5 research outputs found
TraMNet - Transition Matrix Network for Efficient Action Tube Proposals
Current state-of-the-art methods solve spatiotemporal action localisation by
extending 2D anchors to 3D-cuboid proposals on stacks of frames, to generate
sets of temporally connected bounding boxes called \textit{action micro-tubes}.
However, they fail to consider that the underlying anchor proposal hypotheses
should also move (transition) from frame to frame, as the actor or the camera
does. Assuming we evaluate 2D anchors in each frame, then the number of
possible transitions from each 2D anchor to the next, for a sequence of
consecutive frames, is in the order of , expensive even for small
values of . To avoid this problem, we introduce a Transition-Matrix-based
Network (TraMNet) which relies on computing transition probabilities between
anchor proposals while maximising their overlap with ground truth bounding
boxes across frames, and enforcing sparsity via a transition threshold. As the
resulting transition matrix is sparse and stochastic, this reduces the proposal
hypothesis search space from to the cardinality of the thresholded
matrix. At training time, transitions are specific to cell locations of the
feature maps, so that a sparse (efficient) transition matrix is used to train
the network. At test time, a denser transition matrix can be obtained either by
decreasing the threshold or by adding to it all the relative transitions
originating from any cell location, allowing the network to handle transitions
in the test data that might not have been present in the training data, and
making detection translation-invariant. Finally, we show that our network can
handle sparse annotations such as those available in the DALY dataset. We
report extensive experiments on the DALY, UCF101-24 and Transformed-UCF101-24
datasets to support our claims.Comment: 15 page
Spatiotemporal Event Graphs for Dynamic Scene Understanding
Dynamic scene understanding is the ability of a computer system to interpret and make sense of the visual information present in a video of a real-world scene. In this thesis, we present a series of frameworks for dynamic scene understanding starting from road event detection from an autonomous driving perspective to complex video activity detection, followed by continual learning approaches for the life-long learning of the models. Firstly, we introduce the ROad event Awareness Dataset (ROAD) for Autonomous Driving, to our knowledge the first of its kind. ROAD is designed to test an autonomous vehicle’s ability to detect road events, defined as triplets composed by an active agent, the action(s) it performs and the corresponding scene locations. Due to the lack of datasets equipped with formally specified logical requirements, we also introduce the ROad event Awareness Dataset with logical Requirements (ROAD-R), the first publicly available dataset for autonomous driving with requirements expressed as logical constraints, as a tool for driving neurosymbolic research in the area.
Next, we extend event detection to holistic scene understanding by proposing two complex activity detection methods. In the first method, we present a deformable, spatiotemporal scene graph approach, consisting of three main building blocks: action tube detection, a 3D deformable RoI pooling layer designed for learning the flexible, deformable geometry of the constituent action tubes, and a scene graph constructed by considering all parts as nodes and connecting them based on different semantics. In a second approach evolving from the first, we propose a hybrid graph neural network that combines attention applied to a graph encoding of the local (short-term) dynamic scene with a temporal graph modelling the overall long-duration activity. Our contribution is threefold: i) a feature extraction technique; ii) a method for constructing a local scene graph followed by graph attention, and iii) a graph for temporally connecting all the local dynamic scene graphs.
Finally, the last part of the thesis is about presenting a new continual semi-supervised learning (CSSL) paradigm, proposed to the attention of the machine learning community. We also propose to formulate the continual semi-supervised learning problem as a latent-variable