arXiv Computer Vision

Technical note on: Zero-Training Feature-Space Alignment via Information Geometry

The paper introduces Zero-Training Fisher Geometry Alignment (ZFGA), a closed‑form test‑time adaptation technique that aligns the Fisher geometry of test‑time feature embeddings with a reference geometry from clean data, improving robustness to covariate shift without altering model parameters. ZFGA is evaluated on CIFAR‑10‑C and ImageNet‑C with ResNet‑50, DINO ViT‑S/16, and CLIP ViT‑B/32, consistently outperforming zero‑shot inference and remaining stable across all three model families. Compared to other training‑free and gradient‑based methods, ZFGA uniquely avoids substantial performance degradation and requires only forward passes and matrix operations at inference time, making it a lightweight, deterministic alternative to optimization‑based test‑time adaptation.

arXiv Computer Vision

Aug 31

SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment

SUFLECA is a weakly supervised framework that improves zero‑shot CAD‑to‑image alignment by scaling geometry‑grounded feature learning using Normalized Object Coordinates across up to 12 real and synthetic datasets. It introduces a geometrically consistent matching algorithm that reliably establishes CAD‑to‑image correspondences, enabling accurate, sub‑second alignment without iterative pose refinement. On the ScanNet25k benchmark, SUFLECA achieves 32.8%/42.6% category/instance accuracy, outperforming the strongest zero‑shot baseline by 9.7/12.5 percentage points and surpassing existing pose‑supervised methods for the first time.

By Saad Ejaz, Miguel Fernandez-Cortizas, Javier Civera, Holger Voos, Jose Luis Sanchez-Lopez

robotics benchmarks safety

arXiv Computer Vision

Sep 3

Self-Geometry: GT-Free and Plug-and-Play Test-Time Adaptation for Geometrically Consistent 3D Vision Foundation Models

The paper introduces Self-Geometry, a plug‑and‑play test‑time adaptation framework that enforces explicit multi‑view geometric constraints on Vision Foundation Models (VFMs) using 2D pixel correspondences as pseudo ground truth. It combines Geometric Disentanglement Optimization—mixing Multi‑View and Epipolar Consistency losses with Gradient Disentanglement—to avoid gradient conflicts, a Frame Angular‑Neighbor sampler based on SO(3) geodesic distances to select informative views, and a Lightweight TTA module that adapts VFMs via LoRA. Experiments on six VFMs and four benchmarks (7Scenes, ETH3D, ScanNet++, HiRoom) show consistent improvements in pose and geometry estimation.

By Seokhyun Youn, Dahyeon Kye, Sung-Ho Bae, Jihyong Oh

fine-tuning benchmarks

arXiv AI

Oct 5

Revisiting Visual Representation Enhancement of VLMs via Kernel Canonical Correlation Analysis

arXiv:2610.02718v1 Announce Type: cross Abstract: Vision-language models such as CLIP exhibit strong semantic generalization, but remain limited in fine-grained visual perception. A recent work named...

By Peilin Yang, Xiaoyu Liu, Jian Sun, Qinghua Tao

llms rag fine-tuning multimodal safety

arXiv AI

Jun 12

GeoWorld-VLM: Geometry from World Models for Vision-Language Models

arXiv:2605. 16713v2 Announce Type: replace-cross Abstract: Modern Vision-Language Models (VLMs) achieve strong semantic recognition, yet remain brittle on elementary spatial relations such as left of, on, behind, and between.

By Renjie Gu, Kaichen Zhou, Yan Luo, Mengyu Wang

llms efficiency multimodal benchmarks safety

Hugging Face Trending Papers

Jul 13

FoundationGeo: Learning Spatial Pixel-Wise Fields for Monocular Metric Geometry

We present FoundationGeo, a two-stage framework that explicitly bridges relative and metric prediction via spatial calibration and principled data design. Stage 1 learns a high-fidelity, affine-invariant geometry model by initializing with DINOv3 and training on a curated 10.

benchmarks safety

arXiv Computer Vision

Oct 7

UniPose9D: Universal Category-Agnostic Object Pose Estimation

arXiv:2607.09985v2 Announce Type: replace Abstract: Object pose estimation is a fundamental problem in 3D vision. Although recent state-of-the-art approaches achieve strong performance, generalizatio...

