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Found 5 papers, 0 papers with code
Object-Centric Open-Vocabulary Image-Retrieval with Aggregated Features
Our approach aggregates dense embeddings extracted from CLIP into a compact representation, essentially combining the scalability of image retrieval pipelines with the object identification capabilities of dense detection methods.
Multi-Task Learning by a Top-Down Control Network
As the range of tasks performed by a general vision system expands, executing multiple tasks accurately and efficiently in a single network has become an important and still open problem.
Task-Based Top-Down Modulation Network for Multi-Task-Learning Applications
Recent approaches address this problem by a channel-wise modulation of the feature-maps along the shared backbone, with task specific vectors, manually or dynamically tuned.
Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects
An image is not just a collection of objects, but rather a graph where each object is related to other objects through spatial and semantic relations.
Deep Multi-Spectral Registration Using Invariant Descriptor Learning
Our algorithm detects corners by Harris and matches them by a patch-metric learned on top of CIFAR-10 network descriptor.
