About

Rameswar Panda

Research Staff Member

Publications Website

Who they work with

Categories

Computer Vision Machine Learning Multimodal Learning Transfer Learning

I am a Research Staff Member at MIT-IBM Watson AI Lab, Cambridge, where I work on solving real world problems using computer vision and machine learning. In particular, my current focus is on learning with limited supervision (transfer learning, few-shot learning) and dynamic computation for several computer vision problems. I received my Ph.D. from UC Riverside in 2018, under the supervision of Prof. Amit K. Roy-Chowdhury. During my Ph.D., I was very fortunate to have worked at NEC Labs America (Cupertino, Summer 2018), Adobe Research (San Jose, Summer/Fall 2017) and Siemens Corporate Research (Princeton, Summer 2016).

Publications with the MIT-IBM Watson AI Lab

ConStruct-VL: Data-Free Continual Structured VL Concepts Learning

CVPR

Teaching Structured Vision & Language Concepts to Vision & Language Models

CVPR

CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning

CVPR

AnyDA: Anytime Domain Adaptation

ICLR

Learning to Grow Pretrained Models for Efficient Transformer Training

ICLR

Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning

ICLR

How Transferable are Video Representations Based on Synthetic Data?

NeurIPS

FETA: Towards Specializing Foundation Models for Expert Task Applications

NeurIPS

Selective Regression under Fairness Criteria

ICML

VALHALLA: Visual Hallucination for Machine Translation

CVPR

Task2Sim: Towards Effective Pre-training and Transfer from Synthetic Data

CVPR

Can an Image Classifier Suffice For Action Recognition?

ICLR

RegionViT: Regional-to-Local Attention for Vision Transformers

ICLR

IA-RED^2 : Interpretability-Aware Redundancy Reduction for Vision Transformers

NeurIPS

Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data

NeurIPS

Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing

NeurIPS

AdaMML: Adaptive Multi-Modal Learning for Efficient Video Recognition

ICCV Multimodal Learning

Dynamic Video Quantization for Efficient Inference

ICCV ICCV

A Broad Study on the Transferability of Visual Representations With Contrastive Learning

ICCV

CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification

ICCV

Detector-Free Weakly Supervised Grounding by Separation

ICCV

Multimodal Clustering Networks for Self-Supervised Learning From Unlabeled Videos

ICCV

Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition

CVPR Computer Vision

NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search

AAAI Computer Vision

Non-Adversarial Video Synthesis with Learned Priors

CVPR Computer Vision

Camera On-boarding for Person Re-identification using Hypothesis Transfer Learning

CVPR Computer Vision

Semi-Supervised Action Recognition with Temporal Contrastive Learning

CVPR Computer Vision

Fair Selective Classification Via Sufficiency

ICML

AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition

ICLR Computer Vision

VA-RED^2: Video Adaptive Redundancy Reduction

ICLR Computer Vision

AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning

Computer Vision Multimodal Learning

AR-Net: Adaptive Frame Resolution for Efficient Action Recognition

ECCV Computer Vision

Relationship Matters: Relation Guided Knowledge Transfer for Incremental Learning of Object Detectors

CVPR Computer Vision