Detect unknown and known objects using CLIP, SAM, and GDINO with cooperative-foundational-models

Count objects in image with point prompts from object localization and CLIP to identify with PseCo

Segment scene with RGB-D images by efficiently fusing RGB and depth features with MIPANet

Detect objects in new domain by distilling more balanced source features with DUA-DA

Get object pose using pre-trained synthetic data and no ground truth labels with RKHSPose

Super-resolution video using recurrent back-projection GAN with RBPGAN

Get 3D object shape from monocular RGB-D by learn surface and map to frame with DynamicSurf

Get 3D object and scene using new regularization term and quadratic layers with StEik

Segmentation and depth using generalized cluster prediction with mask transformer with PolyMaX

Get human pose using attention mechanism to expand receptive fields with SADI-NET

Detect 3D object from one image using features from 3D-aware diffusion with 3DiffTection

Explain a fine-grained image classification result by searching image for class with INTR

Survey of video captioning many events in a scene

Segment 3D point clouds using foundation models for 2D vision by with Dong

Get 3D scene from a few monocular images using CLIP with varying depth bins with Hu

Get 3D object shape with fine shape details using monocular images with SSR

Survey of computer vision backbones on many tasks

Segment objects in scene after training only on object types with SISeg

Segment scene unsupervised using close features have similar semantics with SmooSeg

Segment scene in new domain unsupervised by refine pseudo labels and predict noisy labels with PRN