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Found 6 papers, 1 papers with code
All You Need is a Second Look: Towards Arbitrary-Shaped Text Detection
Compared to the traditional single-stage segmentation network, our NASK conducts the detection in a coarse-to-fine manner with the first stage segmentation spotting the rectangle text proposals and the second one retrieving compact representations.
RR-Net: Injecting Interactive Semantics in Human-Object Interaction Detection
Upon the frame, an Interaction Intensifier Module and a Correlation Parsing Module are carefully designed, where: a) interactive semantics from humans can be exploited and passed to objects to intensify interactions, b) interactive correlations among humans, objects and interactions are integrated to promote predictions.
CoLA: Weakly-Supervised Temporal Action Localization with Snippet Contrastive Learning
In this paper, we argue that learning by comparing helps identify these hard snippets and we propose to utilize snippet Contrastive learning to Localize Actions, CoLA for short.
A Graph-based Interactive Reasoning for Human-Object Interaction Detection
However, recent HOI detection methods mostly rely on additional annotations (e. g., human pose) and neglect powerful interactive reasoning beyond convolutions.
GID-Net: Detecting Human-Object Interaction with Global and Instance Dependency
GID block breaks through the local neighborhoods and captures long-range dependency of pixels both in global-level and instance-level from the scene to help detecting interactions between instances.
C-RPNs: Promoting Object Detection in real world via a Cascade Structure of Region Proposal Networks
Although two-stage detectors like Faster R-CNN achieved big successes in object detection due to the strategy of extracting region proposals by region proposal network, they show their poor adaption in real-world object detection as a result of without considering mining hard samples during extracting region proposals.
