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