Search Results for author: Charless Fowlkes

Found 35 papers, 15 papers with code

Introspective Cross-Attention Probing for Lightweight Transfer of Pre-trained Models

no code implementations7 Mar 2023 Yonatan Dukler, Alessandro Achille, Hao Yang, Varsha Vivek, Luca Zancato, Ben Bowman, Avinash Ravichandran, Charless Fowlkes, Ashwin Swaminathan, Stefano Soatto

We show that, even when selecting a single top-scoring adapter, InCA achieves performance comparable to full fine-tuning, at a cost comparable to fine-tuning just the last layer.

Transfer Learning

Guided Recommendation for Model Fine-Tuning

no code implementations CVPR 2023 Hao Li, Charless Fowlkes, Hao Yang, Onkar Dabeer, Zhuowen Tu, Stefano Soatto

With thousands of historical training jobs, a recommendation system can be learned to predict the model selection score given the features of the dataset and the model as input.

Model Selection Transfer Learning

Task Adaptive Parameter Sharing for Multi-Task Learning

1 code implementation CVPR 2022 Matthew Wallingford, Hao Li, Alessandro Achille, Avinash Ravichandran, Charless Fowlkes, Rahul Bhotika, Stefano Soatto

TAPS solves a joint optimization problem which determines which layers to share with the base model and the value of the task-specific weights.

Multi-Task Learning

GeoFill: Reference-Based Image Inpainting with Better Geometric Understanding

no code implementations20 Jan 2022 Yunhan Zhao, Connelly Barnes, Yuqian Zhou, Eli Shechtman, Sohrab Amirghodsi, Charless Fowlkes

Our approach achieves state-of-the-art performance on both RealEstate10K and MannequinChallenge dataset with large baselines, complex geometry and extreme camera motions.

Image Inpainting Monocular Depth Estimation

Representation Consolidation from Multiple Expert Teachers

no code implementations29 Sep 2021 Zhizhong Li, Avinash Ravichandran, Charless Fowlkes, Marzia Polito, Rahul Bhotika, Stefano Soatto

Indeed, we observe experimentally that standard distillation of task-specific teachers, or using these teacher representations directly, **reduces** downstream transferability compared to a task-agnostic generalist model.

Knowledge Distillation

Representation Consolidation for Training Expert Students

no code implementations16 Jul 2021 Zhizhong Li, Avinash Ravichandran, Charless Fowlkes, Marzia Polito, Rahul Bhotika, Stefano Soatto

Traditionally, distillation has been used to train a student model to emulate the input/output functionality of a teacher.

SSCAP: Self-supervised Co-occurrence Action Parsing for Unsupervised Temporal Action Segmentation

no code implementations29 May 2021 Zhe Wang, Hao Chen, Xinyu Li, Chunhui Liu, Yuanjun Xiong, Joseph Tighe, Charless Fowlkes

However, it is quite expensive to annotate every frame in a large corpus of videos to construct a comprehensive supervised training dataset.

Action Parsing Action Segmentation +2

A linearized framework and a new benchmark for model selection for fine-tuning

no code implementations29 Jan 2021 Aditya Deshpande, Alessandro Achille, Avinash Ravichandran, Hao Li, Luca Zancato, Charless Fowlkes, Rahul Bhotika, Stefano Soatto, Pietro Perona

Since all model selection algorithms in the literature have been tested on different use-cases and never compared directly, we introduce a new comprehensive benchmark for model selection comprising of: i) A model zoo of single and multi-domain models, and ii) Many target tasks.

Model Selection

Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning

1 code implementation CVPR 2021 Zhaowei Cai, Avinash Ravichandran, Subhransu Maji, Charless Fowlkes, Zhuowen Tu, Stefano Soatto

We present a plug-in replacement for batch normalization (BN) called exponential moving average normalization (EMAN), which improves the performance of existing student-teacher based self- and semi-supervised learning techniques.

Self-Supervised Learning Semi-Supervised Image Classification

Camera Pose Matters: Improving Depth Prediction by Mitigating Pose Distribution Bias

1 code implementation CVPR 2021 Yunhan Zhao, Shu Kong, Charless Fowlkes

We show that jointly applying the two methods improves depth prediction on images captured under uncommon and even never-before-seen camera poses.

Data Augmentation Depth Estimation +1

Weak Supervision and Referring Attention for Temporal-Textual Association Learning

no code implementations21 Jun 2020 Zhiyuan Fang, Shu Kong, Zhe Wang, Charless Fowlkes, Yezhou Yang

The referring attention is our designed mechanism acting as a scoring function for grounding the given queries over frames temporally.

Celeganser: Automated Analysis of Nematode Morphology and Age

1 code implementation11 May 2020 Linfeng Wang, Shu Kong, Zachary Pincus, Charless Fowlkes

The nematode Caenorhabditis elegans (C. elegans) serves as an important model organism in a wide variety of biological studies.

Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation

no code implementations CVPR 2020 Yunhan Zhao, Shu Kong, Daeyun Shin, Charless Fowlkes

In this setting, we find that existing domain translation approaches are difficult to train and offer little advantage over simple baselines that use a mix of real and synthetic data.

Depth Prediction Monocular Depth Estimation +2

Modularized Textual Grounding for Counterfactual Resilience

1 code implementation CVPR 2019 Zhiyuan Fang, Shu Kong, Charless Fowlkes, Yezhou Yang

Computer Vision applications often require a textual grounding module with precision, interpretability, and resilience to counterfactual inputs/queries.

