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Found 9 papers, 4 papers with code
HAAV: Hierarchical Aggregation of Augmented Views for Image Captioning
The image captioning model encodes each view independently with a shared encoder efficiently, and a contrastive loss is incorporated across the encoded views in a novel way to improve their representation quality and the model's data efficiency.
CLIP-GCD: Simple Language Guided Generalized Category Discovery
Generalized Category Discovery (GCD) requires a model to both classify known categories and cluster unknown categories in unlabeled data.
Structure-Encoding Auxiliary Tasks for Improved Visual Representation in Vision-and-Language Navigation
In Vision-and-Language Navigation (VLN), researchers typically take an image encoder pre-trained on ImageNet without fine-tuning on the environments that the agent will be trained or tested on.
Beyond a Pre-Trained Object Detector: Cross-Modal Textual and Visual Context for Image Captioning
A key limitation of such methods, however, is that the output of the model is conditioned only on the object detector's outputs.
Ranked #12 on
Image Captioning
on COCO Captions
Unbiased Teacher for Semi-Supervised Object Detection
To address this, we introduce Unbiased Teacher, a simple yet effective approach that jointly trains a student and a gradually progressing teacher in a mutually-beneficial manner.
FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning
Recent state-of-the-art semi-supervised learning (SSL) methods use a combination of image-based transformations and consistency regularization as core components.
Who2com: Collaborative Perception via Learnable Handshake Communication
In this paper, we propose the problem of collaborative perception, where robots can combine their local observations with those of neighboring agents in a learnable way to improve accuracy on a perception task.
Manifold Graph with Learned Prototypes for Semi-Supervised Image Classification
We then show that when combined with these regularizers, the proposed method facilitates the propagation of information from generated prototypes to image data to further improve results.
Data-Efficient Graph Embedding Learning for PCB Component Detection
This paper presents a challenging computer vision task, namely the detection of generic components on a PCB, and a novel set of deep-learning methods that are able to jointly leverage the appearance of individual components and the propagation of information across the structure of the board to accurately detect and identify various types of components on a PCB.
