19 research outputs found
Expert Gate: Lifelong Learning with a Network of Experts
In this paper we introduce a model of lifelong learning, based on a Network
of Experts. New tasks / experts are learned and added to the model
sequentially, building on what was learned before. To ensure scalability of
this process,data from previous tasks cannot be stored and hence is not
available when learning a new task. A critical issue in such context, not
addressed in the literature so far, relates to the decision which expert to
deploy at test time. We introduce a set of gating autoencoders that learn a
representation for the task at hand, and, at test time, automatically forward
the test sample to the relevant expert. This also brings memory efficiency as
only one expert network has to be loaded into memory at any given time.
Further, the autoencoders inherently capture the relatedness of one task to
another, based on which the most relevant prior model to be used for training a
new expert, with finetuning or learning without-forgetting, can be selected. We
evaluate our method on image classification and video prediction problems.Comment: CVPR 2017 pape
FQDet: Fast-converging Query-based Detector
Recently, two-stage Deformable DETR introduced the query-based two-stage
head, a new type of two-stage head different from the region-based two-stage
heads of classical detectors as Faster R-CNN. In query-based two-stage heads,
the second stage selects one feature per detection processed by a transformer,
called the query, as opposed to pooling a rectangular grid of features
processed by CNNs as in region-based detectors. In this work, we improve the
query-based head by improving the prior of the cross-attention operation with
anchors, significantly speeding up the convergence while increasing its
performance. Additionally, we empirically show that by improving the
cross-attention prior, auxiliary losses and iterative bounding box mechanisms
typically used by DETR-based detectors are no longer needed. By combining the
best of both the classical and the DETR-based detectors, our FQDet head peaks
at 45.4 AP on the 2017 COCO validation set when using a ResNet-50+TPN backbone,
only after training for 12 epochs using the 1x schedule. We outperform other
high-performing two-stage heads such as e.g. Cascade R-CNN, while using the
same backbone and while being computationally cheaper. Additionally, when using
the large ResNeXt-101-DCN+TPN backbone and multi-scale testing, our FQDet head
achieves 52.9 AP on the 2017 COCO test-dev set after only 12 epochs of
training. Code is released at https://github.com/CedricPicron/FQDet .Comment: Accepted at NeurIPS VTTA workshop 202
Propagating State Uncertainty Through Trajectory Forecasting
Uncertainty pervades through the modern robotic autonomy stack, with nearly
every component (e.g., sensors, detection, classification, tracking, behavior
prediction) producing continuous or discrete probabilistic distributions.
Trajectory forecasting, in particular, is surrounded by uncertainty as its
inputs are produced by (noisy) upstream perception and its outputs are
predictions that are often probabilistic for use in downstream planning.
However, most trajectory forecasting methods do not account for upstream
uncertainty, instead taking only the most-likely values. As a result,
perceptual uncertainties are not propagated through forecasting and predictions
are frequently overconfident. To address this, we present a novel method for
incorporating perceptual state uncertainty in trajectory forecasting, a key
component of which is a new statistical distance-based loss function which
encourages predicting uncertainties that better match upstream perception. We
evaluate our approach both in illustrative simulations and on large-scale,
real-world data, demonstrating its efficacy in propagating perceptual state
uncertainty through prediction and producing more calibrated predictions.Comment: IEEE International Conference on Robotics and Automation (ICRA) 2022
-- 8 pages, 6 figures, 4 table