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Found 9 papers, 7 papers with code
Benchmarking Object Detectors with COCO: A New Path Forward
With these findings, we advocate using COCO-ReM for future object detection research.
Hyperbolic Image-Text Representations
Visual and linguistic concepts naturally organize themselves in a hierarchy, where a textual concept "dog" entails all images that contain dogs.
Learning Visual Representations via Language-Guided Sampling
Our approach diverges from image-based contrastive learning by sampling view pairs using language similarity instead of hand-crafted augmentations or learned clusters.
RedCaps: web-curated image-text data created by the people, for the people
We introduce RedCaps -- a large-scale dataset of 12M image-text pairs collected from Reddit.
CASTing Your Model: Learning to Localize Improves Self-Supervised Representations
Recent advances in self-supervised learning (SSL) have largely closed the gap with supervised ImageNet pretraining.
VirTex: Learning Visual Representations from Textual Annotations
The de-facto approach to many vision tasks is to start from pretrained visual representations, typically learned via supervised training on ImageNet.
Ranked #1 on
Object Detection
on COCO test-dev
(Hardware Burden metric)
Continual Reinforcement Learning in 3D Non-stationary Environments
High-dimensional always-changing environments constitute a hard challenge for current reinforcement learning techniques.
Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering
We propose a new class of probabilistic neural-symbolic models, that have symbolic functional programs as a latent, stochastic variable.
nocaps: novel object captioning at scale
To encourage the development of image captioning models that can learn visual concepts from alternative data sources, such as object detection datasets, we present the first large-scale benchmark for this task.
