Computer vision is one of our deepest areas, covering how machines learn to see, segment, and reason about images and video. Articles here range from convolutional and transformer based architectures to dense prediction tasks like detection and segmentation, with regular coverage of medical imaging where reliable vision models carry real clinical weight. The emphasis stays on what makes a method work and where it breaks, backed by the original research.
Retail demand planning would be simple if every product existed in isolation. It does not. A grocery chain needs a forecast for total daily revenue, for each brand it stocks, for each store, and for each item on the shelf,…
Graph Neural Networks Bring Coherent Forecasts to Retail Read More »
ProtoSig replaces thousands of random forgeries with 50 clustered prototype signatures, cutting training compute by over 98% while matching verification accuracy — and making signature verification fairer and more stable.
RigFace edits expression, pose, and lighting in portraits while preserving identity, by injecting identity features through self-attention instead of cross-attention in a diffusion model.
Why RigFace Injects Identity Through Self-Attention Instead of Cross-Attention Read More »
DiffFuseNet runs diffusion denoising on shallow encoded features instead of full images, making infrared-visible fusion roughly ten times faster than prior diffusion methods without sacrificing quality.
SGF-MRI uses a fast auxiliary MRI sequence to guide reconstruction of slower scans, speeding up brain and knee MRI while preserving diagnostic detail.
How SGF-MRI Speeds Up Brain and Knee MRI Scans Read More »
HCEP tackles open-set emotion recognition: teaching multimodal AI to admit when an expression matches no known category instead of forcing a wrong label.
Teaching AI to Say “I Don’t Know”: HCEP and the Open-Set Emotion Recognition Problem Read More »
SMM-FNet fuses hyperspectral, multispectral, and radar imagery through purpose-built Mamba modules, showing why three sensor sources beat two in remote sensing.
When Two Sensors Are Not Enough: SMM-FNet Fuses Three Remote Sensing Sources With Mamba Read More »
RASD-FuNet routes each feature point to a low-rank or attention branch based on a learned confidence score, topping hyperspectral fusion benchmarks at a fraction of the FLOPs.
KEDR separates shared disease knowledge from modality-specific visual detail so medical AI can still diagnose reliably when one scan is missing.
Missing Modality Medical Imaging: When a Scan Is Absent, Can AI Still Diagnose You? Read More »
KECL detects multimodal misinformation with knowledge-enhanced encoding and disentangled cross-modal alignment, catching image-caption pairs that lie together.
When Images and Text Lie Together — A New AI (KECL) Framework Catches What Others Miss Read More »