2,103 research outputs found
Class-Agnostic Counting
Nearly all existing counting methods are designed for a specific object
class. Our work, however, aims to create a counting model able to count any
class of object. To achieve this goal, we formulate counting as a matching
problem, enabling us to exploit the image self-similarity property that
naturally exists in object counting problems. We make the following three
contributions: first, a Generic Matching Network (GMN) architecture that can
potentially count any object in a class-agnostic manner; second, by
reformulating the counting problem as one of matching objects, we can take
advantage of the abundance of video data labeled for tracking, which contains
natural repetitions suitable for training a counting model. Such data enables
us to train the GMN. Third, to customize the GMN to different user
requirements, an adapter module is used to specialize the model with minimal
effort, i.e. using a few labeled examples, and adapting only a small fraction
of the trained parameters. This is a form of few-shot learning, which is
practical for domains where labels are limited due to requiring expert
knowledge (e.g. microbiology). We demonstrate the flexibility of our method on
a diverse set of existing counting benchmarks: specifically cells, cars, and
human crowds. The model achieves competitive performance on cell and crowd
counting datasets, and surpasses the state-of-the-art on the car dataset using
only three training images. When training on the entire dataset, the proposed
method outperforms all previous methods by a large margin.Comment: Asian Conference on Computer Vision (ACCV), 201
Grid Loss: Detecting Occluded Faces
Detection of partially occluded objects is a challenging computer vision
problem. Standard Convolutional Neural Network (CNN) detectors fail if parts of
the detection window are occluded, since not every sub-part of the window is
discriminative on its own. To address this issue, we propose a novel loss layer
for CNNs, named grid loss, which minimizes the error rate on sub-blocks of a
convolution layer independently rather than over the whole feature map. This
results in parts being more discriminative on their own, enabling the detector
to recover if the detection window is partially occluded. By mapping our loss
layer back to a regular fully connected layer, no additional computational cost
is incurred at runtime compared to standard CNNs. We demonstrate our method for
face detection on several public face detection benchmarks and show that our
method outperforms regular CNNs, is suitable for realtime applications and
achieves state-of-the-art performance.Comment: accepted to ECCV 201
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Active learning of an action detector on untrimmed videos
textCollecting and annotating videos of realistic human actions is tedious, yet critical for training action recognition systems. We propose a method to actively request the most useful video annotations among a large set of unlabeled videos. Predicting the utility of annotating unlabeled video is not trivial, since any given clip may contain multiple actions of interest, and it need not be trimmed to temporal regions of interest. To deal with this problem, we propose a detection-based active learner to train action category models. We develop a voting-based framework to localize likely intervals of interest in an unlabeled clip, and use them to estimate the total reduction in uncertainty that annotating that clip would yield. On three datasets, we show our approach can learn accurate action detectors more efficiently than alternative active learning strategies that fail to accommodate the "untrimmed" nature of real video data.Computer Science
CounTR: Transformer-based Generalised Visual Counting
In this paper, we consider the problem of generalised visual object counting,
with the goal of developing a computational model for counting the number of
objects from arbitrary semantic categories, using arbitrary number of
"exemplars", i.e. zero-shot or few-shot counting. To this end, we make the
following four contributions: (1) We introduce a novel transformer-based
architecture for generalised visual object counting, termed as Counting
Transformer (CounTR), which explicitly capture the similarity between image
patches or with given "exemplars" with the attention mechanism;(2) We adopt a
two-stage training regime, that first pre-trains the model with self-supervised
learning, and followed by supervised fine-tuning;(3) We propose a simple,
scalable pipeline for synthesizing training images with a large number of
instances or that from different semantic categories, explicitly forcing the
model to make use of the given "exemplars";(4) We conduct thorough ablation
studies on the large-scale counting benchmark, e.g. FSC-147, and demonstrate
state-of-the-art performance on both zero and few-shot settings.Comment: Accepted by BMVC202
Zero-Shot Object Counting with Language-Vision Models
Class-agnostic object counting aims to count object instances of an arbitrary
class at test time. It is challenging but also enables many potential
applications. Current methods require human-annotated exemplars as inputs which
are often unavailable for novel categories, especially for autonomous systems.
Thus, we propose zero-shot object counting (ZSC), a new setting where only the
class name is available during test time. This obviates the need for human
annotators and enables automated operation. To perform ZSC, we propose finding
a few object crops from the input image and use them as counting exemplars. The
goal is to identify patches containing the objects of interest while also being
visually representative for all instances in the image. To do this, we first
construct class prototypes using large language-vision models, including CLIP
and Stable Diffusion, to select the patches containing the target objects.
Furthermore, we propose a ranking model that estimates the counting error of
each patch to select the most suitable exemplars for counting. Experimental
results on a recent class-agnostic counting dataset, FSC-147, validate the
effectiveness of our method.Comment: Extended version of CVPR23 arXiv:2303.02001 . Currently under review
at T-PAM
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