APEX is a new image quality assessment framework that eliminates assumptions by using the Sliced Wasserstein Distance as a similarity measure. It is embedding‑agnostic, scalable to high‑dimensional spaces, and employs open‑vocabulary models CLIP and DINOv2 for feature extraction. Benchmarks show that APEX is more robust to visual degradations and remains stable across different datasets, including out‑of‑domain ones.
By Caterina Gallegati, Monica Bianchini, Franco Scarselli, Vittorio Murino, Barbara Toniella Corradini
The paper reviews 50 image augmentation and generation techniques, categorizing them into ten groups, and conducts a large‑scale empirical study to assess their effectiveness as test generators for embedding‑based image retrieval systems. Using Amazon Titan and OpenCLIP embeddings, the authors evaluate the techniques across four dimensions—embedding‑space similarity, embedding uncertainty, semantic realism, and retrieval failure rate—on CIFAR‑10, ImageNet‑1K, and an industrial dataset. Results show that weather simulation and SaSPA yield the highest uncertainty and failure rates while maintaining realistic visuals, whereas GAN‑based methods produce low realism due to synthetic artifacts.
By Yehan De Silva, Anirudh Sridhar, Armin Lotfy, Nafiseh Kahani, Yvan Labiche, Ziyu Wang, Frank Ouyang, Clare Carty, Azalia Shamsaei
arXiv:2603.08064v3 Announce Type: replace
Abstract: Most evaluations of generative models rely on feature-distribution metrics such as FID, which operate on continuous recognition features that are e...
By Zexi Jia, Pengcheng Luo, Yijia Zhong, Jinchao Zhang, Jie Zhou
arXiv:2608. 04234v1 Announce Type: cross Abstract: We study the problem of aligning data from multiple modalities into a shared representation space, focusing on settings where strong pretrained unimodal encoders are available but cross-modal paired data are scarce.
By Yixuan Florence Wu, Yilun Zhu, Naichen Shi
arXiv:2610.05518v1 Announce Type: cross
Abstract: The Fr\'echet distance is a de facto standard for evaluating generative models across domains, appearing as FID for images and FVD for videos. It sum...
By Yunghee Lee, Jaeyeon Kim
The paper introduces SADGE, a quantitative similarity metric that predicts how well synthetic image datasets will perform on real-world computer vision tasks without requiring downstream model training. SADGE combines appearance and geometric similarity measures through a constrained bilinear interaction, outperforming appearance-only and geometry-only baselines across 79,000 image pairs in five synthetic-to-real benchmark families. The study demonstrates that the non‑linear interplay between appearance and geometry, rather than either alone, best predicts downstream performance in tasks such as object detection, semantic segmentation, and pose estimation.
By Patryk Bartkowiak, Dominik Michels, Soren Pirk, Wojtek Palubicki
The paper introduces a divergence-based similarity function (DSF) for multi-view contrastive learning, representing each set of augmented views as a distribution and measuring similarity via distribution divergence. DSF captures joint structure across all views, outperforming prior pairwise methods on tasks such as kNN classification, linear evaluation, transfer learning, and distribution shift. It also offers greater efficiency and eliminates the need for a temperature hyperparameter, unlike cosine similarity.
By Jaehyoung Jeon, Cheolsu Lim, Myungjoo Kang
arXiv:2409. 10094v3 Announce Type: replace-cross Abstract: Out-of-Distribution (OoD) detection aims to justify whether a given sample is from the training distribution of the classifier-under-protection, i.
By Kun Fang, Zuopeng Yang, Haibo Hu, Xiaolin Huang, Jie Yang, Qinghua Tao
arXiv:2608. 18339v1 Announce Type: cross Abstract: Vision-language models (VLMs) have demonstrated remarkable zero-shot capabilities yet remain sensitive to real-world distribution shifts during inference.
By Qi Yu, Zhichen Zeng, Katherine Tieu, Xiyuan Yang, Ruizhong Qiu, Yuchen Yan, Lihui Liu, Yanjun Zhao, Lingjie Chen, Jingrui He, Hanghang Tong
arXiv:2604. 11539v2 Announce Type: replace-cross Abstract: Human perception of visual similarity is inherently adaptive and subjective, depending on the users' interests and focus.
By Sohwi Lim, Lee Hyoseok, Jungjoon Park, Tae-Hyun Oh
The paper introduces LOT Wassmap, an algorithm designed to uncover low‑dimensional structures in the Wasserstein space without computing a full pairwise distance matrix. It leverages approximation schemes such as Sinkhorn distances and linearized optimal transport to accelerate computations, and provides theoretical guarantees on embedding quality even when only finite samples are available. Experiments show that LOT Wassmap achieves accurate embeddings that improve with larger sample sizes while significantly reducing computational cost compared to traditional pairwise‑distance‑based methods.
By Alexander Cloninger, Keaton Hamm, Varun Khurana, Caroline Moosm\"uller
The paper introduces Inverted Contrastive Learning for Unsupervised Feature Selection (ICLFS), a method that treats each feature as a sample by inverting the data matrix and applies a contrastive learning framework to learn consistent representations across masked positive views and a shuffled negative view. Feature saliency is derived from the magnitude of projector‑space embeddings, and a Laplacian‑Gated Ranking Correction step refines the ranking by reducing local redundancy. Experiments on 12 benchmark datasets show that ICLFS achieves the best clustering accuracy on 10 datasets compared to both classical and neural baselines, demonstrating the effectiveness of feature‑wise contrastive consistency for unsupervised feature selection.
By Utsab Ghosh, Roshni Chakraborty