Rameswar Panda

Computer Vision and Pattern Recognition · 2016–2023

A researcher in the Science corpus: 15 papers; trusts 2,065 researchers and is trusted by 93 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 626 citations into it; #3,327 of 21,575 by trust here, #3,659 by citations
• Artificial Intelligence — 531 citations into it; #7,012 of 35,345 by trust here, #5,187 by citations
• Model Architectures — 205 citations into it; #990 of 2,278 by trust here, #994 by citations
• Efficiency & Compression — 124 citations into it; #717 of 1,264 by trust here, #852 by citations
• Evaluation & Benchmarks — 69 citations into it
• Representation & Self-Supervised Learning — 66 citations into it
• Optimization & Training — 59 citations into it
• Multimodal Learning — 56 citations into it; #1,206 of 1,211 by trust here, #1,104 by citations

Whom Rameswar Panda trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Yoshua Bengio
6. Diederik P. Kingma
7. Karen Simonyan
8. Ilya Sutskever
9. Alex Graves
10. Andrew Zisserman

Who trusts Rameswar Panda overall

1. Yanyu Li
2. Bowen Pan
3. Yulin Wang
4. Yuda Song
5. Chun-Fu Chen
6. Rahul Ramesh
7. Amir Ghodrati
8. Shiji Song
9. Wenxiao Wang
10. Yizeng Han

Papers from Rameswar Panda's perspective

Ordered by how strongly the researchers Rameswar Panda trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification (2021) — 2,175 citations
• Webly Supervised Joint Embedding for Cross-Modal Image-Text Retrieval (2018) — 81 citations
• AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning (2019) — 327 citations
• AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition (2021) — 70 citations
• AR-Net: Adaptive Frame Resolution for Efficient Action Recognition (2020) — 177 citations
• VA-RED2: Video Adaptive Redundancy Reduction (2021) — 22 citations
• RegionViT: Regional-to-Local Attention for Vision Transformers (2021) — 255 citations
• Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos (2021) — 100 citations
• Gated Linear Attention Transformers with Hardware-Efficient Training (2023) — 480 citations
• CODA-Prompt: COntinual Decomposed Attention-Based Prompting for Rehearsal-Free Continual Learning (2022) — 609 citations
• AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition (2021) — 67 citations
• Dynamic Network Quantization for Efficient Video Inference (2021) — 57 citations
• IA-RED2: Interpretability-Aware Redundancy Reduction for Vision Transformers (2021) — 204 citations
• Relationship Matters: Relation Guided Knowledge Transfer for Incremental Learning of Object Detectors (2020) — 12 citations
• Generating Diverse Image Datasets with Limited Labeling (2016) — 12 citations