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
arXiv:2606. 14703v1 Announce Type: cross Abstract: How a vision-language model internally solves the task of describing an image is far from obvious.
By Rohit Gandikota, David Bau
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.
By Sheng Zhao, Weikai Lin, Yuhao Zhu
Gaze Attention is a new mechanism for multimodal large language models that selectively focuses on relevant visual regions during each generation step, rather than attending to all visual tokens. By grouping tokens into spatial regions and using learnable context tokens to retain global information, it reduces attention computation and visual key‑value entries by up to 90%. Experiments on 13 image and 6 video benchmarks show that Gaze Attention matches or outperforms dense‑attention baselines while using fewer visual resources.
By Junha Song, Byeongho Heo, Geonmo Gu, Jaegul Choo, Dongyoon Han, Sangdoo Yun
arXiv:2603.26945v2 Announce Type: replace
Abstract: Appearance-based gaze estimation (AGE) has achieved remarkable performance in constrained settings, yet we reveal a significant generalization gap...
By Zhenhao Li, Zheng Liu, Seunghyun Lee, Amin Fadaeinejad, Yuanhao Yu
The paper investigates saliency-guided data augmentation for low-resource handwritten character recognition, proposing that saliency maps can be used to design augmentation variants that perturb, preserve, or constrain visually informative regions. It introduces the Explainability Consistency Index (ECI) to measure saliency stability under perturbations and compares classical transformations, random lacunae, and saliency-constrained lacunae. Results show that saliency-constrained augmentation can improve recognition and explanation consistency, but the optimal strategy depends on architecture and corpus, suggesting saliency should be used as a control mechanism rather than a fixed augmentation rule.
By Paraskevi Platanou, John Pavlopoulos
Understanding how supervision signals shape semantic visual representations remains challenging, particularly in fine-grained, low-data settings. Standard evaluations of vision models rely on downstre...