Optical flow has long dominated motion representation in video by focusing on explicit pixel displacement caused by object or camera movement. In this work, we argue that video also contains a largely overlooked form of motion information, namely latent micro-motion, which arises from subtle, structure-constrained responses of rigid components to physical interaction with the environment. We study this phenomenon in UAV video, a physically grounded setting where onboard structures are continuously exposed to aerodynamic forces. Although such micro-motions are low in amplitude and are often treated as noise or residual vibration, we show that they form a consistent visual signal that becomes observable through structure-aware and temporally aggregated analysis, even when using simple segmentation and coarse motion descriptors. Through an exploratory analysis, we demonstrate that micro-motion patterns exhibit clear structure and respond systematically to changes in wind conditions, with particularly strong sensitivity to wind direction and weaker dependence on wind magnitude in the examined scenarios. These observations suggest that micro-motion constitutes a distinct regime of motion information in video, complementary to explicit displacement, and motivate a broader reconsideration of how motion is represented and exploited in physically grounded multimedia scenarios.

Real-world time-series can be decomposed into multiple interacting frequency components whose amplitudes and phases co-evolve over time. Such coupled dynamics can deform spectral trajectories, leading to pronounced non-stationarity in frequency-domain representations and substantial degradation in long-horizon forecasting performance. However, most existing frequency-domain forecasting methods do not explicitly model amplitude–phase interactions and often implicitly assume globally consistent spectral structures, which limits their ability to capture drifting spectra under non-stationary dynamics. To address this challenge, we propose a novel Amplitude–Phase Reconstruction Network (APRNet), a frequency-domain time-series forecasting framework that models amplitude–phase interactions from temporal and channel-wise perspectives. Specifically, we propose an innovative Amplitude-Phase Global Correlation (APGC) module to capture spectrum-wide amplitude-phase dependencies and derive frequency-wise calibration factors under non-stationary dynamics. The calibrated spectral representations are further reconstructed into the time domain, forming a closed time-frequency reconstruction loop that suppresses spectral drift and mitigates non-stationarity in temporal features. In addition, we propose a novel Discrete Kolmogorov–Arnold Network (D-KAN) to further enhance fine-grained amplitude–phase modeling. By combining discretized nonlinear activations with locally supported B-spline basis functions, D-KAN enables frequency-adaptive piecewise nonlinear modeling, improving sensitivity to localized spectral variations and enhancing high-frequency expressiveness. Extensive experiments verify the superior performance of our APRNet. Our codes are available at:https://github.com/LH325/APRNet.

Temporal Knowledge Graph (TKG) reasoning often involves completing missing factual elements along the timeline. Although existing methods can learn good embeddings for each factual element in quadruples by integrating temporal information, they often fail to infer the evolution of temporal facts. This is mainly because of (1) insufficiently exploring the internal structure and semantic relationships within individual quadruples and (2) inadequately learning a unified representation of the contextual and temporal correlations among different quadruples. To overcome these limitations, we propose a novel Transformer-based reasoning model (dubbed ECEformer) for TKG to learn the Evolutionary Chain of Events (ECE). Specifically, we unfold the neighborhood subgraph of an entity node in chronological order, forming an evolutionary chain of events as the input for our model. Subsequently, we utilize a Transformer encoder to learn the embeddings of intra-quadruples for ECE. We then craft a mixed-context reasoning module based on the multi-layer perceptron (MLP) to learn the unified representations of inter-quadruples for ECE while accomplishing temporal knowledge reasoning. In addition, to enhance the timeliness of the events, we devise an additional time prediction task to complete effective temporal information within the learned unified representation. Extensive experiments on six benchmark datasets verify the state-of-the-art performance and the effectiveness of our method.

Text extraction plays an important role in news images analysis tasks. However, the conglutination of subtitles and station logos makes text detection challenging. In this paper, we develop an effective news text detection framework by introducing a novel similarity embedded proposal mechanism. The main idea is to predict similarity for each fine-scale coarse proposal to help construct text bounding boxes. Specifically, a CNN and bi-directional LSTM based network is used to produce vectors embedded in coarse proposals provided by Connectionist Text Proposal Network (CTPN). Notably, similarity embedded proposal mechanism can be generalized to other sub-text level text detection models. Comparing to the state-of-the-art method (CTPN), our framework improves F-measure by 25.2% on our Private News Dataset and 8.9% on ICDAR 2013 benchmarks, respectively.