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Found 19 papers, 8 papers with code
Hyper-VolTran: Fast and Generalizable One-Shot Image to 3D Object Structure via HyperNetworks
Solving image-to-3D from a single view is an ill-posed problem, and current neural reconstruction methods addressing it through diffusion models still rely on scene-specific optimization, constraining their generalization capability.
GenTron: Delving Deep into Diffusion Transformers for Image and Video Generation
In this study, we explore Transformer-based diffusion models for image and video generation.
FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing
In this paper, for the first time, we introduce optical flow into the attention module in the diffusion model's U-Net to address the inconsistency issue for text-to-video editing.
Multi-Modal Few-Shot Temporal Action Detection
In this work, we introduce a new multi-modality few-shot (MMFS) TAD problem, which can be considered as a marriage of FS-TAD and ZS-TAD by leveraging few-shot support videos and new class names jointly.
Where is my Wallet? Modeling Object Proposal Sets for Egocentric Visual Query Localization
Our experiments show the proposed adaptations improve egocentric query detection, leading to a better visual query localization system in both 2D and 3D configurations.
Negative Frames Matter in Egocentric Visual Query 2D Localization
The repeated gradient computation of the same object lead to an inefficient training; (2) The false positive rate is high on background frames.
SAIC_Cambridge-HuPBA-FBK Submission to the EPIC-Kitchens-100 Action Recognition Challenge 2021
This report presents the technical details of our submission to the EPIC-Kitchens-100 Action Recognition Challenge 2021.
TNT: Text-Conditioned Network with Transductive Inference for Few-Shot Video Classification
Recently, few-shot learning has received increasing interest.
Space-time Mixing Attention for Video Transformer
In this work, we propose a Video Transformer model the complexity of which scales linearly with the number of frames in the video sequence and hence induces no overhead compared to an image-based Transformer model.
Ranked #32 on
Action Classification
on Kinetics-600
Low-Fidelity End-to-End Video Encoder Pre-training for Temporal Action Localization
This results in a task discrepancy problem for the video encoder -- trained for action classification, but used for TAL.
Few-shot Action Recognition with Prototype-centered Attentive Learning
Extensive experiments on four standard few-shot action benchmarks show that our method clearly outperforms previous state-of-the-art methods, with the improvement particularly significant (10+\%) on the most challenging fine-grained action recognition benchmark.
Boundary-sensitive Pre-training for Temporal Localization in Videos
However, most existing models developed for these tasks are pre-trained on general video action classification tasks.
Ranked #23 on
Temporal Action Localization
on ActivityNet-1.3
Egocentric Action Recognition by Video Attention and Temporal Context
In this challenge, action recognition is posed as the problem of simultaneously predicting a single `verb' and `noun' class label given an input trimmed video clip.
Knowing What, Where and When to Look: Efficient Video Action Modeling with Attention
Departing from existing alternatives, our W3 module models all three facets of video attention jointly.
Ranked #1 on
Action Recognition
on EgoGesture
Incremental Few-Shot Object Detection
To this end we propose OpeN-ended Centre nEt (ONCE), a detector designed for incrementally learning to detect novel class objects with few examples.
MFAS: Multimodal Fusion Architecture Search
We tackle the problem of finding good architectures for multimodal classification problems.
Efficient Progressive Neural Architecture Search
We demonstrate with experiments on the CIFAR-10 dataset that our method, denominated Efficient progressive neural architecture search (EPNAS), leads to increased search efficiency, while retaining competitiveness of found architectures.
Learning how to be robust: Deep polynomial regression
We bypass the need for a tailored loss function on the regression parameters by attaching to our model a differentiable hard-wired decoder corresponding to the polynomial operation at hand.
Determining Occlusions From Space and Time Image Reconstructions
With this in mind, we propose a novel approach to occlusion detection where visibility or not of a point in next frame is formulated in terms of visual reconstruction.
