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Showing 2301–2325 of 10696 papers

TitleStatusHype
SPOT: Self-Training with Patch-Order Permutation for Object-Centric Learning with Autoregressive TransformersCode1
Open-vocabulary object 6D pose estimation—0
Gaussian Grouping: Segment and Edit Anything in 3D ScenesCode2
TrackDiffusion: Tracklet-Conditioned Video Generation via Diffusion ModelsCode2
Lasagna: Layered Score Distillation for Disentangled Object RelightingCode1
LucidDreaming: Controllable Object-Centric 3D Generation—0
Is Underwater Image Enhancement All Object Detectors Need?Code1
TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios—0
TIDE: Test Time Few Shot Object DetectionCode0
SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation—0
Union-over-Intersections: Object Detection beyond Winner-Takes-AllCode0
FoundPose: Unseen Object Pose Estimation with Foundation Features—0
A Simple Video Segmenter by Tracking Objects Along Axial TrajectoriesCode1
Hy-Tracker: A Novel Framework for Enhancing Efficiency and Accuracy of Object Tracking in Hyperspectral Videos—0
HOLD: Category-agnostic 3D Reconstruction of Interacting Hands and Objects from VideoCode2
Informal Safety Guarantees for Simulated Optimizers Through Extrapolation from Partial Simulations—0
Object-based (yet Class-agnostic) Video Domain Adaptation—0
A Stochastic-Geometrical Framework for Object Pose Estimation based on Mixture Models Avoiding the Correspondence Problem—0
RQFormer: Rotated Query Transformer for End-to-End Oriented Object DetectionCode1
Weakly-semi-supervised object detection in remotely sensed imagery—0
The devil is in the fine-grained details: Evaluating open-vocabulary object detectors for fine-grained understandingCode1
CG3D: Compositional Generation for Text-to-3D via Gaussian Splatting—0
Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object SegmentationCode1
Leveraging VLM-Based Pipelines to Annotate 3D Objects—0
HiDiffusion: Unlocking Higher-Resolution Creativity and Efficiency in Pretrained Diffusion Models—0
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