Search Results for author: Davide Modolo

Found 18 papers, 1 papers with code

What to look at and where: Semantic and Spatial Refined Transformer for detecting human-object interactions

no code implementations2 Apr 2022 A S M Iftekhar, Hao Chen, Kaustav Kundu, Xinyu Li, Joseph Tighe, Davide Modolo

We propose a novel one-stage Transformer-based semantic and spatial refined transformer (SSRT) to solve the Human-Object Interaction detection task, which requires to localize humans and objects, and predicts their interactions.

Human-Object Interaction Detection

Transfer of Representations to Video Label Propagation: Implementation Factors Matter

no code implementations10 Mar 2022 Daniel McKee, Zitong Zhan, Bing Shuai, Davide Modolo, Joseph Tighe, Svetlana Lazebnik

This work studies feature representations for dense label propagation in video, with a focus on recently proposed methods that learn video correspondence using self-supervised signals such as colorization or temporal cycle consistency.

Colorization

Multi-Object Tracking with Hallucinated and Unlabeled Videos

no code implementations19 Aug 2021 Daniel McKee, Bing Shuai, Andrew Berneshawi, Manchen Wang, Davide Modolo, Svetlana Lazebnik, Joseph Tighe

Next, to tackle harder tracking cases, we mine hard examples across an unlabeled pool of real videos with a tracker trained on our hallucinated video data.

Multi-Object Tracking

MoDist: Motion Distillation for Self-supervised Video Representation Learning

no code implementations17 Jun 2021 Fanyi Xiao, Joseph Tighe, Davide Modolo

We present MoDist as a novel method to explicitly distill motion information into self-supervised video representations.

Action Detection Action Recognition +2

Selective Feature Compression for Efficient Activity Recognition Inference

no code implementations ICCV 2021 Chunhui Liu, Xinyu Li, Hao Chen, Davide Modolo, Joseph Tighe

In this work, we focus on improving the inference efficiency of current action recognition backbones on trimmed videos, and illustrate that one action model can also cover then informative region by dropping non-informative features.

Action Recognition

Multi-Object Tracking with Siamese Track-RCNN

no code implementations16 Apr 2020 Bing Shuai, Andrew G. Berneshawi, Davide Modolo, Joseph Tighe

Multi-object tracking systems often consist of a combination of a detector, a short term linker, a re-identification feature extractor and a solver that takes the output from these separate components and makes a final prediction.

Multi-Object Tracking

Combining detection and tracking for human pose estimation in videos

no code implementations CVPR 2020 Manchen Wang, Joseph Tighe, Davide Modolo

Our approach consists of three components: (i) a Clip Tracking Network that performs body joint detection and tracking simultaneously on small video clips; (ii) a Video Tracking Pipeline that merges the fixed-length tracklets produced by the Clip Tracking Network to arbitrary length tracks; and (iii) a Spatial-Temporal Merging procedure that refines the joint locations based on spatial and temporal smoothing terms.

Pose Estimation Pose Tracking

Understanding the impact of mistakes on background regions in crowd counting

no code implementations30 Mar 2020 Davide Modolo, Bing Shuai, Rahul Rama Varior, Joseph Tighe

Our results show that (i) mistakes on background are substantial and they are responsible for 18-49% of the total error, (ii) models do not generalize well to different kinds of backgrounds and perform poorly on completely background images, and (iii) models make many more mistakes than those captured by the standard Mean Absolute Error (MAE) metric, as counting on background compensates considerably for misses on foreground.

Crowd Counting

Action recognition with spatial-temporal discriminative filter banks

no code implementations ICCV 2019 Brais Martinez, Davide Modolo, Yuanjun Xiong, Joseph Tighe

In this work we focus on how to improve the representation capacity of the network, but rather than altering the backbone, we focus on improving the last layers of the network, where changes have low impact in terms of computational cost.

Ranked #20 on Action Recognition on Something-Something V1 (using extra training data)

Action Classification Action Recognition

Multi-Scale Attention Network for Crowd Counting

no code implementations17 Jan 2019 Rahul Rama Varior, Bing Shuai, Joseph Tighe, Davide Modolo

In crowd counting datasets, people appear at different scales, depending on their distance from the camera.

Crowd Counting

Objects as context for detecting their semantic parts

no code implementations CVPR 2018 Abel Gonzalez-Garcia, Davide Modolo, Vittorio Ferrari

We present a semantic part detection approach that effectively leverages object information. We use the object appearance and its class as indicators of what parts to expect.

Semantic Part Detection

Learning Semantic Part-Based Models from Google Images

no code implementations11 Sep 2016 Davide Modolo, Vittorio Ferrari

We evaluate our models on the challenging PASCAL-Part dataset [1] and show how their performance increases at every step of the learning, with the final models more than doubling the performance of directly training from images retrieved by querying for part names (from 12. 9 to 27. 2 AP).

Object Detection

Do semantic parts emerge in Convolutional Neural Networks?

no code implementations13 Jul 2016 Abel Gonzalez-Garcia, Davide Modolo, Vittorio Ferrari

We also investigate the other direction: we determine which semantic parts are the most discriminative and whether they correspond to those parts emerging in the network.

Object Recognition

Context Forest for efficient object detection with large mixture models

no code implementations3 Mar 2015 Davide Modolo, Alexander Vezhnevets, Vittorio Ferrari

We present Context Forest (ConF), a technique for predicting properties of the objects in an image based on its global appearance.

Object Detection

Joint calibration of Ensemble of Exemplar SVMs

no code implementations CVPR 2015 Davide Modolo, Alexander Vezhnevets, Olga Russakovsky, Vittorio Ferrari

We formulate joint calibration as a constrained optimization problem and devise an efficient optimization algorithm to find its global optimum.

Object Detection

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