Skip to content

Category

computer vision

3,315 papers

#machine learning Preprint Sep 2026

Learning from Distributed Eyes: Leveraging Collaborative Perception for Automated Model Adaptation

In autonomous driving, perception models often struggle to generalize to new environments due to domain shifts. While unsupervised model adaptation offers a feasible solution without labor-intensive manual labeling, existing methods that rely solely on the ego-vehicle's data often lead to inferior pseudo-labeling perfo...

Ya-Nan Ma, Yi-Hang Tao, Zheng-Ru Fang et al. · 0 citations
#machine learning Preprint Sep 2026

Beyond Random Couplings: Contrastive Noise Alignment in Generative Flows

This work introduces Contrastive Noise Alignment (CNA), a training-time method that creates dynamic, contrastive couplings by optimizing the noise representations directly, and shows theoretically that this equilibrium asymptotically preserves Gaussian structures, maintaining tractability during inference.

Lennart Wittke, Vinicius C. Azevedo · 0 citations
#machine learning Preprint Sep 2026

MCLC-NET: Multimodal Continual Learning for Leaf Counting

MCLC-NET is proposed, a multimodal continual learning framework for leaf counting that learns tasks sequentially using a memory-based strategy with a memory buffer to retain important samples from previous tasks.

Ruchi Bhatt, Pratibha Kumari, Shreya Bansal et al. · 0 citations
#machine learning Preprint Sep 2026

Beyond Pixel Similarity: Task-Aware Evaluation of GAN-Based Synthetic Sonar Data for Robotic Perception

For the datasets and models considered, pixel-level image-fidelity metrics alone may not consistently capture the task-relevant realism of synthetic sonar observations and motivate the use of task-aware evaluation for synthetic sensor data intended for robotic perception.

Hannan Ejaz Keen, M. Fraz, Karsten Berns · 0 citations
#machine learning Preprint Sep 2026

Not All Layers Need Tuning: Diagnosing and Directing Adaptation in Vision-Language-Action Models

Fine-tuning a Vision-Language-Action (VLA) model for a new deployment environment is expensive, yet most methods apply uniform-capacity adapters to every network region as if every region requires equal adjustment. This paper tests that assumption on five architecturally diverse VLAs (OpenVLA-OFT, $\pi_0$, SmolVLA, DTP...

Shahram Najam Syed, Arthur Jakobsson, Prayuj Sachdev et al. · 0 citations
#machine learning Preprint Sep 2026

vidax: A Unified JAX Framework for Video Generative Models on Accelerator Meshes

Open-source video generative models ship almost exclusively as PyTorch/CUDA reference implementations. This leaves Cloud TPU pods without a production-ready inference path, despite offering large, cost-effective accelerator memory pools ideal for long-sequence spatiotemporal attention. We present vidax, an open-source...

Cong-Yue Deng · 0 citations
#machine learning Preprint Sep 2026

Zing-0.5: Toward Playable Worlds with Real-Time Joint Action and Text Control

We introduce Zing-0.5, a 5B autoregressive world model designed for playability: users can explore generated worlds, influence unfolding events, and respond to the resulting feedback through joint keyboard and online text control. Our approach brings together three technical contributions: (1) Unified action and text c...

Ming-Yang Chen, Sheng Chen, Xiao-Xiao Fu et al. · 2 citations
#machine learning Preprint Open access Sep 2026

Can VLMs Reliably Assess Sidewalk Accessibility Attributes from Pedestrian-Level Imagery?

An important component of urban accessibility, particularly for wheelchair users and people with reduced mobility, is sidewalk compliance with measurable requirements. We test whether effective width, longitudinal slope, cross slope, and pavement condition can be assessed reliably from pedestrian-level imagery using vi...

Seung Jae Lieu, Diego Morra, Chiara Cadoni et al. · 0 citations
#machine learning Preprint Jul 2026

Temperon: Full-Time SAM Quality at a Third Less Wall-Clock

The proposed Temperon is a plain-SGD explorer for the first 43% of the epoch budget, then one scheduled hand-off that gives the entire final cosine anneal to a SAM-wrapped Muon refiner, which matches the best full-time-SAM recipe on accuracy everywhere while reaching the hardest common target on three of the four datas...

Stamatis Mastromichalakis · 0 citations
#artificial intelligence Preprint Aug 2026

Ranking Infrared-Visible Fusion the Way Humans Do: A Learned Pairwise Preference Measure

The Learned Perceptual Image Fusion Measure (LPIFM), among the earliest learned fusion assessors trained directly on dense human A/B/Tie comparisons, provides a scalable instrument for human-aligned fusion assessment, with the preference corpus, model weights, and code publicly available.

Hao-Ran Liu, Ming-Zhe Liu, Peng Li et al. · 0 citations

SegTME-UNI2: A Foundation Model-Based Framework for Generalisable Multiclass Cell Segmentation and LLM-Driven Tumour Microenvironment Characterisation in Histopathology

SegTME-UNI2, a unified framework addressing all three requirements end-to-end of TME profiling and spatial biology research, is presented, with Qualitative validation on IGNITE NSCLC tiles shows the pipeline produces biologically coherent phenotype classifications and narratives despite inter-institutional stain variab...

Wan Siti Halimatul Munirah Wan Ahmad, F. S. Samidi, Mohammad Badal Ahmmed et al. · 0 citations
#artificial intelligence Preprint Open access Sep 2026

Detect Before You Leap: Mirage Detection in Vision-Language Models

Vision-language models (VLMs) can produce confident answers without relevant visual evidence, a failure mode known as mirage reasoning (Asadi et al., 2026). To that end, we study pre-release mirage detection: deciding whether a VLM answer should be released or withheld. Our model-agnostic method, Text-Conditioned Layer...

Md. Shaown Miah, S. M. Taiabul Haque, Syed Ishtiaque Ahmed et al. · 0 citations

From tech blogs

See all →
Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.  The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.