Add to ORKGOne of the main challenges for scaling up object recognition systems is the lack of annotated images for real-world categories. It is estimated that humans can recognize and discriminate among about 30,000 categories [4]. Typically there are few images avail
Most current image categorization methods require large collections of man-ually annotated training ...
textVisual recognition research develops algorithms and representations to autonomously recognize vi...
textVisual recognition research develops algorithms and representations to autonomously recognize vi...
In pattern recognition and computer vision, one is often faced with scenarios where the training dat...
Abstract. In this paper we report the contribution of XRCE team to the Domain Adaptation Challenge [...
In visual recognition problems, the common data distribution mismatches between training and testing...
Recent studies have shown that recognition datasets are biased. Paying no heed to those biases, lear...
Recent studies have shown that recognition datasets are biased. Paying no heed to those biases, lear...
Abstract. Domain adaptation is an important emerging topic in com-puter vision. In this paper, we pr...
In many visual recognition tasks, the domain distribution mismatch between the training set (i.e., s...
Machine learning has achieved great successes in the area of computer vision, especially in object r...
We address various issues in learning and representation of visual object categories. A key componen...
Together with the development of deep neural networks, artificial intelligence is getting unpreceden...
Most successful object classification and detection meth-ods rely on classifiers trained on large la...
Artificial intelligent and machine learning technologies have already achieved significant success i...
Most current image categorization methods require large collections of man-ually annotated training ...
textVisual recognition research develops algorithms and representations to autonomously recognize vi...
textVisual recognition research develops algorithms and representations to autonomously recognize vi...
In pattern recognition and computer vision, one is often faced with scenarios where the training dat...
Abstract. In this paper we report the contribution of XRCE team to the Domain Adaptation Challenge [...
In visual recognition problems, the common data distribution mismatches between training and testing...
Recent studies have shown that recognition datasets are biased. Paying no heed to those biases, lear...
Recent studies have shown that recognition datasets are biased. Paying no heed to those biases, lear...
Abstract. Domain adaptation is an important emerging topic in com-puter vision. In this paper, we pr...
In many visual recognition tasks, the domain distribution mismatch between the training set (i.e., s...
Machine learning has achieved great successes in the area of computer vision, especially in object r...
We address various issues in learning and representation of visual object categories. A key componen...
Together with the development of deep neural networks, artificial intelligence is getting unpreceden...
Most successful object classification and detection meth-ods rely on classifiers trained on large la...
Artificial intelligent and machine learning technologies have already achieved significant success i...
Most current image categorization methods require large collections of man-ually annotated training ...
textVisual recognition research develops algorithms and representations to autonomously recognize vi...
textVisual recognition research develops algorithms and representations to autonomously recognize vi...