Search Results for author:
Found 30 papers, 6 papers with code
SiamMOT: Siamese Multi-Object Tracking
In this paper, we focus on improving online multi-object tracking (MOT).
TubeR: Tubelet Transformer for Video Action Detection
We propose TubeR: a simple solution for spatio-temporal video action detection.
Semi-supervised Vision Transformers at Scale
We study semi-supervised learning (SSL) for vision transformers (ViT), an under-explored topic despite the wide adoption of the ViT architectures to different tasks.
MaCLR: Motion-aware Contrastive Learning of Representations for Videos
We present MaCLR, a novel method to explicitly perform cross-modal self-supervised video representations learning from visual and motion modalities.
Self-Supervised Multi-Object Tracking with Path Consistency
In this paper, we propose a novel concept of path consistency to learn robust object matching without using manual object identity supervision.
Objects as context for detecting their semantic parts
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.
Do semantic parts emerge in Convolutional Neural Networks?
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.
Learning Semantic Part-Based Models from Google Images
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).
Joint calibration of Ensemble of Exemplar SVMs
We formulate joint calibration as a constrained optimization problem and devise an efficient optimization algorithm to find its global optimum.
Context Forest for efficient object detection with large mixture models
We present Context Forest (ConF), a technique for predicting properties of the objects in an image based on its global appearance.
Multi-Scale Attention Network for Crowd Counting
In crowd counting datasets, people appear at different scales, depending on their distance from the camera.
Action recognition with spatial-temporal discriminative filter banks
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 #36 on
Action Recognition
on Something-Something V1
(using extra training data)
Understanding the impact of mistakes on background regions in crowd counting
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.
Combining detection and tracking for human pose estimation in videos
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.
Ranked #1 on
Pose Tracking
on PoseTrack2017
Multi-Object Tracking with Siamese Track-RCNN
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.
Selective Feature Compression for Efficient Activity Recognition Inference
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.
Multi-Object Tracking with Hallucinated and Unlabeled Videos
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.
Transfer of Representations to Video Label Propagation: Implementation Factors Matter
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.
What to look at and where: Semantic and Spatial Refined Transformer for detecting human-object interactions
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.
Hierarchical Self-supervised Representation Learning for Movie Understanding
Most self-supervised video representation learning approaches focus on action recognition.
SCVRL: Shuffled Contrastive Video Representation Learning
We propose SCVRL, a novel contrastive-based framework for self-supervised learning for videos.
Musketeer: Joint Training for Multi-task Vision Language Model with Task Explanation Prompts
We present a vision-language model whose parameters are jointly trained on all tasks and fully shared among multiple heterogeneous tasks which may interfere with each other, resulting in a single model which we named Musketeer.
ScaleDet: A Scalable Multi-Dataset Object Detector
Our results show that ScaleDet achieves compelling strong model performance with an mAP of 50. 7 on LVIS, 58. 8 on COCO, 46. 8 on Objects365, 76. 2 on OpenImages, and 71. 8 on ODinW, surpassing state-of-the-art detectors with the same backbone.
Ranked #1 on
Object Detection
on OpenImages-v6
(using extra training data)
Benchmarking Zero-Shot Recognition with Vision-Language Models: Challenges on Granularity and Specificity
This paper introduces innovative benchmarks to evaluate Vision-Language Models (VLMs) in real-world zero-shot recognition tasks, focusing on the granularity and specificity of prompting text.
SkeleTR: Towrads Skeleton-based Action Recognition in the Wild
It first models the intra-person skeleton dynamics for each skeleton sequence with graph convolutions, and then uses stacked Transformer encoders to capture person interactions that are important for action recognition in general scenarios.
SkeleTR: Towards Skeleton-based Action Recognition in the Wild
It first models the intra-person skeleton dynamics for each skeleton sequence with graph convolutions, and then uses stacked Transformer encoders to capture person interactions that are important for action recognition in the wild.
Ranked #2 on
Human Interaction Recognition
on NTU RGB+D
Denoising and Selecting Pseudo-Heatmaps for Semi-Supervised Human Pose Estimation
First, we introduce a denoising scheme to generate reliable pseudo-heatmaps as targets for learning from unlabeled data.
SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processes
We show that SemiGPC improves performance when paired with different Semi-Supervised methods such as FixMatch, ReMixMatch, SimMatch and FreeMatch and different pre-training strategies including MSN and Dino.
Early Action Recognition with Action Prototypes
For example, on early recognition observing only the first 10% of each video, our method improves the SOTA by +2. 23 Top-1 accuracy on Something-Something-v2, +3. 55 on UCF-101, +3. 68 on SSsub21, and +5. 03 on EPIC-Kitchens-55, where prior work used either multi-modal inputs (e. g. optical-flow) or batched inference.
Hyperbolic Learning with Synthetic Captions for Open-World Detection
Specifically, we bootstrap dense synthetic captions using pre-trained VLMs to provide rich descriptions on different regions in images, and incorporate these captions to train a novel detector that generalizes to novel concepts.
