AI Techniques For Image Recognition

Vision Transformers Need Registers The outstanding paper at #ICLR 2024, "Vision Transformers Need Registers” by Dracet et al., which tackles the challenge in vision transformers (#ViTs) of high-norm tokens skewing attention towards uninformative background regions. In traditional ViTs, each image patch is treated like a sequence before self-attention mechanisms. However, this often results emphasis on background noise, detracting from the model’s ability to concentrate on salient features. The solution? Introducing additional "register tokens" into the architecture. These tokens aren't derived from the image data but are included to accumulate and refine essential features across transformer layers. By balancing the attention mechanism, these registers help mitigate the impact of high-norm tokens and enhance the overall focus and efficacy of the model. This approach not only improves clarity and relevance in image analysis but also sets a new standard for addressing common pitfalls in vision transformers, potentially revolutionizing how we tackle various image-based tasks. 📖 Dive deeper into this transformative work and explore its implications for the future of computer vision: https://lnkd.in/gmPgj82t #ICLR2024 #MachineLearning #ComputerVision #AIResearch #Innovation

Open Vocabulary Detection with Qwen2.5-VL 🔥 🔥 🔥 I've been diving into how well Vision-Language Models (VLMs) like Qwen2.5-VL can understand images and find objects. This builds on my earlier tutorials about zero-shot detection with models like GroundingDINO and YOLO-World (links in comments). I wanted to see how well VLMs can not only detect objects but also understand where they are in an image. As usual, I've created a notebook to show you what I've been testing: - Object detection using Qwen2.5-VL with different types of instructions (prompts). - Trying to find single and multiple objects in an image. - Using descriptions like "the object on the left" or "the closest object" to find specific items. - Asking the model to reason about objects: "What would I use to open this?" ⮑ 🔗 notebook: https://lnkd.in/dJchZZiJ More examples are in the comments! 👇🏻

Some new impressive open vocabulary detectors landed in the Hugging Face Transformers library! 🔥 LLMDet (CVPR '25 highlight) and MM-Grounding DINO are now available. These models are so-called "open vocabulary" or "zero-shot" detection models. This means that they can detect objects in an image just via prompting, no training involved! Previously, we supported impressive models such as Google's OWL-ViT, OWLv2 as well as Grounding DINO - they are pretty popular on the hub with more than 1 million monthly downloads. They can greatly speed up annotation. Today, those got leveled up by some more recent works. MM-Grounding DINO is an improvement of Grounding DINO built on the MMDetection library. It serves as an open-source, comprehensive, and user-friendly baseline, as the original Grounding DINO didn't open-source any training code 😞. The authors also outperform the original model. The second one is called LLMDet and leverages a Large Language Model (LLM) to generate both region-level short captions and image-level long captions on 1 1 million images in total. Thanks to the supervision of the LLM, the model outperforms prior models by a large margin, especially for rare or compositional categories. It is fully compatible with the architecture of MM-Grounding DINO. As we now support 5 different models, Aritra Roy Gosthipaty build an awesome "zero-shot object detection" arena where you can quickly compare the results on an image. Resources: - MM-Grounding-DINO: https://lnkd.in/e-uNjvzj - LLMDet: https://lnkd.in/e5Jxdbqh - Zero-shot object detection arena: https://lnkd.in/et7tpdxu - Zero-shot object detection explained: https://lnkd.in/e6SprRBV

Real-time object detection had a hard trade-off. Speed or accuracy. Rarely both. For years, this limitation defined how computer vision systems were built. Until one idea changed it. Process the image once, understand everything. That is exactly what YOLO changed. Instead of scanning an image multiple times, it divides the image into a grid. Each grid cell predicts: → Objectness confidence: is there an object → Class probability: what the object is → Bounding box: where it is located All in a single forward pass. This made real-time object detection practical at scale, enabling systems like video analytics and autonomous driving. ➤ Why YOLO works so well → Single-pass detection reduces latency significantly → End-to-end architecture simplifies the pipeline → Ideal for real-time applications where speed matters ➤ But every system has trade-offs → Struggles with small or densely packed objects → Less precise in complex scenes compared to region-based models ➤ Choosing the right approach → Need millisecond latency → YOLO → Need high precision → Faster R-CNN → Limited hardware → Lightweight models like MobileNet → Complex global context → Vision Transformers The real takeaway is simple. Performance is not about the best model. It is about the right model for the constraint. In production, the best system is not the most accurate one. It is the one that works reliably under real-world constraints. Where have you seen speed vs accuracy trade-offs in real systems? Repost to help an engineer in your network Follow Piku Maity for daily hands-on AI learnings #AI #ComputerVision #YOLO #MachineLearning #DeepLearning #VisionTransformer #AIEngineering #AISystems

AI is actively reshaping clinical care, and this recent work from Mayo Clinic makes that clear. Researchers developed a system that analyzes patient-submitted wound photos to detect surgical site infections with 94% incision detection accuracy and 81% AUC for infection identification. https://lnkd.in/gfnETJeh   The two-stage Vision Transformer first confirms image quality, then flags signs of infection. Trained on 20,000+ images from over 6,000 patients across nine hospitals, it delivers scalable, bias-resistant performance.   By streamlining triage, this AI tool accelerates care, reduces burden on clinicians and broadens access, especially in outpatient and remote settings. A strong example of how intelligent systems can move beyond support and into core care delivery.

