Video-Text Temporal Localization via Multi-Scale Convolution and Dynamic Routing

Cross-Modal Temporal Alignment for Action Grounding in Videos

A cross-modal temporal alignment framework that combines a multi-scale temporal convolutional encoder with capsule-based dynamic routing, jointly optimizing temporal boundary prediction, cross-modal semantic alignment, and capsule diversity is proposed.

ViTAL-X: Video-Text Alignment with Cross-Modal Temporal Edits

Video-text models adapted from image-text architectures (e.g., CLIP) frequently exhibit temporal blindness, the inability to perceive fundamental cues like order, direction, and motion dynamics. Standard datasets mask this limitation by enabling models to exploit static spatial shortcuts. To systematically evaluate this, we introduce XTE-Bench, a diagnostic probe revealing that even large-scale video-language models struggle with basic temporal reasoning, indicating that parameter scaling alone is insufficient to resolve this flaw. To address this, we propose Cross-Modal Temporal Edits (XTE), a self-supervised framework that injects precise temporal supervision. By performing synchronized video-text transformations, XTE generates hard temporal negatives without manual annotation. We instantiate this with ViTAL-X, a lightweight model that equips frozen image-text backbones with temporal awareness while preserving their foundational spatial knowledge. Across six temporal benchmarks, ViTAL-X achieves state-of-the-art performance. Utilizing only 0.4B parameters and 1M training clips, ViTAL-X outperforms 7B-parameter models and surpasses baselines trained on 600x more data. These results demonstrate that targeted, high-quality temporal alignment provides a highly efficient alternative to pure scaling.

DiaVTG: multi-turn reasoning framework for video temporal grounding

Towards an Efficient and Unified Strategy for Video Understanding Applications

Understanding long-range videos remains a key challenge in computer vision due to high temporal redundancy and computational burden. Despite strong performance of recent models, they are constrained in terms of scalability and generalization when applied to longer video sequences. In this work, we present Keyframe-based Spatio-Temporal Adaptive Representation (K-STAR), a redundancy-aware video summarization framework designed to generate compact and semantically rich representations that are effective in downstream tasks. The proposed method jointly models appearance and motion cues while filtering redundant frames. Importantly, it preserves critical temporal transitions while significantly reducing the number of processed frames. Additionally, each key frame is encoded using object, scene, and background-aware prompts, enabling richer semantic representation. Evaluated on the UCF-101 dataset, K-STAR achieves Top-1 accuracy of 93.06% and Top-5 accuracy of $\mathbf{9 8. 7 3 \%}$, with $\mathbf{5 6} \times$ frame reduction and $\mathbf{1 1. 5} \times$ faster inference, demonstrating competitive performance with substantially improved efficiency.

Long-Horizon Video Generation with Temporally Consistent Diffusion and Scene-Graph Guidance

A novel framework that integrates temporally consistent diffusion models with dynamic scene-graph guidance that structurally constrains the generative process, ensuring that objects, their attributes, and their interrelationships remain stable over extended durations is introduced.

Explicit temporal inductive biases for video moment retrieval via multi-scale Gaussian priors

Video moment retrieval aims to localize video segments corresponding to natural language queries. While Transformer models are widely applied, their localization performance declines when processing diverse temporal scales. This limitation arises from the dependence on implicitly learned decoder queries. Without explicit temporal grounding, these unstructured embeddings lead the decoder to perform a global search across the full video sequence. We propose Gaussian-DETR to address this by introducing explicit temporal awareness into the decoding process. Our framework replaces unstructured embeddings with queries constructed from multi-scale Gaussian temporal priors, parameterized by a learnable center and scale. To adapt these priors to the input, we design a multi-scale Gaussian pooling mechanism that extracts local features from the encoder for query initialization, introducing a temporal inductive bias while maintaining the architectural efficiency of DETR. On the QVHighlights benchmark, Gaussian-DETR provides a scale-specific benefit for long-event retrieval, improving Long mAP by +1.98 points over Moment-DETR with statistical support (p = 0.0111), while the differences in overall recall are statistically inconclusive. Ablations suggest that the Gaussian prior improves scale-aware query initialization by balancing long-event coverage and local temporal specificity. This mechanism provides a parameter-efficient component for studying scale-aware query initialization in DETR-like video moment retrieval models.