665 citations · 2.3k across the 48 of their papers we have counts for
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MINOTAUR: Multi-task Video Grounding From Multimodal Queries
Raghav Goyal, Effrosyni Mavroudi, Xitong Yang +5
Video understanding tasks take many forms, from action detection to visual query localization and spatio-temporal grounding of sentences. These tasks differ in the type of inputs (…
Memory-Augmented Reinforcement Learning for Image-Goal Navigation
Lina Mezghani, Sainbayar Sukhbaatar, Thibaut Lavril +4
In this work, we present a memory-augmented approach for image-goal navigation. Earlier attempts, including RL-based and SLAM-based approaches have either shown poor generalization…
Learning to Visually Navigate in Photorealistic Environments Without any Supervision
Lina Mezghani, Sainbayar Sukhbaatar, Arthur Szlam +2
Learning to navigate in a realistic setting where an agent must rely solely on visual inputs is a challenging task, in part because the lack of position information makes it diffic…
Simple Baseline for Visual Question Answering
Bolei Zhou, Yuandong Tian, Sainbayar Sukhbaatar +2
We describe a very simple bag-of-words baseline for visual question answering. This baseline concatenates the word features from the question and CNN features from the image to pre…
Training Convolutional Networks with Noisy Labels
Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri +2
The availability of large labeled datasets has allowed Convolutional Network models to achieve impressive recognition results. However, in many settings manual annotation of the da…
Auto-pooling: Learning to Improve Invariance of Image Features from Image Sequences
Sainbayar Sukhbaatar, Takaki Makino, Kazuyuki Aihara
Learning invariant representations from images is one of the hardest challenges facing computer vision. Spatial pooling is widely used to create invariance to spatial shifting, but…