Some of my most memorable research moments came from TEM images, and some of my most tedious ones too. When I was working on nanomaterials in graduate school, TEM (transmission electron microscopy) was indispensable. Whether studying catalysts, battery electrodes, or nanostructured films, those images revealed the fine details that governed material performance. But to go from raw images to statistically sound conclusions requires tedious data process steps. Computer-vision-based methods can handle standardized, well-operationalized tasks such as particle size distribution. Machine learning offers more flexibility for diverse morphological and imaging conditions, yet curating labeled datasets for every system is rarely feasible for fast-moving discovery projects. Published in npj Computional Materials, Fengqi You, Qian He et al. demonstrated #EMcopilot, a label-efficient generative framework that segments electron micrographs with minimal manual annotation. Here's how it works: 🔹Extract structural patterns from 33 labeled images using a vision model (SAM), such as particle sizes, shapes, and arrangements. Then randomly reorganize these to create thousands of new particle configurations with realistic statistical properties. 🔹Generate thousands of synthetic micrographs by training a conditional GAN (pix2pix) to translate these particle configurations into realistic-looking microscope images that mimic real variations in particle contrast, substrate thickness effects, and intensity distributions. 🔹Refine through domain adaptation by adding microscope-specific imperfections: noise from electron beam fluctuations, detector noise, sample drift etc., so the final training set mirrors real experimental conditions. 🔹Train a segmentation model on this synthetic dataset (~6,000 images from 33 starting examples) to automatically identify and measure particles in new microscope images. This process achieves segmentation accuracy on par with fully supervised models, and even detects particles in low-contrast regions often missed by human annotators. As research workflows become more autonomous, characterization remains the slowest link. We'll need more AI-driven analysis to close that gap, turning data-rich imaging into insight at the pace of discovery. 📄 Generative learning of morphological and contrast heterogeneities for self-supervised electron micrograph segmentation, npj Computational Materials, October 29, 2025 🔗 https://lnkd.in/e7NafhiK

I-JEPA: Teaching AI to Understand Images Without Labels In this episode of Artificial Intelligence: Papers and Concepts, we break down I-JEPA, a self-supervised vision architecture that moves beyond pixel-level learning toward true conceptual understanding. Instead of forcing models to memorize images or rely on massive labeled datasets, I-JEPA learns by predicting meaningful representations - helping AI focus on structure, context, and relationships within a scene rather than surface details. We explore how joint-embedding predictive architectures reshape computer vision, why traditional training methods struggle to capture real-world understanding, and how researchers from Meta AI and leading institutions are redefining how machines learn from visual data. If you’re interested in foundation models, self-supervised learning, or the future of computer vision beyond labels, this episode explains why I-JEPA marks a major shift toward more human-like visual intelligence. Resources Paper Link: https://lnkd.in/gDWxN_Mr Interested in Computer Vision and AI consulting and product development services? Email us at contact@bigvision.ai or  visit us at https://bigvision.ai

𝘓𝘢𝘳𝘨𝘦 𝘧𝘰𝘶𝘯𝘥𝘢𝘵𝘪𝘰𝘯𝘢𝘭 𝘈𝘐 𝘮𝘰𝘥𝘦𝘭𝘴 𝘢𝘭𝘸𝘢𝘺𝘴 𝘴𝘤𝘢𝘭𝘦 𝘣𝘦𝘵𝘵𝘦𝘳, 𝘳𝘪𝘨𝘩𝘵? 𝘞𝘩𝘢𝘵 𝘪𝘧 𝘵𝘩𝘢𝘵 𝘢𝘴𝘴𝘶𝘮𝘱𝘵𝘪𝘰𝘯 𝘪𝘴 𝘤𝘰𝘮𝘱𝘭𝘦𝘵𝘦𝘭𝘺 𝘣𝘢𝘤𝘬𝘸𝘢𝘳𝘥𝘴 𝘧𝘰𝘳 𝘥𝘦𝘯𝘴𝘦 𝘷𝘪𝘴𝘶𝘢𝘭 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥𝘪𝘯𝘨? New research reveals vision encoders actually lose their grip on local details, but a clever pretraining tweak fixes this flaw. This is important because achieving precise, pixel-level alignment between images and text remains a massive bottleneck for complex multimodal applications like open-vocabulary segmentation. A recent paper from Google DeepMind titled "𝐓𝐈𝐏𝐒𝐯2: 𝐀𝐝𝐯𝐚𝐧𝐜𝐢𝐧𝐠 𝐕𝐢𝐬𝐢𝐨𝐧-𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐏𝐫𝐞𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐏𝐚𝐭𝐜𝐡-𝐓𝐞𝐱𝐭 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭" tackles this problem. The researchers discovered that standard masked image modeling ignores visible tokens during training, which actively degrades a model's local semantic grounding. To solve this, they developed iBOT++, a novel self-supervised objective that forces the model to align both masked and unmasked patches with textual concepts. This simple architectural shift yielded a massive +14.1 mIoU improvement in zero-shot segmentation. Furthermore, by introducing a "head-only" exponential moving average (EMA) strategy, they reduced trainable memory overhead by nearly half, matching or exceeding state-of-the-art vision models across 20 distinct datasets. This paves the way for highly efficient, natively text-aligned vision models that don't sacrifice spatial awareness for global understanding. #ArtificialIntelligence #MachineLearning #ComputerVision #DeepLearning #AIResearch

