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Found 7 papers, 3 papers with code
Generative Region-Language Pretraining for Open-Ended Object Detection
To address it, we formulate object detection as a generative problem and propose a simple framework named GenerateU, which can detect dense objects and generate their names in a free-form way.
Learning Object-Language Alignments for Open-Vocabulary Object Detection
In this paper, we propose a novel open-vocabulary object detection framework directly learning from image-text pair data.
Multimodal Transformer with Variable-length Memory for Vision-and-Language Navigation
Vision-and-Language Navigation (VLN) is a task that an agent is required to follow a language instruction to navigate to the goal position, which relies on the ongoing interactions with the environment during moving.
Domain-Invariant Disentangled Network for Generalizable Object Detection
By disentangling representations on both image and instance levels, DIDN is able to learn domain-invariant representations that are suitable for generalized object detection.
Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation
First, we generate an adapted domain to align the source and target domains on the pixel-level by improving CycleGAN with a multi-scale structured cycle-consistency loss.
Multi-source Domain Adaptation for Visual Sentiment Classification
Existing domain adaptation methods on visual sentiment classification typically are investigated under the single-source scenario, where the knowledge learned from a source domain of sufficient labeled data is transferred to the target domain of loosely labeled or unlabeled data.
Robust Multi-subspace Analysis Using Novel Column L0-norm Constrained Matrix Factorization
To tackle all these problems, we propose a method, called Matrix Factorization with Column L0-norm constraint (MFC0), that can simultaneously learn the basis for each subspace, generate a direct sparse representation for each data sample, as well as removing errors in the data in an efficient way.
