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The tweet discusses the difficulty of setting a quality metric for polygon output without ground truth labels, but proposes a 3-way agreement method as reliable. It highlights impressive results for a non-specialized Vision-Language Model despite false positives/negatives, with improvements noted using a grid prompt.
This article explains how to use the logprobs parameter in LLMs to implement Jev-style encapsulation for supporting structured queries, and extend to visual models via the attachments field for image processing, offering a flexible and customizable computer vision approach.
The article discusses the increasing capabilities and improvements in AI video models, highlighting their growing effectiveness in generating or processing video content.
ImageJevBench v0.1 is introduced as a benchmark for evaluating AI models on image decision tasks, ranking systems like Jev-Omni and decider-2b-vision based on performance, cost, and calibration metrics.
Gen-HumanEgo is a new dataset released by GenrobotAI for robot learning, featuring over 1,800 hours of egocentric human data with 10K+ unique tasks and synchronized camera views including 3D hand keypoints and structured annotations.
AI capabilities for expression recognition, object detection, and finger counting have achieved less than 1 second latency, highlighting notable real-time performance gains.
Researchers from MIT Senseable City Lab discuss the use of visual AI to analyze urban environments, highlighting its potential for urban planning while addressing concerns about privacy and fairness in a new book.
This paper introduces RGBD20K, a large-scale benchmark dataset for RGB-D semantic segmentation with 160 fine-grained categories and 20,000 image pairs, featuring high-quality annotations and a novel score-purified fusion method.
Ryan Sael demonstrates Opus 5.5's ability to create an interactive lens lab for camera focus education in under two hours at a low cost, highlighting advanced AI prototyping capabilities.
A developer shares their achievement of reducing object detection latency to below 0.35 seconds on local hardware, highlighting progress in AI performance optimization.
This paper introduces SCoR, a hierarchical framework for forecasting relations between scientific concepts, with a benchmark and model that improve research-direction discovery by predicting typed relations.
ImIR adapts a pretrained image-editing model for six image restoration tasks using image-derived instructions, enabling efficient and task-agnostic restoration.
The paper introduces ScriptMoE, a script-aware mixture-of-experts architecture for all-in-one multilingual scene text recognition, along with the TextMuSS-10M synthetic dataset, achieving state-of-the-art accuracy on benchmarks.
This paper introduces Segment-Snap, a method that combines geometric and semantic cues to improve interaction understanding in 3D scenes, achieving significant gains in motion-gated AP and handle detection metrics.
SAM 3.1 can detect, segment, and track objects in images and video using text prompts on the Meta Model API, with specified inference costs.
A user showcased a project using OpenAI's Codex to code a game controlled by hand gestures with computer vision and projection mapping, with a timelapse and process to be shared in replies.
A test showcasing the Qwen-Image-2.1 model's ability to handle multiple reference images.
UltraTex is an efficient framework for high-resolution multi-view diffusion-based 3D texturing, introducing techniques to reduce redundancy and achieve significant speedups in training and inference.
A Stanford deep learning course on computer vision taught by Professor Fei-Fei Li is available on YouTube, discussing the evolutionary role of vision in developing intelligence.
Fei-Fei Li's AI model for 3D reconstruction faces significant challenges in shipping as a reliable product, highlighting the gap between research and real-world application. The article compares it to LiDAR, discussing trade-offs in accuracy and accessibility for industries like construction and design.