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.
A small games studio scans a vintage bulldozer toy on a desk with a phone. The reconstruction looks perfect from every angle, right up to the moment an artist drops it into a sunset scene and the warm desk lamp…
Occlusion Aware 3DGS Inverse Rendering Without Ray Tracing Read More »
Point a camera down from a drone flying at altitude and the world turns into a field of specks. A car becomes a smudge of a dozen pixels. A pedestrian is barely a dark mark against pavement. In the VisDrone-2019…
ScopeMamba-YOLO Rethinks Small Object Detection Read More »
Graph neural networks look deceptively sturdy on paper. Stack a few convolution layers, pool the node embeddings, feed the result to a classifier, and you get strong numbers on clean benchmark data. The trouble starts once the input graph stops…
HPGRL: How Hierarchical Prototypes Make Graph Classification Resist Noise Read More »
Unsupervised image segmentation asks a model to draw meaningful boundaries around objects in a photo without ever being told what those objects are. No labeled training data, no annotated masks, just pixels in and a partition of the image out.…
DeepCut++’s Single Best Fusion Weight Does Not Actually Win Every Task Read More »
Picture a strip of land seen through radar instead of a camera. A river cuts a clean line through farmland, a cluster of buildings sits in a blocky grid, and a forest spreads out as a grainy, speckled mess. To…
Manifold Aware Fusion for PolSAR Image Classification Read More »
Picture a rooftop and a road sitting a few pixels apart in a satellite scene, both gray, both flat looking in a spectral plot, both the kind of thing that trips up a classifier trained on color alone. A sensor…
Graph databases have spent the last two decades moving from a niche curiosity to the default choice for anyone managing genuinely connected data, social networks, biological pathways, financial transaction webs, legal citation graphs. Most of the top ranked systems have…
We Mapped 45 Graph Algorithms Onto 20 Graph Databases Read More »
Every low rank recovery method eventually runs into the same wall. You factor your target matrix or tensor as a product of two smaller pieces, say L times R transpose, and then chase the factors with gradient steps until the…
Scaled Gradient Descent for Stable Tensor Video Recovery Read More »
Hyperspectral images are not ordinary photographs. Where a standard camera captures three color channels, a hyperspectral sensor captures hundreds of narrow, adjacent spectral bands per pixel, turning every single pixel into a rich chemical fingerprint of whatever material it is…
MCFRNet Shows Lightweight CNNs Can Rival Transformers Read More »
Unsupervised anomaly detection on streaming data carries an unusual constraint baked into its name. There are no anomaly labels to learn from, only a continuous flow of mostly normal readings that a model has to characterize on its own. That…
ADAPTS Classifies Concept Drift Before It Decides How To Adapt Read More »