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Abstract: The ability to reason about temporal and causal events from videos lies at the core of human intelligence. Most video reasoning benchmarks, however, focus on pattern recognition from complex visual and language input, instead of on causal structure. We study the complementary problem, exploring the temporal and causal structures ...
First, there is a lack of diversity in both event types and natural language descriptions; second, causal relationships based on manually-defined heuristics are different from human judgments. To address both shortcomings, we present the CLEVRER-Humans benchmark, a video reasoning dataset for causal judgment of physical events with human labels.
Our analysis yields a novel robustness metric called CLEVER, which is short for Cross Lipschitz Extreme Value for nEtwork Robustness. The proposed CLEVER score is attack-agnostic and is computationally feasible for large neural networks. Experimental results on various networks, including ResNet, Inception-v3 and MobileNet, show that (i) CLEVER ...
CLEVER score is attack-agnostic and computationally feasible for large neural networks. Experimental results on various networks, including ResNet, Inception-v3 and MobileNet, show that (i) CLEVER is aligned with the robustness indica-tion measured by the ‘ 2 and ‘ 1norms of adversarial examples from powerful
tokens. We call this the Clever Hans cheat, named after a famous arithmetic-solving horse that was debunked to have been following subtle cues from a trainer. Now, while the later tokens become easy to fit by the Clever Hans cheat, in contrast, the earlier answer tokens become impossible to learn. This
016 view, namely CLEVER, which is augmentation-017 free and mitigates biases on the inference stage. 018 Specifically, we train a claim-evidence fusion 019 model and a claim-only model independently. 020 Then, we obtain the final prediction via sub-021 tracting output of the claim-only model from 022 output of the claim-evidence fusion model,
their Clever Hans effect [1] with the actual planning being done by the humans in the loop rather than the LLMs themselves. We thus separate our evaluation into two modes–autonomous and as assistants to external planners/reasoners. There have also been efforts which mostly depended on
e has been developed towards a comprehensive measure of robustness. In this paper, we provide theoretical justifi-cation for converting robustness analysis into a local Lipschitz constant estimation problem, a. d propose to use the Extreme Value Theory for efficient evaluation. Our analysis yields a novel robustness metric called CLEVER, whic.
Seaclan is clever, wise and smart in terms of skills. They live by a open sea (real life - the Baltic Sea) and are known for their ability to swim and dive. Th
4- A cat has been injured. Give them goldenrod to prevent infection. (use one of the mates as the cat) 5- A cat has a deep wound. Give them horsetail to cure them. (use one of the mates as the cat) 6- A cat has broken a bone. Give them comfrey to cure them. (use one of the mates as the cat) :The Path to Being a Warrior/Medicine Cat: Your goal ...