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Found 14 papers, 6 papers with code
A convolutional approach to reflection symmetry
We present a convolutional approach to reflection symmetry detection in 2D.
Unsupervised Learning of Disentangled Representations from Video
We present a new model DrNET that learns disentangled image representations from video.
Semantic Redundancies in Image-Classification Datasets: The 10% You Don't Need
Large datasets have been crucial to the success of deep learning models in the recent years, which keep performing better as they are trained with more labelled data.
Straight to the point: reinforcement learning for user guidance in ultrasound
Point of care ultrasound (POCUS) consists in the use of ultrasound imaging in critical or emergency situations to support clinical decisions by healthcare professionals and first responders.
A Closed-Form Learned Pooling for Deep Classification Networks
This operator can learn a strict super-set of what can be learned by average pooling or convolutions.
The surprising impact of mask-head architecture on novel class segmentation
Under this family, we study Mask R-CNN and discover that instead of its default strategy of training the mask-head with a combination of proposals and groundtruth boxes, training the mask-head with only groundtruth boxes dramatically improves its performance on novel classes.
The iWildCam 2021 Competition Dataset
The challenge is to classify species and count individual animals across sequences in the test cameras.
Less is More: Generating Grounded Navigation Instructions from Landmarks
We study the automatic generation of navigation instructions from 360-degree images captured on indoor routes.
Proper Reuse of Image Classification Features Improves Object Detection
A common practice in transfer learning is to initialize the downstream model weights by pre-training on a data-abundant upstream task.
Open-Vocabulary Temporal Action Detection with Off-the-Shelf Image-Text Features
Detecting actions in untrimmed videos should not be limited to a small, closed set of classes.
Scaling Vision Transformers to 22 Billion Parameters
The scaling of Transformers has driven breakthrough capabilities for language models.
Ranked #1 on
Zero-Shot Transfer Image Classification
on ObjectNet
Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation
While Large Language Models (LLMs) are the dominant models for generative tasks in language, they do not perform as well as diffusion models on image and video generation.
Ranked #2 on
Video Prediction
on Kinetics-600 12 frames, 64x64
Text and Click inputs for unambiguous open vocabulary instance segmentation
We propose a new segmentation process, Text + Click segmentation, where a model takes as input an image, a text phrase describing a class to segment, and a single foreground click specifying the instance to segment.
VideoPoet: A Large Language Model for Zero-Shot Video Generation
We present VideoPoet, a language model capable of synthesizing high-quality video, with matching audio, from a large variety of conditioning signals.
Ranked #3 on
Text-to-Video Generation
on MSR-VTT
