HyperDet is presented, a detector- agnostic input enhancement pipeline that constructs task- aware hyper 4D radar point clouds by combining measured observations with completed foreground geometry, validating input-level radar enhancement as an effective approach to radar-only 3D detection.
Yi Xiao, Jin Jin, Run-Wei Guan et al.· arXiv.org· 3 citations
High-quality annotated datasets are crucial for advancing machine learning in medical image analysis. However, a critical gap exists: most datasets either offer a single, clean ground truth, which hides real-world expert disagreement, or they provide multiple annotations without a separate gold standard for objective e...
Yonghao Si, Xingyuan Zeng, Zhao Chen et al.· 0 citations
An annotation pipeline consisting of a diffusion model to lift 2D hand keypoint sequences to 4D hand motion and a diffusion loss to account for the multimodality in hand motion distribution is designed.
Aditya Prakash, R. Li, David Forsyth et al.· arXiv.org· 1 citation
This work introduces Frame Selection Sensitivity (FSS), a per-sample diagnostic that measures how much VLM accuracy changes when the most relevant frames are replaced with the least relevant ones, and constructs TempCore, compact evaluation subsets that isolate temporal samples from existing benchmarks.
Hyunjong Ok, Jaeho Lee· 3 citations
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A unified benchmarking platform consolidating 22 baselines spanning shallow ML, CNN, Transformer, graph neural network (GNN), and multimodal architectures under a standardized, reproducible pipeline for pedestrian-level place recognition in dense urban environments is released.
Yi-Wei Ou, Xiao-Bin Ren, Rong Sun et al.· 5 citations
As machine learning evolves, domain generalization (DG) and domain adaptation (DA) have become crucial for improving model robustness across diverse environments. Contrastive Language-Image Pretraining (CLIP) plays a central role in these tasks, offering strong zero-shot capabilities that allow models to operate effect...
Jindong Li, Yongguang Li, Yali Fu et al.· 0 citations
Beyond high-fidelity image synthesis, diffusion models have recently exhibited promising results in dense visual perception tasks. However, most existing work treats diffusion models as a standalone component for perception tasks, employing them either solely for off-the-shelf data augmentation or as mere feature extra...
Shuhong Zheng, Zhipeng Bao, Ruoyu Zhao et al.· 0 citations
We propose a graph-based tracking formulation for multi-object tracking (MOT) where target detections contain kinematic information and re-identification features (attributes). Our method applies a successive shortest paths (SSP) algorithm to a tracking graph defined over a batch of frames. The edge costs in this track...
Griffin Golias, Masa Nakura-Fan, Vitaly Ablavsky· 0 citations
Chance-calibrated neighborhood discordance (CANDOR) is applied to 22 frozen encoders on 605,443 images from 20 public datasets, covering 8 binary tasks in 7 domains, and finds that on every task, the best encoder is below 1/2.
Results indicate that temporal editing is a relevant threat model for online handwriting recognition, particularly in one-shot black-box transfer settings.
Yataro Tamura, Brian Kenji Iwana, Jiseok Lee· arXiv.org· 0 citations
Clinical decision-making in oncology requires forecasting how disease evolves under treatment, yet most AI systems remain static predictors that cannot model longitudinal, treatment-conditioned progression. Although generative and world models have demonstrated strong capabilities in general domains, their adaptation t...
Tianxingjian Ding, Yuanhao Zou, Chen Chen et al.· 0 citations
This paper proposes a novel Large Language Model (LLM)-driven Training-free Automatic Proxy (dubbed TAP) discovery framework that reforms the design paradigm of MPQ by utilizing LLMs and evolutionary search strategies to automatically find superior TAP tailored for MPQ.
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
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.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
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