Multi-Granularity Aggregation with Spatiotemporal Consistency for Video-Based Person Re-Identification

Video-based person re-identification (ReID) aims to exploit relevant features from spatial and temporal knowledge. Widely used methods include the part- and attention-based approaches for suppressing irrelevant spatial-temporal features. However, it is still challenging to overcome inconsistencies across video frames due to occlusion and imperfect detection. These mismatches make temporal processing ineffective and create an imbalance of crucial spatial information. To address these problems, we propose the Spatiotemporal Multi-Granularity Aggregation (ST-MGA) method, which is specifically designed to accumulate relevant features with spatiotemporally consistent cues. The proposed framework consists of three main stages: extraction, which extracts spatiotemporally consistent partial information; augmentation, which augments the partial information with different granularity levels; and aggregation, which effectively aggregates the augmented spatiotemporal information. We first introduce the consistent part-attention (CPA) module, which extracts spatiotemporally consistent and well-aligned attentive parts. Sub-parts derived from CPA provide temporally consistent semantic information, solving misalignment problems in videos due to occlusion or inaccurate detection, and maximize the efficiency of aggregation through uniform partial information. To enhance the diversity of spatial and temporal cues, we introduce the Multi-Attention Part Augmentation (MA-PA) block, which incorporates fine parts at various granular levels, and the Long-/Short-term Temporal Augmentation (LS-TA) block, designed to capture both long- and short-term temporal relations. Using densely separated part cues, ST-MGA fully exploits and aggregates the spatiotemporal multi-granular patterns by comparing relations between parts and scales. In the experiments, the proposed ST-MGA renders state-of-the-art performance on several video-based ReID benchmarks (i.e., MARS, DukeMTMC-VideoReID, and LS-VID).

A Study on Wheel Member Condition Recognition Using Machine Learning (Support Vector Machine)

The wheels of railway vehicles are of paramount importance in relation to railroad operations and safety. Currently, the management of railway vehicle wheels is restricted to post-event inspections of the wheels whenever physical phenomena, such as abnormal vibrations and noise, occur during the operation of railway vehicles. To address this issue, this paper proposes a method for predicting abnormalities in railway wheels in advance and enhancing the learning and prediction performance of machine learning algorithms. Data were collected during the operation of Line 4 of the Busan Metro in South Korea by directly attaching sensors to the railway vehicles. Through the analysis of key factors in the collected data, factors that can be used for tire condition classification were derived. Additionally, through data distribution analysis and correlation analysis, factors for classifying tire conditions were identified. As a result, it was determined that the z-axis of acceleration has a significant impact, and machine learning techniques such as SVM (Linear Kernel, RBF Kernel) and Random Forest were utilized based on acceleration data to classify tire conditions into in-service and defective states. The SVM (Linear Kernel) yielded the highest recognition rate at 98.70%.

An Efficient Approach Using Knowledge Distillation Methods to Stabilize Performance in a Lightweight Top-Down Posture Estimation Network

Multi-person pose estimation has been gaining considerable interest due to its use in several real-world applications, such as activity recognition, motion capture, and augmented reality. Although the improvement of the accuracy and speed of multi-person pose estimation techniques has been recently studied, limitations still exist in balancing these two aspects. In this paper, a novel knowledge distilled lightweight top-down pose network (KDLPN) is proposed that balances computational complexity and accuracy. For the first time in multi-person pose estimation, a network that reduces computational complexity by applying a "Pelee" structure and shuffles pixels in the dense upsampling convolution layer to reduce the number of channels is presented. Furthermore, to prevent performance degradation because of the reduced computational complexity, knowledge distillation is applied to establish the pose estimation network as a teacher network. The method performance is evaluated on the MSCOCO dataset. Experimental results demonstrate that our KDLPN network significantly reduces 95% of the parameters required by state-of-the-art methods with minimal performance degradation. Moreover, our method is compared with other pose estimation methods to substantiate the importance of computational complexity reduction and its effectiveness.

Synergistic antinociception between zaprinast and morphine in the spinal cord of rats on the formalin test

BACKGROUND AND OBJECTIVE

The cyclic guanosine monophosphate level, which causes an antinociception, is increased in cells as a direct result of phosphodiesterase inhibition. This study used a nociceptive test to examine the nature of the pharmacological interaction between intrathecal zaprinast, a phosphodiesterase inhibitor, and morphine.

METHODS

Catheters were inserted into the intrathecal space through an incision in the atlantooccipital membrane of male Sprague-Dawley rats. As a nociceptive model, 50 microL of a 5% formalin solution was injected into the hind paw. After observing the effect of zaprinast (37, 111, 369 nmol) and morphine (1, 4, 10, 40 nmol) alone, the interactions of their combination were examined by an isobolographic analysis.

RESULTS

Intrathecal zaprinast (P < 0.05) and morphine (P < 0.05) dose-dependently suppressed the flinching observed during phase 1 and phase 2 in the formalin test. The ED50 values (95% confidence intervals) of zaprinast and morphine in phase 1 were 161.9 (87.9-298.3) and 11.6 nmol (4.8-27.9 nmol), respectively. The phase 2 ED50 values (95% confidence intervals) of zaprinast and morphine were 229.9 (142.5-370.9) and 3.9 nmol (1.9-7.6 nmol), respectively. Isobolographic analysis revealed a synergistic interaction after intrathecal delivery a zaprinast-morphine mixture in both phases. The ED50 values of (95% confidence intervals) zaprinast in the combination of zaprinast with morphine in phase 1 and phase 2 were 14.2 (4.9-40.6) and 10.4 nmol (3-35.9 nmol), respectively.

CONCLUSIONS

Intrathecal zaprinast and morphine are effective against acute pain and facilitated pain state. Zaprinast interacts synergistically with morphine.