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Found 12 papers, 5 papers with code
Vamos: Versatile Action Models for Video Understanding
What makes good video representations for video understanding, such as anticipating future activities, or answering video-conditioned questions?
Object-centric Video Representation for Long-term Action Anticipation
To recognize and predict human-object interactions, we use a Transformer-based neural architecture which allows the "retrieval" of relevant objects for action anticipation at various time scales.
Ranked #2 on
Long Term Action Anticipation
on Ego4D
ViCor: Bridging Visual Understanding and Commonsense Reasoning with Large Language Models
We categorize the problem of VCR into visual commonsense understanding (VCU) and visual commonsense inference (VCI).
AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?
We propose to formulate the LTA task from two perspectives: a bottom-up approach that predicts the next actions autoregressively by modeling temporal dynamics; and a top-down approach that infers the goal of the actor and plans the needed procedure to accomplish the goal.
AdamsFormer for Spatial Action Localization in the Future
SALF is challenging because it requires understanding the underlying physics of video observations to predict future action locations accurately.
ViTGAN: Training GANs with Vision Transformers
Recently, Vision Transformers (ViTs) have shown competitive performance on image recognition while requiring less vision-specific inductive biases.
Ranked #68 on
Image Generation
on CIFAR-10
Dual Contradistinctive Generative Autoencoder
Our model, named dual contradistinctive generative autoencoder (DC-VAE), integrates an instance-level discriminative loss (maintaining the instance-level fidelity for the reconstruction/synthesis) with a set-level adversarial loss (encouraging the set-level fidelity for there construction/synthesis), both being contradistinctive.
Ranked #2 on
Image Generation
on LSUN Bedroom 128 x 128
Unaligned Image-to-Sequence Transformation with Loop Consistency
We tackle the problem of modeling sequential visual phenomena.
Learning Instance Occlusion for Panoptic Segmentation
Panoptic segmentation requires segments of both "things" (countable object instances) and "stuff" (uncountable and amorphous regions) within a single output.
Ranked #22 on
Panoptic Segmentation
on COCO test-dev
Meta-Learning with Differentiable Convex Optimization
We propose to use these predictors as base learners to learn representations for few-shot learning and show they offer better tradeoffs between feature size and performance across a range of few-shot recognition benchmarks.
Ranked #12 on
Few-Shot Image Classification
on FC100 5-way (1-shot)
Controllable Top-down Feature Transformer
We study the intrinsic transformation of feature maps across convolutional network layers with explicit top-down control.
Wasserstein Introspective Neural Networks
We present Wasserstein introspective neural networks (WINN) that are both a generator and a discriminator within a single model.
