Robert Boschgmbh

Adobe, Inc.

Karren Yang

Filipe J Cabrita Condessa

Wan-Yi Lin

Manash Pratim

Jeremy Zico Kolter

Bryan Russell

Justin Salamon

A computer-implemented method for training a machine learning network. The method may include receiving an input data, selecting one or more batch samples from the input data, applying a perturbation object onto the one or more batch samples to create a perturbed sample, running the perturbed sample through the machine learning network, updating the perturbation object in response to the function in response to running the perturbed sample, and outputting the perturbation object in response to exceeding a convergence threshold.

System and method for utilizing perturbation in a multimodal environment

A multimodal perception system for an autonomous vehicle includes a first sensor that is one of a video, RADAR, LIDAR, or ultrasound sensor, and a controller. The controller may be configured to, receive a first signal from a first sensor, a second signal from a second sensor, and a third signal from a third sensor, extract a first feature vector from the first signal, extract a second feature vector from the second signal, extract a third feature vector from the third signal, determine an odd-one-out vector from the first, second, and third feature vectors via an odd-one-out network of a machine learning network, based on inconsistent modality prediction, fuse the first, second, and third feature vectors and odd-one-out vector into a fused feature vector, output the fused feature vector, and control the autonomous vehicle based on the fused feature vector.

Defending multimodal fusion models against single-source adversaries

A computer system is trained to understand audio-visual spatial correspondence using audio-visual clips having multi-channel audio. The computer system includes an audio subnetwork, video subnetwork, and pretext subnetwork. The audio subnetwork receives the two channels of audio from the audio-visual clips, and the video subnetwork receives the video frames from the audio-visual clips. In a subset of the audio-visual clips the audio-visual spatial relationship is misaligned, causing the audio-visual spatial cues for the audio and video to be incorrect. The audio subnetwork outputs an audio feature vector for each audio-visual clip, and the video subnetwork outputs a video feature vector for each audio-visual clip. The audio and video feature vectors for each audio-visual clip are merged and provided to the pretext subnetwork, which is configured to classify the merged vector as either having a misaligned audio-visual spatial relationship or not. The subnetworks are trained based on the loss calculated from the classification.

Representation learning from video with spatial audio

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Karren Yang