588 results on '"Stephen Lin"'
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2. Expression of the αVβ3 integrin affects prostate cancer sEV cargo and density and promotes sEV pro‐tumorigenic activity in vivo through a GPI‐anchored receptor, NgR2
3. Unifying Feature and Cost Aggregation with Transformers for Semantic and Visual Correspondence.
4. NuTime: Numerically Multi-Scaled Embedding for Large- Scale Time-Series Pretraining.
5. Collaboratively Self-supervised Video Representation Learning for Action Recognition.
6. Image to Pseudo-Episode: Boosting Few-Shot Segmentation by Unlabeled Data.
7. Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised Learning.
8. ClipCrop: Conditioned Cropping Driven by Vision-Language Model.
9. Working in fours: generational communication in the emergency department
10. EVA Mission Systems Software (EMSS): How Software Systems are Supporting Science and Operations in the Planning and Execution of Artemis EVAs
11. Global Context Networks.
12. Cross-Model Pseudo-Labeling for Semi-Supervised Action Recognition.
13. Video Swin Transformer.
14. A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation.
15. Unsupervised Learning of Efficient Geometry-Aware Neural Articulated Representations.
16. Bringing Rolling Shutter Images Alive with Dual Reversed Distortion.
17. Animation from Blur: Multi-modal Blur Decomposition with Motion Guidance.
18. Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentation.
19. The NOGO receptor NgR2, a novel αVβ3 integrin effector, induces neuroendocrine differentiation in prostate cancer
20. Exploring Transferability for Randomized Smoothing.
21. NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time Series Pretraining.
22. Associative Transformer Is A Sparse Representation Learner.
23. Extreme Masking for Learning Instance and Distributed Visual Representations.
24. Swin Transformer: Hierarchical Vision Transformer using Shifted Windows.
25. Neural Articulated Radiance Field.
26. Aligning Pretraining for Detection via Object-Level Contrastive Learning.
27. Bootstrap Your Object Detector via Mixed Training.
28. The Emergence of Objectness: Learning Zero-shot Segmentation from Videos.
29. Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning.
30. Instance Localization for Self-Supervised Detection Pretraining.
31. Cross-Iteration Batch Normalization.
32. Distilling Localization for Self-Supervised Representation Learning.
33. Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistency.
34. Single Image Reflection Removal Through Cascaded Refinement.
35. A Transductive Approach for Video Object Segmentation.
36. Dense RepPoints: Representing Visual Objects with Dense Point Sets.
37. Detecting Human-Object Interactions with Action Co-occurrence Priors.
38. Disentangled Non-local Neural Networks.
39. Object-Based Illumination Estimation with Rendering-Aware Neural Networks.
40. SRNet: Improving Generalization in 3D Human Pose Estimation with a Split-and-Recombine Approach.
41. Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation.
42. Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation.
43. Leveraging Multi-View Image Sets for Unsupervised Intrinsic Image Decomposition and Highlight Separation.
44. Could Giant Pre-trained Image Models Extract Universal Representations?
45. Machine Boss: rapid prototyping of bioinformatic automata.
46. Deep Depth from Uncalibrated Small Motion Clip.
47. Dense Cross-Modal Correspondence Estimation With the Deep Self-Correlation Descriptor.
48. ACP++: Action Co-Occurrence Priors for Human-Object Interaction Detection.
49. Local Relation Networks for Image Recognition.
50. RepPoints: Point Set Representation for Object Detection.
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