122,651 research outputs found
Combining Language and Vision with a Multimodal Skip-gram Model
We extend the SKIP-GRAM model of Mikolov et al. (2013a) by taking visual
information into account. Like SKIP-GRAM, our multimodal models (MMSKIP-GRAM)
build vector-based word representations by learning to predict linguistic
contexts in text corpora. However, for a restricted set of words, the models
are also exposed to visual representations of the objects they denote
(extracted from natural images), and must predict linguistic and visual
features jointly. The MMSKIP-GRAM models achieve good performance on a variety
of semantic benchmarks. Moreover, since they propagate visual information to
all words, we use them to improve image labeling and retrieval in the zero-shot
setup, where the test concepts are never seen during model training. Finally,
the MMSKIP-GRAM models discover intriguing visual properties of abstract words,
paving the way to realistic implementations of embodied theories of meaning.Comment: accepted at NAACL 2015, camera ready version, 11 page
Cross-Modal Concept Learning and Inference for Vision-Language Models
Large-scale pre-trained Vision-Language Models (VLMs), such as CLIP,
establish the correlation between texts and images, achieving remarkable
success on various downstream tasks with fine-tuning. In existing fine-tuning
methods, the class-specific text description is matched against the whole
image. We recognize that this whole image matching is not effective since
images from the same class often contain a set of different semantic objects,
and an object further consists of a set of semantic parts or concepts.
Individual semantic parts or concepts may appear in image samples from
different classes. To address this issue, in this paper, we develop a new
method called cross-model concept learning and inference (CCLI). Using the
powerful text-image correlation capability of CLIP, our method automatically
learns a large set of distinctive visual concepts from images using a set of
semantic text concepts. Based on these visual concepts, we construct a
discriminative representation of images and learn a concept inference network
to perform downstream image classification tasks, such as few-shot learning and
domain generalization. Extensive experimental results demonstrate that our CCLI
method is able to improve the performance upon the current state-of-the-art
methods by large margins, for example, by up to 8.0% improvement on few-shot
learning and by up to 1.3% for domain generalization
Detecting Semantic Parts on Partially Occluded Objects
In this paper, we address the task of detecting semantic parts on partially
occluded objects. We consider a scenario where the model is trained using
non-occluded images but tested on occluded images. The motivation is that there
are infinite number of occlusion patterns in real world, which cannot be fully
covered in the training data. So the models should be inherently robust and
adaptive to occlusions instead of fitting / learning the occlusion patterns in
the training data. Our approach detects semantic parts by accumulating the
confidence of local visual cues. Specifically, the method uses a simple voting
method, based on log-likelihood ratio tests and spatial constraints, to combine
the evidence of local cues. These cues are called visual concepts, which are
derived by clustering the internal states of deep networks. We evaluate our
voting scheme on the VehicleSemanticPart dataset with dense part annotations.
We randomly place two, three or four irrelevant objects onto the target object
to generate testing images with various occlusions. Experiments show that our
algorithm outperforms several competitors in semantic part detection when
occlusions are present.Comment: Accepted to BMVC 2017 (13 pages, 3 figures
Everyday concept detection in visual lifelogs: validation, relationships and trends
The Microsoft SenseCam is a small lightweight wearable camera used to passively capture photos and other sensor readings from a user's day-to-day activities. It can capture up to 3,000 images per day, equating to almost 1 million images per year. It is used to aid memory by creating a personal multimedia lifelog, or visual recording of the wearer's life. However the sheer volume of image data captured within a visual lifelog creates a number of challenges, particularly for locating relevant content. Within this work, we explore the applicability of semantic concept detection, a method often used within video retrieval, on the novel domain of visual lifelogs. A concept detector models the correspondence between low-level visual features and high-level semantic concepts (such as indoors, outdoors, people, buildings, etc.) using supervised machine learning. By doing so it determines the probability of a concept's presence. We apply detection of 27 everyday semantic concepts on a lifelog collection composed of 257,518 SenseCam images from 5 users. The results were then evaluated on a subset of 95,907 images, to determine the precision for detection of each semantic concept. We conduct further analysis on the temporal consistency, co-occurance and trends within the detected concepts to more extensively investigate the robustness of the detectors within this novel domain. We additionally present future applications of concept detection within the domain of lifelogging
Concept Generalization in Visual Representation Learning
Measuring concept generalization, i.e., the extent to which models trained on
a set of (seen) visual concepts can be used to recognize a new set of (unseen)
concepts, is a popular way of evaluating visual representations, especially
when they are learned with self-supervised learning. Nonetheless, the choice of
which unseen concepts to use is usually made arbitrarily, and independently
from the seen concepts used to train representations, thus ignoring any
semantic relationships between the two. In this paper, we argue that semantic
relationships between seen and unseen concepts affect generalization
performance and propose ImageNet-CoG, a novel benchmark on the ImageNet dataset
that enables measuring concept generalization in a principled way. Our
benchmark leverages expert knowledge that comes from WordNet in order to define
a sequence of unseen ImageNet concept sets that are semantically more and more
distant from the ImageNet-1K subset, a ubiquitous training set. This allows us
to benchmark visual representations learned on ImageNet-1K out-of-the box: we
analyse a number of such models from supervised, semi-supervised and
self-supervised approaches under the prism of concept generalization, and show
how our benchmark is able to uncover a number of interesting insights. We will
provide resources for the benchmark at
https://europe.naverlabs.com/cog-benchmark
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