CVPR 2015

The most popular implementations from this conference
1
Card image cap
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
2
Card image cap
Show and Tell: A Neural Image Caption Generator
Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69.
3
Card image cap
Fully Convolutional Networks for Semantic Segmentation
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation.
4
Card image cap
Convolutional Feature Masking for Joint Object and Stuff Segmentation
The current leading approaches for semantic segmentation exploit shape information by extracting CNN features from masked image regions. In this paper, we propose to exploit shape information via masking convolutional features.
5
Card image cap
Show and Tell: A Neural Image Caption Generator
Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69.
6
Show and Tell: A Neural Image Caption Generator
Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69.
7
Card image cap
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
8
Card image cap
Deformable Part Models are Convolutional Neural Networks
Deformable part models (DPMs) and convolutional neural networks (CNNs) are two widely used tools for visual recognition. They are typically viewed as distinct approaches: DPMs are graphical models (Markov random fields), while CNNs are "black-box" non-linear classifiers.
9
Card image cap
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
10
Show and Tell: A Neural Image Caption Generator
Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69.
11
Card image cap
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or "temporally deep", are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and demonstrate the value of these models on benchmark video recognition tasks, image description and retrieval problems, and video narration challenges.
12
Card image cap
Fully Convolutional Networks for Semantic Segmentation
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation.
13
Card image cap
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
14
Card image cap
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
15
Card image cap
Fully Convolutional Networks for Semantic Segmentation
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation.
16
Card image cap
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
17
Card image cap
Fully Convolutional Networks for Semantic Segmentation
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation.
18
Card image cap
Understanding Deep Image Representations by Inverting Them
Image representations, from SIFT and Bag of Visual Words to Convolutional Neural Networks (CNNs), are a crucial component of almost any image understanding system. Nevertheless, our understanding of them remains limited.
19
Card image cap
Show and Tell: A Neural Image Caption Generator
Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69.
20
Card image cap
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
21
Card image cap
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
22
Card image cap
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or "temporally deep", are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and demonstrate the value of these models on benchmark video recognition tasks, image description and retrieval problems, and video narration challenges.
23
Card image cap
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
24
Card image cap
Pooled Motion Features for First-Person Videos
In this paper, we present a new feature representation for first-person videos. We also confirm that our feature representation has superior performance to existing state-of-the-art features like local spatio-temporal features and Improved Trajectory Features (originally developed for 3rd-person videos) when handling first-person videos.
25
Card image cap
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or "temporally deep", are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and demonstrate the value of these models on benchmark video recognition tasks, image description and retrieval problems, and video narration challenges.
26
Card image cap
Show and Tell: A Neural Image Caption Generator
Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69.
27
Card image cap
From Captions to Visual Concepts and Back
We use multiple instance learning to train visual detectors for words that commonly occur in captions, including many different parts of speech such as nouns, verbs, and adjectives. The language model learns from a set of over 400,000 image descriptions to capture the statistics of word usage.
28
Card image cap
The Treasure beneath Convolutional Layers: Cross-convolutional-layer Pooling for Image Classification
A number of recent studies have shown that a Deep Convolutional Neural Network (DCNN) pretrained on a large dataset can be adopted as a universal image description which leads to astounding performance in many visual classification tasks. This paper, however, advocates that if used appropriately convolutional layer activations can be turned into a powerful image representation which enjoys many advantages over fully-connected layer activations.
29
Card image cap
Fully Convolutional Networks for Semantic Segmentation
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation.
30
Card image cap
Fully Convolutional Networks for Semantic Segmentation
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation.
31
Card image cap
A Large-Scale Car Dataset for Fine-Grained Categorization and Verification
Updated on 24/09/2015: This update provides preliminary experiment results for fine-grained classification on the surveillance data of CompCars. The train/test splits are provided in the updated dataset.
32
Card image cap
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
33
Card image cap
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
34
Card image cap
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
35
Card image cap
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
36
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
37
Card image cap
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
38
Card image cap
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
39
Card image cap
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
40
Card image cap
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
41
Card image cap
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
42
Card image cap
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
43
Card image cap
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
44
Card image cap
FaceNet: A Unified Embedding for Face Recognition and Clustering
Despite significant recent advances in the field of face recognition, implementing face verification and recognition efficiently at scale presents serious challenges to current approaches. On the widely used Labeled Faces in the Wild (LFW) dataset, our system achieves a new record accuracy of 99.63%.
45
Card image cap
Efficient Object Localization Using Convolutional Networks
Recent state-of-the-art performance on human-body pose estimation has been achieved with Deep Convolutional Networks (ConvNets). Traditional ConvNet architectures include pooling and sub-sampling layers which reduce computational requirements, introduce invariance and prevent over-training.
46
Card image cap
Show and Tell: A Neural Image Caption Generator
Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69.
47
Card image cap
Show and Tell: A Neural Image Caption Generator
Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69.
48
Card image cap
Show and Tell: A Neural Image Caption Generator
Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69.
49
Card image cap
Show and Tell: A Neural Image Caption Generator
Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69.
50
Card image cap
Show and Tell: A Neural Image Caption Generator
Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69.
51
Card image cap
Show and Tell: A Neural Image Caption Generator
Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69.
52
Card image cap
Show and Tell: A Neural Image Caption Generator
Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69.
53
Card image cap
Show and Tell: A Neural Image Caption Generator
Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69.
54
Card image cap
Show and Tell: A Neural Image Caption Generator
Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69.
55
Card image cap
Appearance-Based Gaze Estimation in the Wild
Appearance-based gaze estimation is believed to work well in real-world settings, but existing datasets have been collected under controlled laboratory conditions and methods have been not evaluated across multiple datasets. In this work we study appearance-based gaze estimation in the wild.
56
Card image cap
Appearance-Based Gaze Estimation in the Wild
Appearance-based gaze estimation is believed to work well in real-world settings, but existing datasets have been collected under controlled laboratory conditions and methods have been not evaluated across multiple datasets. In this work we study appearance-based gaze estimation in the wild.
57
Card image cap
Understanding Deep Image Representations by Inverting Them
Image representations, from SIFT and Bag of Visual Words to Convolutional Neural Networks (CNNs), are a crucial component of almost any image understanding system. Nevertheless, our understanding of them remains limited.