Category
4,009 papers
BanglaMemeX is introduced, a culturally grounded multimodal benchmark comprising 3,000 Bangla memes annotated with multi-dimensional labels and human-written explanations that explicitly describe textual and visual metaphors.
This work proposes visual search augmented chain-of-thought reasoning (ViS-CoT), a training-free, plug-and-play pipeline that can be easily applied to any open-source video VLM for video-to-text AVE in e-Commerce.
CONDUIT, a training-free refresh policy that unifies single- and multi-image reuse as residual-stream restoration, is proposed, building on norm-weighted attention and an accessible pre-output cached-value-norm proxy, then applies empirical image-level relevance amplification before one global selection.
Pre-Warm, the proposed methodology, is the rule-based use of both dictionaries: the patch count follows the filter count and a foreground density, both dictionaries are built from a single minibatch, and whichever of principal components or k-means better reconstructs those patches is written into the first half of the...
Reach audiences
Advertise in front of researchers, engineers, and readers.
This paper presents a new theoretical perspective to understand the cross-modal (vision-language) contributions to consecutive environments, and provides deeper insights into continual VLMs, highlighting their contribution robustness to varying task orders and inter-task similarities, and their improved generalization...
This work proposes a geometry-driven metric that predicts the utility of synthetic data without requiring model training and shows that this metric exhibits strong correlation with downstream classification performance of CNNs trained on mixtures of real negative and synthetic positive data.
Interpreting the decisions of complex computer vision models is crucial to establish trust and accountability, especially in safety-critical domains. An established approach to interpretability is generating visual attribution maps that highlight regions of the input most relevant to the model's prediction. However, ex...
Trajectory anomaly detection underpins applications from fraud detection to urban mobility analysis. Dense GPS methods preserve fine-grained evidence such as abnormal speeds and short-duration events, but their quadratic cost makes multi-month analysis intractable; consequently, no existing approach detects anomalies o...
Recent collapse-free quantizers such as FSQ achieve stable training by replacing the learnable codebook with an engineered geometry: a fixed scalar grid whose structure is dictated by the codebook size K. We show this trade-off is unnecessary. We introduce Learnable Geometric Quantization (LGQ), which retains a learnab...
Online handwritten character recognition leverages stroke order and dynamic features, which generally provide higher accuracy and robustness compared with offline recognition. However, in practical applications, rotational deformations can disrupt the spatial layout of strokes, substantially reducing recognition accura...
A geo-context-guided visual transformer (GCGVT) that enhances remote sensing imagery processing with auxiliary geospatial guidance and outperforms pretrained remote sensing vision-language models and graph-based spatial fusion baselines in disease prevalence prediction.
Previous works identify sensitivity to option order as a key issue in multiple-choice VQA (MC-VQA) evaluation and propose protocols to mitigate this effect. We show that such mitigation is insufficient to ensure the validity of MC-VQA as a reliable benchmark for Multimodal Large Language Model (MLLMs): performance rema...
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
GPT-Lab
· gpt-lab.eu
Oct 8, 2026
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.
A Blog post by H company on Hugging Face
A Blog post by Liquid AI on Hugging Face
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
