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computer vision

3,080 papers

#artificial intelligence Preprint Feb 2026

VLANeXt Family: A Systematic Study of VLA Models from Core Recipes to Emerging Paradigms

This study systematically dissects design choices along three dimensions: foundational components, perception essentials, and action modeling perspectives and distill 12 key findings that together form a practical recipe for building strong VLA models, resulting in a simple yet effective model, VLANeXt.

Xiao-Ming Wu, Kang Liao, Yi-Hang Luo et al. · 12 citations · ⚡3
#artificial intelligence Preprint Nov 2025

Cross-Task Generalization in Handwriting-Based Alzheimer's Screening via Vision Language Adaptation

A lightweight Cross-Layer Fusion Adapter (CLFA) framework that repurposes Contrastive Language--Image Pre-training for handwriting-based AD screening and progressively aligns pretrained vision--language representations with AD-related handwriting cues and supports transfer from supervised source tasks to task-disjoint...

Chang-Qing Gong, Hua-Feng Qin, Mounîm A. El-Yacoubi · 0 citations
#artificial intelligence Preprint Apr 2024

Band-Attention Modulation Network for Robust Face Forgery Detection

The Band-Attention Modulation Network (BAM-Net) is proposed, a novel framework that pioneers learnable, fine-grained modulation of frequency components for forgery detection and exhibits exceptional generalization in cross-dataset, cross-compression, and cross-manipulation scenarios.

Zhi-Da Zhang, Wen-Kui Yang, Xin Ma et al. · 1 citation
#artificial intelligence Preprint Sep 2026

RAPID: Robot Agentic Programming from Demonstrations

This work introduces Robot Agentic Programming from Demonstrations (RAPID), which automatically generates, verifies, and refines robot programs, given a single visual human demonstration, using an object-centric relational program representation.

Yu-Yao Liu, Jia-Yuan Mao, David Hsu et al. · 0 citations
#artificial intelligence Preprint Sep 2026

PoEM: Predicting RL Outcomes from Existing Policies

PoEM, a framework to predict the outputs of RL on a new reward function using a set of models already post-trained on other rewards, is introduced by introducing PoEM, a framework to predict the outputs of RL on a new reward function using a set of models already post-trained on other rewards.

K. Hamidieh, G. Daras, Antonio Torralba · 0 citations
#artificial intelligence Preprint Sep 2026

TrackEverything: Long Horizon Dense Tracking via De-Duplicating 3D Scene Representations

TrackEverything is the first 3D tracker capable of tracking all visible points across videos exceeding 1000 frames within 40 GB of GPU memory, and 3D WAFT is proposed, replacing memory-prohibitive 4D correlation volumes with efficient feature sampling in the scene cloud.

Ayush Jain, Sreeharsha Paruchuri, Ishita Gupta et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Accelerating Video Diffusion via Training-Free Trajectory Routing

TRACK: TRajectory-Aware Capacity routing via top-K selection is presented, a heterogeneous denoising strategy that switches between compatible large and small models at selected steps, reducing the average cost per denoising evaluation.

Mustafa Munir, H. Vũ, Shreyas Misra et al. · 0 citations
#artificial intelligence Preprint Sep 2026

GHOST-Q: Towards Studying Grounding Hallucinations Overlooked Under Same-score TradeOffs in Quantized VLMS

Post-training quantization of vision--language models (VLMs) is typically assessed through aggregate task accuracy and memory savings, but preserving a headline score does not guarantee preservation of visual grounding behavior. We present GHOST-Q, a cross-precision controlled evaluation of three 8B VLM families under...

S. Rehman, Muhammad Shafique · 0 citations
#artificial intelligence Preprint Sep 2026

ADATEX4D: adaptive texture capacity allocation for 4D gaussian splatting

This work introduces AdaTex4D, an adaptive texture-capacity module for deformation-based 4D Gaussian Splatting, and shows that dynamic, anisotropic texture allocation provides a more efficient way to distribute local appearance capacity in 4D Gaussian representations.

Deng-Du Jiang, Pei-Qiang Wang, Ke-Hong Yuan et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Mind What Matters for Reasoning: Aligning Cross-Modal Attention via Selective Probability Mass Concentration

SPMC, a training framework that identifies grounding-responsive heads and selectively regularizes their text-to-image attention, demonstrates that targeted guidance of sparse and implicit visual evidence pathways can directly improve multimodal reasoning.

Jia-Qi Deng, Zong-Han Wu, Zhan Heng et al. · 0 citations

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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.

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