By Yang You, Yi Du, Cole Harrison, Leonidas Guibas

diffusion computer-vision benchmarks

arXiv AI

Jun 24

Zero-Shot Test-Time Canonicalization using Out-of-Distribution Scoring

arXiv:2606. 24178v1 Announce Type: cross Abstract: Pretrained vision models often misclassify inputs that are rotated, scaled, or sheared, even though these affine transformations leave the object class unchanged.

By Dominik Lindner, Johann Schmidt, Tom Siegl, Martin Becker, Sebastian Stober

benchmarks

arXiv Computer Vision

Oct 1

Depth Anything in $360^\circ$: Towards Scale Invariance in the Wild

arXiv:2512.22819v2 Announce Type: replace Abstract: Panoramic depth estimation captures the complete 360$^\circ$ scene geometry, being essential for robotics and AR/VR applications. While perspective...

By Hualie Jiang, Ziyang Song, Zhiqiang Lou, Rui Xu, Minglang Tan

computer-vision robotics fine-tuning benchmarks

arXiv AI

Aug 28

Subspace Alignment for Vision-Language Model Test-time Adaptation

The paper introduces SubTTA, a test-time adaptation method for vision‑language models that aligns the semantic subspaces of visual and textual modalities to improve zero‑shot predictions. It addresses two issues: the modality gap caused by distribution shifts and visual nuisance that masks task‑specific semantics. By minimizing chordal distance between principal subspaces and projecting visual features onto a task‑specific textual subspace, SubTTA refines decision boundaries and achieves an average 2.24% improvement over existing TTA methods.

By Zhichen Zeng, Wenxuan Bao, Xiao Lin, Ruizhong Qiu, Tianxin Wei, Xuying Ning, Yuchen Yan, Chen Luo, Monica Xiao Cheng, Jingrui He, Hanghang Tong

llms rag multimodal benchmarks safety

arXiv Computer Vision

Sep 3

Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption

The paper introduces UnInfo, a test‑time adaptation method for vision‑language models like CLIP that addresses image corruption—a realistic distribution shift caused by sensor conditions. UnInfo leverages uniformity‑aware confidence maximization, information‑aware loss balancing, and knowledge distillation from an EMA teacher to preserve embedding uniformity and improve zero‑shot classification accuracy. Experiments show that UnInfo outperforms existing TTA methods on corrupted image datasets.

By Kazuki Adachi, Shin'ya Yamaguchi, Tomoki Hamagami

llms rag fine-tuning efficiency multimodal

arXiv Computer Vision

Aug 31

GAAT: Geometry-Aware Alignment Transformer for Multimodal UAV Perception

arXiv:2608.27971v1 Announce Type: new Abstract: Unmanned aerial vehicle (UAV) multimodal perception integrates visible (RGB), infrared (IR), synthetic aperture radar (SAR), and depth sensors for scen...

By Jingpu Yang, Debin Tang, Yilin Sun, Fengxian Ji, Jiahua Zhu, Wenrui Ding, Yufeng Wang

llms nlp multimodal benchmarks safety

Hugging Face Trending Papers

Jun 23

REDI-Match: Rotation-Equivariant Distillation for Efficient and Robust Dense Matching

Vision Foundation Models (VFMs) have significantly advanced dense feature matching, yet severe in-plane rotation remains a critical challenge. Existing solutions face a fundamental dilemma: data-driven methods require inefficient parameter scaling to implicitly learn rotations, whereas strictly equivariant networks lack the semantic capacity of modern VFMs.

efficiency benchmarks safety

Related stories