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Found 15 papers, 4 papers with code
BootPIG: Bootstrapping Zero-shot Personalized Image Generation Capabilities in Pretrained Diffusion Models
We propose a novel architecture (BootPIG) that allows a user to provide reference images of an object in order to guide the appearance of a concept in the generated images.
Diffusion Model Alignment Using Direct Preference Optimization
Large language models (LLMs) are fine-tuned using human comparison data with Reinforcement Learning from Human Feedback (RLHF) methods to make them better aligned with users' preferences.
XGen-7B Technical Report
Most open-source LLMs, on the other hand, are limited in their ability to support longer sequence lengths, which is a key requirement for many tasks that require inference over an input context.
The Challenges of Continuous Self-Supervised Learning
As a result, SSL holds the promise to learn representations from data in-the-wild, i. e., without the need for finite and static datasets.
The Unsurprising Effectiveness of Pre-Trained Vision Models for Control
In this context, we revisit and study the role of pre-trained visual representations for control, and in particular representations trained on large-scale computer vision datasets.
The Functional Correspondence Problem
For example, what are the correspondences between a bottle and shoe for the task of pounding or the task of pouring.
Audio-Visual Floorplan Reconstruction
Given only a few glimpses of an environment, how much can we infer about its entire floorplan?
Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases
Second, we demonstrate that these approaches obtain further gains from access to a clean object-centric training dataset like Imagenet.
Aligning Videos in Space and Time
During training, given a pair of videos, we compute cycles that connect patches in a given frame in the first video by matching through frames in the second video.
Task-Driven Modular Networks for Zero-Shot Compositional Learning
When extending the evaluation to the generalized setting which accounts also for pairs seen during training, we discover that naive baseline methods perform similarly or better than current approaches.
Bounce and Learn: Modeling Scene Dynamics with Real-World Bounces
To achieve our results, we introduce the Bounce Dataset comprising 5K RGB-D videos of bouncing trajectories of a foam ball to probe surfaces of varying shapes and materials in everyday scenes including homes and offices.
Pose from Action: Unsupervised Learning of Pose Features based on Motion
We propose an unsupervised method to learn pose features from videos that exploits a signal which is complementary to appearance and can be used as supervision: motion.
Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles
Many practical perception systems exist within larger processes that include interactions with users or additional components capable of evaluating the quality of predicted solutions.
Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks
Convolutional Neural Networks have achieved state-of-the-art performance on a wide range of tasks.
Combining the Best of Graphical Models and ConvNets for Semantic Segmentation
We present a two-module approach to semantic segmentation that incorporates Convolutional Networks (CNNs) and Graphical Models.
