20 research outputs found
Doubly Right Object Recognition: A Why Prompt for Visual Rationales
Many visual recognition models are evaluated only on their classification
accuracy, a metric for which they obtain strong performance. In this paper, we
investigate whether computer vision models can also provide correct rationales
for their predictions. We propose a ``doubly right'' object recognition
benchmark, where the metric requires the model to simultaneously produce both
the right labels as well as the right rationales. We find that state-of-the-art
visual models, such as CLIP, often provide incorrect rationales for their
categorical predictions. However, by transferring the rationales from language
models into visual representations through a tailored dataset, we show that we
can learn a ``why prompt,'' which adapts large visual representations to
produce correct rationales. Visualizations and empirical experiments show that
our prompts significantly improve performance on doubly right object
recognition, in addition to zero-shot transfer to unseen tasks and datasets
RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces
We present RELATE, a model that learns to generate physically plausible
scenes and videos of multiple interacting objects. Similar to other generative
approaches, RELATE is trained end-to-end on raw, unlabeled data. RELATE
combines an object-centric GAN formulation with a model that explicitly
accounts for correlations between individual objects. This allows the model to
generate realistic scenes and videos from a physically-interpretable
parameterization. Furthermore, we show that modeling the object correlation is
necessary to learn to disentangle object positions and identity. We find that
RELATE is also amenable to physically realistic scene editing and that it
significantly outperforms prior art in object-centric scene generation in both
synthetic (CLEVR, ShapeStacks) and real-world data (cars). In addition, in
contrast to state-of-the-art methods in object-centric generative modeling,
RELATE also extends naturally to dynamic scenes and generates videos of high
visual fidelity. Source code, datasets and more results are available at
http://geometry.cs.ucl.ac.uk/projects/2020/relate/