conventional insulator diverges as temperature approaches zero. The peculiar low temperature resistivity saturation in the 4f Kondo insulator (KI) SmB6 has spurred proposals of a correlation-driven topological Kondo insulator (TKI) with exotic ground states. However, the scarcity of model TKI material families leaves difficulties in disentangling key ingredients from irrelevant details. Here we use angle-resolved photoemission spectroscopy (ARPES) to study FeSb2, a correlated d-electron KI candidate that also exhibits a low temperature resistivity saturation. On the (010) surface, we find a rich assemblage of metallic states with two-dimensional dispersion. Measurements of the bulk band structure reveal band renormalization, a large temperature-dependent band shift, and flat spectral features along certain high symmetry directions, providing spectroscopic evidence for strong correlations. Our observations suggest that exotic insulating states resembling those in SmB6 and YbB12 may also exist in systems with d instead of f electrons

Sufficient Conditions for a Graph to Be ℓ-Connected, ℓ-Deficient, ℓ-Hamiltonian and ℓ−-Independent in Terms of the Forgotten Topological Index

The forgotten topological index of a (molecule) graph is the sum of cubes of all its vertex degrees, which plays a significant role in measuring the branching of the carbon atom skeleton. It is meaningful and difficult to explore sufficient conditions for a given graph keeping certain properties in graph theory. In this paper, we mainly explore sufficient conditions in terms of the forgotten topological index for a graph to be ℓ-connected, ℓ-deficient, ℓ-Hamiltonian and ℓ−-independent, respectively. The conditions cannot be dropped

Sharp bounds for the general Randić index of graphs with fixed number of vertices and cyclomatic number

The cyclomatic number, denoted by $\gamma$, of a graph $G$ is the minimum number of edges of $G$ whose removal makes $G$ acyclic. Let $\mathscr{G}_{n}^{\gamma}$ be the class of all connected graphs with order $n$ and cyclomatic number $\gamma$. In this paper, we characterized the graphs in $\mathscr{G}_{n}^{\gamma}$ with minimum general Randić index for $\gamma\geq 3$ and $1\leq\alpha\leq \frac{39}{25}$. These extend the main result proved by A. Ali, K. C. Das and S. Akhter in 2022. The elements of $\mathscr{G}_{n}^{\gamma}$ with maximum general Randić index were also completely determined for $\gamma\geq 3$ and $\alpha\geq 1$