Natural Language Visual Grounding Phrase Grounding +2

Task2Vec: Task Embedding for Meta-Learning

1 code implementation ICCV 2019 Alessandro Achille, Michael Lam, Rahul Tewari, Avinash Ravichandran, Subhransu Maji, Charless Fowlkes, Stefano Soatto, Pietro Perona

We demonstrate that this embedding is capable of predicting task similarities that match our intuition about semantic and taxonomic relations between different visual tasks (e. g., tasks based on classifying different types of plants are similar) We also demonstrate the practical value of this framework for the meta-task of selecting a pre-trained feature extractor for a new task.


Image Reconstruction with Predictive Filter Flow

2 code implementations28 Nov 2018 Shu Kong, Charless Fowlkes

We propose a simple, interpretable framework for solving a wide range of image reconstruction problems such as denoising and deconvolution.

Deblurring Denoising +3

Resisting Large Data Variations via Introspective Transformation Network

no code implementations16 May 2018 Yunhan Zhao, Ye Tian, Charless Fowlkes, Wei Shen, Alan Yuille

Experimental results verify that our approach significantly improves the ability of deep networks to resist large variations between training and testing data and achieves classification accuracy improvements on several benchmark datasets, including MNIST, affNIST, SVHN, CIFAR-10 and miniImageNet.

Data Augmentation Few-Shot Learning

Pixel-wise Attentional Gating for Parsimonious Pixel Labeling

1 code implementation3 May 2018 Shu Kong, Charless Fowlkes

To achieve parsimonious inference in per-pixel labeling tasks with a limited computational budget, we propose a \emph{Pixel-wise Attentional Gating} unit (\emph{PAG}) that learns to selectively process a subset of spatial locations at each layer of a deep convolutional network.

Boundary Detection Semantic Segmentation +1

Fine-Grained Facial Expression Analysis Using Dimensional Emotion Model

no code implementations2 May 2018 Feng Zhou, Shu Kong, Charless Fowlkes, Tao Chen, Baiying Lei

Specifically, we first mapped facial expressions into dimensional measures so that we transformed facial expression analysis from a classification problem to a regression one.

General Classification regression

Structured Triplet Learning with POS-tag Guided Attention for Visual Question Answering

1 code implementation24 Jan 2018 Zhe Wang, Xiaoyi Liu, Liangjian Chen, Li-Min Wang, Yu Qiao, Xiaohui Xie, Charless Fowlkes

Visual question answering (VQA) is of significant interest due to its potential to be a strong test of image understanding systems and to probe the connection between language and vision.

Multiple-choice POS +3

Recurrent Pixel Embedding for Instance Grouping

2 code implementations CVPR 2018 Shu Kong, Charless Fowlkes

We introduce a differentiable, end-to-end trainable framework for solving pixel-level grouping problems such as instance segmentation consisting of two novel components.

Boundary Detection Instance Segmentation +2

Recurrent Scene Parsing with Perspective Understanding in the Loop

1 code implementation CVPR 2018 Shu Kong, Charless Fowlkes

We propose a depth-aware gating module that adaptively selects the pooling field size in a convolutional network architecture according to the object scale (inversely proportional to the depth) so that small details are preserved for distant objects while larger receptive fields are used for those nearby.

Monocular Depth Estimation Scene Parsing +1

Low-rank Bilinear Pooling for Fine-Grained Classification

no code implementations CVPR 2017 Shu Kong, Charless Fowlkes

To address the computational demands of high feature dimensionality, we propose to represent the covariance features as a matrix and apply a low-rank bilinear classifier.

Classification General Classification

Photo Aesthetics Ranking Network with Attributes and Content Adaptation

2 code implementations6 Jun 2016 Shu Kong, Xiaohui Shen, Zhe Lin, Radomir Mech, Charless Fowlkes

In this work, we propose to learn a deep convolutional neural network to rank photo aesthetics in which the relative ranking of photo aesthetics are directly modeled in the loss function.

Aesthetics Quality Assessment

On Image segmentation using Fractional Gradients-Learning Model Parameters using Approximate Marginal Inference

1 code implementation7 May 2016 Anish Acharya, Uddipan Mukherjee, Charless Fowlkes

Estimates of image gradients play a ubiquitous role in image segmentation and classification problems since gradients directly relate to the boundaries or the edges of a scene.

Edge Detection General Classification +2

Spatially Aware Dictionary Learning and Coding for Fossil Pollen Identification

no code implementations3 May 2016 Shu Kong, Surangi Punyasena, Charless Fowlkes

We propose a robust approach for performing automatic species-level recognition of fossil pollen grains in microscopy images that exploits both global shape and local texture characteristics in a patch-based matching methodology.

Dictionary Learning General Classification

The Open World of Micro-Videos

no code implementations31 Mar 2016 Phuc Xuan Nguyen, Gregory Rogez, Charless Fowlkes, Deva Ramanan

Micro-videos are six-second videos popular on social media networks with several unique properties.

TAG Video Understanding

Do We Need More Training Data?

no code implementations5 Mar 2015 Xiangxin Zhu, Carl Vondrick, Charless Fowlkes, Deva Ramanan

Datasets for training object recognition systems are steadily increasing in size.

Object Recognition

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