Search Results for author:
Found 8 papers, 4 papers with code
Rethinking Class Activation Mapping for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) is a task of localizing an object in an image only using image-level labels.
Object Discovery via Contrastive Learning for Weakly Supervised Object Detection
Weakly Supervised Object Detection (WSOD) is a task that detects objects in an image using a model trained only on image-level annotations.
Ranked #1 on
Weakly Supervised Object Detection
on MS-COCO-2017
One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model
Semi-weakly supervised semantic segmentation (SWSSS) aims to train a model to identify objects in images based on a small number of images with pixel-level labels, and many more images with only image-level labels.
What and When to Look?: Temporal Span Proposal Network for Video Relation Detection
TSPN tells when to look: it simultaneously predicts start-end timestamps (i. e., temporal spans) and categories of the all possible relations by utilizing full video context.
Ranked #2 on
Video Visual Relation Detection
on ImageNet-VidVRD
Tackling the Challenges in Scene Graph Generation with Local-to-Global Interactions
To quantify how much LOGIN is aware of relational direction, a new diagnostic task called Bidirectional Relationship Classification (BRC) is also proposed.
Ranked #1 on
Scene Graph Classification
on Visual Genome
(Recall@20 metric)
Bidirectional Relationship Classification
Graph Generation
+4
LID 2020: The Learning from Imperfect Data Challenge Results
The purpose of the Learning from Imperfect Data (LID) workshop is to inspire and facilitate the research in developing novel approaches that would harness the imperfect data and improve the data-efficiency during training.
Better to Follow, Follow to Be Better: Towards Precise Supervision of Feature Super-Resolution for Small Object Detection
In spite of recent success of proposal-based CNN models for object detection, it is still difficult to detect small objects due to the limited and distorted information that small region of interests (RoI) contain.
Improving Occlusion and Hard Negative Handling for Single-Stage Pedestrian Detectors
We propose methods of addressing two critical issues of pedestrian detection: (i) occlusion of target objects as false negative failure, and (ii) confusion with hard negative examples like vertical structures as false positive failure.
