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Found 12 papers, 8 papers with code
Differentiable Product Quantization for Learning Compact Embedding Layers
Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings.
Guiding Image Captioning Models Toward More Specific Captions
We further explore the use of language models to guide the decoding process, obtaining small improvements over the Pareto frontier of reference-free vs. reference-based captioning metrics that arises from classifier-free guidance, and substantially improving the quality of captions generated from a model trained only on minimally curated web data.
FIT: Far-reaching Interleaved Transformers
We employ two types of transformer layers: local layers operate on data tokens within each group, while global layers operate on a smaller set of introduced latent tokens.
A Generalist Framework for Panoptic Segmentation of Images and Videos
Panoptic segmentation assigns semantic and instance ID labels to every pixel of an image.
A Unified Sequence Interface for Vision Tasks
Despite that, by formulating the output of each task as a sequence of discrete tokens with a unified interface, we show that one can train a neural network with a single model architecture and loss function on all these tasks, with no task-specific customization.
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding.
Ranked #17 on
Text-to-Image Generation
on MS COCO
(using extra training data)
Pix2seq: A Language Modeling Framework for Object Detection
We present Pix2Seq, a simple and generic framework for object detection.
Ranked #77 on
Object Detection
on COCO minival
(using extra training data)
Intriguing Properties of Contrastive Losses
We construct datasets with explicit and controllable competing features, and show that, for contrastive learning, a few bits of easy-to-learn shared features can suppress, and even fully prevent, the learning of other sets of competing features.
Big Bidirectional Insertion Representations for Documents
The Insertion Transformer is well suited for long form text generation due to its parallel generation capabilities, requiring $O(\log_2 n)$ generation steps to generate $n$ tokens.
Learning Compact Embedding Layers via Differentiable Product Quantization
Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings.
Differentiable Product Quantization for End-to-End Embedding Compression
Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings.
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
Increasing the batch size is a popular way to speed up neural network training, but beyond some critical batch size, larger batch sizes yield diminishing returns.
