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Found 14 papers, 8 papers with code
When Do We Not Need Larger Vision Models?
Our results show that a multi-scale smaller model has comparable learning capacity to a larger model, and pre-training smaller models with S$^2$ can match or even exceed the advantage of larger models.
Humanoid Locomotion as Next Token Prediction
We cast real-world humanoid control as a next token prediction problem, akin to predicting the next word in language.
Rethinking Patch Dependence for Masked Autoencoders
In this work, we re-examine inter-patch dependencies in the decoding mechanism of masked autoencoders (MAE).
Recursive Visual Programming
Visual Programming (VP) has emerged as a powerful framework for Visual Question Answering (VQA).
LLM-grounded Video Diffusion Models
We show that LLMs are able to understand complex spatiotemporal dynamics from text alone and generate layouts that align closely with both the prompts and the object motion patterns typically observed in the real world.
Robot Learning with Sensorimotor Pre-training
We present a self-supervised sensorimotor pre-training approach for robotics.
TOAST: Transfer Learning via Attention Steering
We introduce Top-Down Attention Steering (TOAST), a novel transfer learning algorithm that keeps the pre-trained backbone frozen, selects task-relevant features in the output, and feeds those features back to the model to steer the attention to the task-specific features.
Top-Down Visual Attention from Analysis by Synthesis
In this paper, we consider top-down attention from a classic Analysis-by-Synthesis (AbS) perspective of vision.
Visual Attention Emerges from Recurrent Sparse Reconstruction
We present VARS, Visual Attention from Recurrent Sparse reconstruction, a new attention formulation built on two prominent features of the human visual attention mechanism: recurrency and sparsity.
Temporal Action Detection with Multi-level Supervision
We extensively benchmark against the baselines for SSAD and OSAD on our created data splits in THUMOS14 and ActivityNet1. 2, and demonstrate the effectiveness of the proposed UFA and IB methods.
Auxiliary Task Reweighting for Minimum-data Learning
By adjusting the auxiliary task weights to minimize the divergence between the surrogate prior and the true prior of the main task, we obtain a more accurate prior estimation, achieving the goal of minimizing the required amount of training data for the main task and avoiding a costly grid search.
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective
Specifically, we discriminate texture from shape based on local self-information in an image, and adopt a Dropout-like algorithm to decorrelate the model output from the local texture.
Weakly-Supervised Action Localization with Expectation-Maximization Multi-Instance Learning
Weakly-supervised action localization requires training a model to localize the action segments in the video given only video level action label.
Ranked #9 on
Weakly Supervised Action Localization
on THUMOS’14
Weakly-Supervised Action Localization by Generative Attention Modeling
By maximizing the conditional probability with respect to the attention, the action and non-action frames are well separated.
Weakly Supervised Action Localization
Weakly-supervised Temporal Action Localization
+1
