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Found 28 papers, 12 papers with code
6-DoF Stability Field via Diffusion Models
A core capability for robot manipulation is reasoning over where and how to stably place objects in cluttered environments.
Self-Supervised Video Transformers for Isolated Sign Language Recognition
This paper presents an in-depth analysis of various self-supervision methods for isolated sign language recognition (ISLR).
NeRFuser: Large-Scale Scene Representation by NeRF Fusion
A practical benefit of implicit visual representations like Neural Radiance Fields (NeRFs) is their memory efficiency: large scenes can be efficiently stored and shared as small neural nets instead of collections of images.
Classification Confidence Estimation with Test-Time Data-Augmentation
This suggests the task of detecting errors, which we tackle in this paper for the case of visual classification.
Analysis of diversity-accuracy tradeoff in image captioning
We investigate the effect of different model architectures, training objectives, hyperparameter settings and decoding procedures on the diversity of automatically generated image captions.
DIODE: A Dense Indoor and Outdoor DEpth Dataset
We introduce DIODE, a dataset that contains thousands of diverse high resolution color images with accurate, dense, long-range depth measurements.
Natural and Adversarial Error Detection using Invariance to Image Transformations
Our approach is based on the observation that correctly classified images tend to exhibit robust and consistent classifications under certain image transformations (e. g., horizontal flip, small image translation, etc.).
Regularizing Deep Networks by Modeling and Predicting Label Structure
Our technique is applicable when the ground-truth labels themselves exhibit internal structure; we derive a regularizer by learning an autoencoder over the set of annotations.
Confidence from Invariance to Image Transformations
We develop a technique for automatically detecting the classification errors of a pre-trained visual classifier.
Discriminability objective for training descriptive captions
One property that remains lacking in image captions generated by contemporary methods is discriminability: being able to tell two images apart given the caption for one of them.
Semantic speech retrieval with a visually grounded model of untranscribed speech
We introduce a newly collected data set of human semantic relevance judgements and an associated task, semantic speech retrieval, where the goal is to search for spoken utterances that are semantically relevant to a given text query.
Training Deep Networks to be Spatially Sensitive
In many computer vision tasks, for example saliency prediction or semantic segmentation, the desired output is a foreground map that predicts pixels where some criteria is satisfied.
Visually grounded learning of keyword prediction from untranscribed speech
In this setting of images paired with untranscribed spoken captions, we consider whether computer vision systems can be used to obtain textual labels for the speech.
Comprehension-guided referring expressions
Second, we use the comprehension module in a generate-and-rerank pipeline, which chooses from candidate expressions generated by a model according to their performance on the comprehension task.
Diverse Sampling for Self-Supervised Learning of Semantic Segmentation
We propose an approach for learning category-level semantic segmentation purely from image-level classification tags indicating presence of categories.
Examining the Impact of Blur on Recognition by Convolutional Networks
We investigate the extent to which this degradation is due to the mismatch between training and input image statistics.
Lexicon-Free Fingerspelling Recognition from Video: Data, Models, and Signer Adaptation
Recognizing fingerspelling is challenging for a number of reasons: It involves quick, small motions that are often highly coarticulated; it exhibits significant variation between signers; and there has been a dearth of continuous fingerspelling data collected.
FractalNet: Ultra-Deep Neural Networks without Residuals
We introduce a design strategy for neural network macro-architecture based on self-similarity.
Ranked #29 on
Image Classification
on SVHN
Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions
A single color image can contain many cues informative towards different aspects of local geometric structure.
Learning Representations for Automatic Colorization
This intermediate output can be used to automatically generate a color image, or further manipulated prior to image formation.
Feedforward semantic segmentation with zoom-out features
We introduce a purely feed-forward architecture for semantic segmentation.
Discriminative Re-ranking of Diverse Segmentations
This paper introduces a two-stage approach to semantic image segmentation.
Part Discovery from Partial Correspondence
We study the problem of part discovery when partial correspondence between instances of a category are available.
Image Segmentation by Cascaded Region Agglomeration
We propose a hierarchical segmentation algorithm that starts with a very fine oversegmentation and gradually merges regions using a cascade of boundary classifiers.
Sparse Coding for Learning Interpretable Spatio-Temporal Primitives
Sparse coding has recently become a popular approach in computer vision to learn dictionaries of natural images.
