5 accepted papers
Most current captioning systems use language models trained on data from specific settings, such as image-based captioning via Amazon Mechanical Turk, limiting their ability to generalize to other modality distributions and contexts. This limitation hinders performance in tasks like audio or video c…
In the weakly supervised localization setting, supervision is given as an image-level label. We propose employing an image classifier f and training a generative network g that outputs, given the input image, a per-pixel weight map that indicates the location of the object within the image. Network…
A phrase grounding model receives an input image and a text phrase and outputs a suitable localization map. We present an effective way to refine a phrase ground model by considering self-similarity maps extracted from the latent representation of the model's image encoder. Our main insights are tha…
Given an input image, and nothing else, our method returns the bounding boxes of objects in the image and phrases that describe the objects. This is achieved within an open world paradigm, in which the objects in the input image may not have been encountered during the training of the localization m…
We present an image segmentation method that iteratively evolves a polygon. At each iteration, the vertices of the polygon are displaced based on the local value of a 2D shift map that is inferred from the input image via an encoder-decoder architecture. The main training loss that is used is the di…