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Found 16 papers, 4 papers with code
M2D2M: Multi-Motion Generation from Text with Discrete Diffusion Models
We introduce the Multi-Motion Discrete Diffusion Models (M2D2M), a novel approach for human motion generation from textual descriptions of multiple actions, utilizing the strengths of discrete diffusion models.
Can't make an Omelette without Breaking some Eggs: Plausible Action Anticipation using Large Video-Language Models
To address this limitation, we explore the generative capability of a large video-language model in our work and further, develop the understanding of plausibility in an action sequence by introducing two objective functions, a counterfactual-based plausible action sequence learning loss and a long-horizon action repetition loss.
Ranked #1 on
Action Anticipation
on EPIC-KITCHENS-100
Multi-Objective Recommendation via Multivariate Policy Learning
We frame this as a decision-making task, where the scalarisation weights are actions taken to maximise an overall North Star reward (e. g. long-term user retention or growth).
Disentangled Neural Relational Inference for Interpretable Motion Prediction
The results show superior performance compared to existing methods in modeling spatio-temporal relations, motion prediction, and identifying time-invariant latent features.
Uncertainty-aware Action Decoupling Transformer for Action Anticipation
The objective is to make the two decoupled tasks assist each other and eventually improve the action anticipation task.
Ranked #1 on
Action Anticipation
on 50-Salads
Vamos: Versatile Action Models for Video Understanding
To interpret the important text evidence for question answering, we generalize the concept bottleneck model to work with tokens and nonlinear models, which uses hard attention to select a small subset of tokens from the free-form text as inputs to the LLM reasoner.
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 #4 on
Long Term Action Anticipation
on Ego4D
Rank2Tell: A Multimodal Driving Dataset for Joint Importance Ranking and Reasoning
The widespread adoption of commercial autonomous vehicles (AVs) and advanced driver assistance systems (ADAS) may largely depend on their acceptance by society, for which their perceived trustworthiness and interpretability to riders are crucial.
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.
Ranked #2 on
Long Term Action Anticipation
on Ego4D
Latency Matters: Real-Time Action Forecasting Transformer
We present RAFTformer, a real-time action forecasting transformer for latency aware real-world action forecasting applications.
Weakly-Supervised Action Segmentation and Unseen Error Detection in Anomalous Instructional Videos
We present a novel method for weakly-supervised action segmentation and unseen error detection in anomalous instructional videos.
Ordered Atomic Activity for Fine-grained Interactive Traffic Scenario Understanding
We introduce a novel representation called Ordered Atomic Activity for interactive scenario understanding.
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.
Weakly-Supervised Online Action Segmentation in Multi-View Instructional Videos
Experimental results show efficacy of the proposed methods both qualitatively and quantitatively in two domains of cooking and assembly.
Unsupervised Domain Adaptation for Spatio-Temporal Action Localization
Spatio-temporal action localization is an important problem in computer vision that involves detecting where and when activities occur, and therefore requires modeling of both spatial and temporal features.
Connecting Visual Experiences using Max-flow Network with Application to Visual Localization
This problem is solved by finding the maximum flow in a directed graph flow-network, whose vertices represent the matches between frames in the test and reference sequences.
