The paper introduces SalTempto, a new video saliency benchmark featuring 224 highly dynamic clips from HACS‑Segments, each with event context and gaze data from up to 16 subjects. It shows that static baselines perform poorly on SalTempto compared to LEDOV, while fine‑tuned temporal models achieve significant gains, yet still leave a large portion of explainable gaze information unaccounted for. The dataset highlights the need for better temporal modeling in video saliency research.
By Susmit Agrawal, Rebecca Wanner, Juliane Verwiebe, Matthias Tangemann, Matthias Bethge, Matthias K\"ummerer
arXiv:2602. 14834v2 Announce Type: replace-cross Abstract: Human eye movements in visual recognition reflect a balance between foveal sampling and peripheral context.
By Pengcheng Pan, Yonekura Shogo, Yasuo Kuniyosh
The paper introduces GazeCLIP, a gaze‑guided CLIP framework that uses adaptive‑enhanced fine‑grained language prompts for deepfake attribution and detection (DFAD). It presents a new benchmark evaluating DFAD on novel generative methods such as diffusion and flow models, and proposes a gaze‑aware image encoder (GIE) that fuses gaze prompts with image embeddings to capture global forgery patterns. A language refinement encoder (LRE) further refines language embeddings for precise vision‑language matching, achieving state‑of‑the‑art performance with 6.56% higher accuracy and 5.32% higher AUC on the benchmark.
By Yaning Zhang, Linlin Shen, Zitong Yu, Chunjie Ma, Zan Gao
arXiv:2610.28051v1 Announce Type: cross
Abstract: Understanding how supervision signals shape semantic visual representations remains challenging, particularly in fine-grained, low-data settings. Sta...
By Md Raisul Kibria, S\'ebastien Lafond
arXiv:2211.11062v2 Announce Type: replace
Abstract: Gaze following aims to predict where a person is looking in a scene, by predicting the target location, or indicating that the target is located ou...
By Qiaomu Miao, Minh Hoai, Dimitris Samaras
arXiv:2609.39635v1 Announce Type: new
Abstract: Given a target dataset, such as faces with eyeglasses, and a background dataset, such as faces without, contrastive analysis separates \textit{salient}...
By Shuang Liang, Lejun Liao, Shiyuan Zhang, Max C. Zhang, Xiaolong Luo, Han Wang, Stefano Anzellotti, Yuan Yuan