Long-horizon future-frame prediction is important for autonomous driving, traffic surveillance, and intelligent transportation systems, yet remains challenging due to temporal ghosting, geometry drift, and inconsistent object motion. Recent latent video diffusion models have achieved impressive visual quality, but directly applying... Long-horizon future-frame prediction is important for autonomous driving, traffic surveillance, and intelligent transportation systems, yet remains challenging due to temporal ghosting, geometry drift, and inconsistent object motion. Recent latent video diffusion models have achieved impressive visual quality, but directly applying them to structured traffic scenes often leads to unstable geometry and degraded temporal coherence over extended horizons. We present a training-free inference framework that stabilizes reliable static structure in pretrained video predictions through multi-frame temporal context and view-conditioned routing. For front-camera videos, our method refines generated futures with a multi-frame depth-layered renderer that projects static geometry from observed history frames while preserving dynamic regions from the generative base model. For heterogeneous traffic views, a frozen vision-language model infers a coarse camera group from the observed clip and selects a specialized motion-based predictor. The framework requires neither retraining nor fine-tuning of the underlying video model and can be applied directly to pretrained generators. We validate the proposed framework on the AI City Challenge Track 5 benchmark, where our final system achieves competitive performance among the top-ranked teams. These results demonstrate that geometry-aware inference-time refinement and view-conditioned hybrid inference can improve static-geometry stability and low-level structural fidelity without changing the original model architecture.

Text-based person anomaly search requires retrieving real-world pedestrian images from detailed natural-language descriptions using models trained primarily on synthetic data. This Sim2Real setting is particularly challenging because visually similar candidates may differ only in subtle actions, object interactions, or appearance... Text-based person anomaly search requires retrieving real-world pedestrian images from detailed natural-language descriptions using models trained primarily on synthetic data. This Sim2Real setting is particularly challenging because visually similar candidates may differ only in subtle actions, object interactions, or appearance attributes, while applying multimodal large language models to the entire gallery is computationally expensive. We propose an anchor-constrained coarse-to-fine retrieval framework that combines global semantic matching with fine-grained verification. First, each query is represented by its original caption, a structured concatenation, and several semantic facets. Heterogeneous vision-language retrievers are then integrated through robust per-query score calibration and soft claim-aware fusion. Full and concatenated captions serve as anchors to preserve candidate recall, whereas appearance, action, and object facets provide bounded corrective evidence. The resulting candidate pool is further refined by a discriminative Qwen3 reranker and two complementary semantic verification modules based on anomaly-aware cloze completion and multi-agent evidence reasoning. Finally, an uncertainty-gated consensus module adaptively reweights the three experts on ambiguous queries. Experiments on the PAB benchmark show that the proposed soft claim-aware retrieval achieves 86.44% mAP@10, substantially outperforming individual retrieval backbones. The complete framework further improves performance to 95.41% mAP@10, 94.44% R@1, and 99.09% R@5. These results demonstrate that preserving strong global retrieval while restricting expensive semantic reasoning to a small candidate pool is effective for fine-grained Sim2Real person anomaly search. Our code will be available on Github.

Real-time vehicle detection often requires trading off accuracy for speed. To validate a solution that excels on both fronts, we adopt fisheye imagery, a domain where extreme radial distortion and scale variation defeat standard detectors, as a rigorous testbed. Our... Real-time vehicle detection often requires trading off accuracy for speed. To validate a solution that excels on both fronts, we adopt fisheye imagery, a domain where extreme radial distortion and scale variation defeat standard detectors, as a rigorous testbed. Our pipeline comprises three key stages: (1) Multi-stage DETR Distillation, a four-phase knowledge transfer leveraging KD-DETR's fixed distillation queries with separate head- and feature-level stages to avoid gradient conflicts and ensure progressive learning; (2) Data-centric Enhancements, creating a diverse training pool via Co-DETR pseudo-labeling, CycleGAN-Turbo day-to-night style transfer, and object-level flash/blur augmentations; and (3) Adaptive Sample Mining, which dynamically upsamples complex examples to sharpen the model's focus. When paired with D-FINE-M, our method achieves an F1 score of 0.6318 at 145 FPS on the AI City Challenge 2024 Track 4 test set, and with D-FINE-N, it reaches 781 FPS with an F1-score of 0.5597, all measured on an RTX 4090. Evaluated on the challenging FishEye8K benchmark of the 9th AI City Challenge, our approach delivers strong accuracy while maintaining real-time FPS. By ignoring fisheye distortions and treating them as a domain-agnostic stress test, we demonstrate that this data-centric, multi-stage distillation framework generalizes seamlessly to standard vehicle and broader object detection tasks, offering a unified solution for high-precision, real-time vision. Our implemented code: https://github.com/NhatHuy1812/VGU_VLinsight_AICITY2025_Track4

Quality of service (QoS) measuring in mobile broadband networks has become a significant issue as a result of the rising awareness of QoS. This paper examines the quality of 4G Internet access at different locations inside Vietnamese-German University campus (VGU),... Quality of service (QoS) measuring in mobile broadband networks has become a significant issue as a result of the rising awareness of QoS. This paper examines the quality of 4G Internet access at different locations inside Vietnamese-German University campus (VGU), in Vietnam, focusing on five key performance indicators (KPI) which are distinct by two mobile network operators (MNO) in Vietnam. To the best of our knowledge, there are many studies on evaluating quality of service (QoS) of mobile network signals, however, there is no extensive research on exploring 4G wireless mobile network at specific positions inside buildings. This paper provides radio frequency (RF) measurements and evaluation, with the experiments took place both indoors and outdoors and determines those operators performance based on stimulation results. Data was gathered from a walk test using QualiPoc installed in a smartphone and analyzed by a ROMES4 application. The study suggests that 4G signal quality is rather good but downloading speed is quite slow at ground level, and the higher building level the user goes, the worse the signal quality gets. Finally, the research group propose solution to enhance quality of the network quality inside VGU campus.