#### Filter Results:

- Full text PDF available (34)

#### Publication Year

2004

2017

- This year (3)
- Last 5 years (19)
- Last 10 years (32)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Data Set Used

#### Key Phrases

Learn More

- Ariadna Quattoni, Antonio Torralba
- 2009 IEEE Conference on Computer Vision and…
- 2009

Indoor scene recognition is a challenging open problem in high level vision. Most scene recognition models that work well for outdoor scenes perform poorly in the indoor domain. The main difficulty is that while some indoor scenes (e.g. corridors) can be well characterized by global spatial properties, others (e.g, bookstores) are better characterized by… (More)

- Ariadna Quattoni, Sy Bor Wang, Louis-Philippe Morency, Michael Collins, Trevor Darrell
- IEEE Transactions on Pattern Analysis and Machine…
- 2007

We present a discriminative latent variable model for classification problems in structured domains where inputs can be represented by a graph of local observations. A hidden-state conditional random field framework learns a set of latent variables conditioned on local features. Observations need not be independent and may overlap in space and time.

- Louis-Philippe Morency, Ariadna Quattoni, Trevor Darrell
- 2007 IEEE Conference on Computer Vision and…
- 2007

Many problems in vision involve the prediction of a class label for each frame in an unsegmented sequence. In this paper, we develop a discriminative framework for simultaneous sequence segmentation and labeling which can capture both intrinsic and extrinsic class dynamics. Our approach incorporates hidden state variables which model the sub-structure of a… (More)

- Sy Bor Wang, Ariadna Quattoni, Louis-Philippe Morency, David Demirdjian, Trevor Darrell
- 2006 IEEE Computer Society Conference on Computer…
- 2006

We introduce a discriminative hidden-state approach for the recognition of human gestures. Gesture sequences often have a complex underlying structure, and models that can incorporate hidden structures have proven to be advantageous for recognition tasks. Most existing approaches to gesture recognition with hidden states employ a Hidden Markov Model or… (More)

- Ariadna Quattoni, Michael Collins, Trevor Darrell
- NIPS
- 2004

We present a discriminative part-based approach for the recognition of object classes from unsegmented cluttered scenes. Objects are modeled as flexible constellations of parts conditioned on local observations found by an interest operator. For each object class the probability of a given assignment of parts to local features is modeled by a Conditional… (More)

In recent years the l1,∞ norm has been proposed for joint regularization. In essence, this type of regularization aims at extending the l1 framework for learning sparse models to a setting where the goal is to learn a set of jointly sparse models. In this paper we derive a simple and effective projected gradient method for optimization of l1,∞ regularized… (More)

We present a discriminative latent variable model for classification problems in structured domains where inputs can be represented by a graph of local observations. A hidden-state Conditional Random Field framework learns a set of latent variables conditioned on local features. Observations need not be independent and may overlap in space and time. We… (More)

- Ariadna Quattoni, Michael Collins, Trevor Darrell
- 2008 IEEE Conference on Computer Vision and…
- 2008

To learn a new visual category from few examples, prior knowledge from unlabeled data as well as previous related categories may be useful. We develop a new method for transfer learning which exploits available unlabeled data and an arbitrary kernel function; we form a representation based on kernel distances to a large set of unlabeled data points. To… (More)

- Edgar Simo-Serra, Ariadna Quattoni, Carme Torras, Francesc Moreno-Noguer
- 2013 IEEE Conference on Computer Vision and…
- 2013

We introduce a novel approach to automatically recover 3D human pose from a single image. Most previous work follows a pipelined approach: initially, a set of 2D features such as edges, joints or silhouettes are detected in the image, and then these observations are used to infer the 3D pose. Solving these two problems separately may lead to erroneous 3D… (More)

- Borja Balle, Ariadna Quattoni, Xavier Carreras
- ICML
- 2012

This paper re-visits the spectral method for learning latent variable models defined in terms of observable operators. We give a new perspective on the method, showing that operators can be recovered by minimizing a loss defined on a finite subset of the domain. This leads to a derivation of a non-convex optimization similar to the spectral method. We also… (More)