For the past few years, medical AI research has focused on scaling vision encoders to extract better features from pixels. But a clinician does more than look; they synthesize. They combine histology with genomics, endoscopy with pathology, and images with dialogue. Four new papers demonstrate a move from unimodal vision to multimodal clinical intelligence. These models are no longer just classifying images; they are integrating diverse data streams to mimic diagnostic reasoning. Here is how the latest research tackles this. Pathology: Two new foundation models tackle pathology with opposing strategies. Eugene Vorontsov et al. introduce PRISM2, betting on massive scale (2.3M slides) and language alignment. By training on 14 million question-answer pairs, they utilize clinical dialogue supervision to teach the model diagnostic reasoning, achieving clinical-grade cancer detection zero-shot. In contrast, Yingxue Xu et al. introduce mSTAR, challenging the "scale is all you need" dogma. Using a dense dataset of tri-modal pairs (WSI + Reports + RNA-Seq), they employ a self-taught pretraining paradigm. This allows mSTAR to outperform larger vision-only models on molecular tasks with significantly less data, proving that multimodal density can be more efficient than brute-force scaling. Dermatology: While pathology models focus on text and genes, Siyuan Yan et al. present PanDerm, a model that unifies the fragmented visual world of dermatology. It is trained on 2 million images across 4 distinct modalities: clinical photography, dermoscopy, total-body photography, and histopathology. The key innovation is versatility; reader studies showed PanDerm improved clinicians' accuracy in diagnosing 128 different skin conditions and outperformed experts in early melanoma detection via longitudinal monitoring. Gastroenterology: Moving beyond static images, Marietta Iacucci et al. introduce the Endo-Histo fusion model. Developed using data from a Mirikizumab clinical trial for Ulcerative Colitis, this framework fuses features from endoscopic video and histologic slides. This multimodal fusion significantly outperformed single-modality assessment for histologic remission and treatment response, offering a new standard for precision medicine in clinical trials. PRISM2: https://lnkd.in/ewytDUtN mSTAR: https://lnkd.in/e-4mdwsU PanDerm: https://lnkd.in/e4WQX4k9 Endo-Histo: https://lnkd.in/e8eHGChq --- Keeping up with the literature is increasingly a team sport. This analysis was supported by NotebookLM and grounded in my own review and experience. If you found this useful, let me know in the comments. If it missed the mark, I want that feedback too. Weekly briefings on making vision AI work in the real world → https://lnkd.in/guekaSPf

Last week Microsoft's Azure AI team dropped the paper for Florence-2: the new version of the foundation computer vision model. This is significant advancement in computer vision and is a significant step up from the original Florence model. 📥 Dataset: Florence-2 has the ability to interpret and understand images comprehensively. Where the original Florence excelled in specific tasks, Florence-2 is adept at multitasking. It's been trained on an extensive FLD-5B dataset encompassing a total of 5.4B comprehensive annotations across 126M images, enhancing its ability to handle a diverse range of visual task such as object detection, image captioning, and semantic segmentation with increased depth and versatility. 📊 Multi-Task Capability: Florence-2's multitasking efficiency is powered by a unified, prompt-based representation. This means it can perform various vision tasks using simple text prompts, a shift from the original Florence model's more task-specific approach. 🤖 Vision and Language Integration: Similar to GPT-4's Vision model, Florence-2 integrates vision and language processing. This integration is facilitated by its sequence-to-sequence architecture, similar to models used in natural language processing but adapted for visual content. 👁️ Practical Applications: Florence-2's capabilities can enhance autonomous vehicle systems' environmental understanding, aid in medical imaging for more accurate diagnoses, surveillance, etc. Its ability to process and understand visual data on a granular level opens up new avenues in AI-driven analysis and automation. Florence-2 offers a glimpse into the future of visual data processing. Its approach to handling diverse visual tasks and the integration of large-scale data sets for training sets it apart as a significant development in computer vision. Paper: https://lnkd.in/deUQf9NG Researchers: Ce Liu, Bin Xiao, Haiping Wu, Weijian Xu, Xiyang Dai, Houdong Hu, Yumao Lu, Michael Zeng, Lu Yuan #Microsoft #AzureAI #Florence #computervision #foundationmodels