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Query-grounded visual sampling can boost long video understanding in LVLMs by over 11% without the need for extensive training.
PixelPilot redefines trajectory prediction in autonomous driving by transforming it into scalable 2D tasks, leading to unprecedented generalization across heterogeneous datasets.
Targeted structural completion can slash Gaussian usage by 74% and rendering time by 34%, revolutionizing 3D reconstruction in autonomous driving.
SparseOcc++ achieves a 2.3-point IoU improvement while being 3.9 times faster than previous methods, revolutionizing 3D semantic occupancy prediction.
User-level depression detection can be dramatically improved by routing individuals to specialized experts based on weak semantic priors, rather than relying on a one-size-fits-all classifier.
Overcome the static train-then-freeze paradigm with a new test-time adaptation framework that significantly improves camouflaged object detection in unseen environments.
Achieve spatially grounded natural language descriptions of urban development with PTNet, a new model that understands change semantics better than existing methods.
Revitalizing the latent edge-sensitivity of DINO with SAM's structural priors yields state-of-the-art open-vocabulary segmentation, especially in cluttered scenes.
