arXiv:2608.29577v1 Announce Type: new Abstract: Traditional video highlight detection relies on a narrow, event-centric definition of saliency, which often fails to generalize to unconstrained person...
arXiv:2609.31654v2 Announce Type: replace-cross Abstract: Video diffusion transformers depend on temporal attention to coordinate information across frames, yet nearly everything known about this mec...
GazeFlow is a new framework for egocentric gaze prediction that models gaze as a joint distribution of temporal positions conditioned on both top‑down task cues and bottom‑up visual saliency. It employs conditional flow matching to iteratively transform Gaussian noise into realistic gaze trajectories, using a velocity field informed by video‑encoded visual features and global task queries. On standard benchmarks, GazeFlow outperforms existing methods on per‑frame metrics and produces trajectories that better reflect human gaze dynamics.
OpenVAM is a new framework for visual attention modeling that combines a dense saliency map with language‑based explanations. It uses a decoupled design: a visual pathway for precise localization and a vision‑language head that generates grounded what/why explanations. The method is trained in three stages to preserve localization while adding language grounding, and a scalable pipeline creates multi‑domain annotations for evaluation.
OpenSAL360 is an open‑source platform that enables scalable, low‑cost collection of 360° video saliency data using only a standard screen, mouse, and internet connection. It bypasses the need for VR headsets, allowing parallel data collection from crowdsourced assessors. The authors validated the protocol against seven VR eye‑tracking datasets, performed ablation studies, and released a new dataset of 500 omnidirectional videos annotated by over 2,000 assessors, the largest in the field to date.
The paper introduces Target Saliency Boosting, a new task that enhances the visual prominence of a specific object in text-to-image generation without visual priors. It proposes GazeME, a lightweight framework that inserts learnable marker tokens around object descriptions to indicate which objects to emphasize or suppress. By building a saliency-semantics dataset and using Saliency Prior Marker Activation, GazeME learns to adjust markers during training and automatically applies them at inference, effectively boosting target saliency while maintaining semantic alignment and image quality.
High-Fidelity Video Quality Assessment (HFVQA) is a new framework that uses fixed-size spatio‑temporal patches across multiple scales, including the original resolution, to preserve low‑level quality cues and semantic context. It incorporates a lightweight auxiliary network that learns VQA‑specific saliency directly from quality supervision, enabling the model to focus on the most important spatio‑temporal regions. By combining high‑fidelity cues with task‑specific saliency, HFVQA achieves state‑of‑the‑art performance on standard no‑reference VQA benchmarks while processing only about 12% of the candidate patches, making it computationally efficient.
arXiv:2610.00922v1 Announce Type: new Abstract: Gaze estimation under natural head-eye motion underpins applications from driver monitoring to human-computer interaction. Single-frame methods predict...
arXiv:2606. 02522v1 Announce Type: cross Abstract: Video multimodal large language models (MLLMs) have made rapid progress on general and long-form video understanding, yet their ability to preserve brief answer-critical visual evidence remains underexplored.
arXiv:2607. 21353v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of specific training data while preserving model utility.
arXiv:2609.09528v1 Announce Type: new Abstract: Video large language models (Video-LLMs) are increasingly used as the perceptual front end of world models, a role that assumes they can read motion: h...
arXiv:2607. 14711v1 Announce Type: cross Abstract: We present for video understanding (classification) a split space-time attention model, VideoSEMA, consisting of a scalable and efficient Mamba-like attention (SEMA) block in space and a softmax temporal attention in time.