Search Results for author:
Found 110 papers, 75 papers with code
Object-Scene Convolutional Neural Networks for Event Recognition in Images
Meanwhile, we investigate different network architectures for OS-CNN design, and adapt the deep (AlexNet) and very-deep (GoogLeNet) networks to the task of event recognition.
Action Recognition with Trajectory-Pooled Deep-Convolutional Descriptors
Visual features are of vital importance for human action understanding in videos.
Ranked #59 on
Action Recognition
on HMDB-51
Towards Good Practices for Very Deep Two-Stream ConvNets
However, for action recognition in videos, the improvement of deep convolutional networks is not so evident.
Ranked #66 on
Action Recognition
on UCF101
Places205-VGGNet Models for Scene Recognition
We verify the performance of trained Places205-VGGNet models on three datasets: MIT67, SUN397, and Places205.
Better Exploiting OS-CNNs for Better Event Recognition in Images
Event recognition from still images is one of the most important problems for image understanding.
Actionness Estimation Using Hybrid Fully Convolutional Networks
Actionness was introduced to quantify the likelihood of containing a generic action instance at a specific location.
Ranked #11 on
Action Detection
on J-HMDB
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
The other contribution is our study on a series of good practices in learning ConvNets on video data with the help of temporal segment network.
Ranked #3 on
Multimodal Activity Recognition
on EV-Action
Transferring Object-Scene Convolutional Neural Networks for Event Recognition in Still Images
These newly designed transferring techniques exploit multi-task learning frameworks to incorporate extra knowledge from other networks and additional datasets into the training procedure of event CNNs.
Knowledge Guided Disambiguation for Large-Scale Scene Classification with Multi-Resolution CNNs
Convolutional Neural Networks (CNNs) have made remarkable progress on scene recognition, partially due to these recent large-scale scene datasets, such as the Places and Places2.
UntrimmedNets for Weakly Supervised Action Recognition and Detection
We exploit the learned models for action recognition (WSR) and detection (WSD) on the untrimmed video datasets of THUMOS14 and ActivityNet.
Ranked #3 on
Action Classification
on ActivityNet-1.2
Weakly Supervised Action Localization
Weakly-Supervised Action Recognition
Temporal Segment Networks for Action Recognition in Videos
Furthermore, based on the temporal segment networks, we won the video classification track at the ActivityNet challenge 2016 among 24 teams, which demonstrates the effectiveness of TSN and the proposed good practices.
Ranked #5 on
Video Classification
on COIN
Appearance-and-Relation Networks for Video Classification
Specifically, SMART blocks decouple the spatiotemporal learning module into an appearance branch for spatial modeling and a relation branch for temporal modeling.
Ranked #51 on
Action Recognition
on UCF101
TDN: Temporal Difference Networks for Efficient Action Recognition
To mitigate this issue, this paper presents a new video architecture, termed as Temporal Difference Network (TDN), with a focus on capturing multi-scale temporal information for efficient action recognition.
Ranked #17 on
Action Recognition
on Something-Something V1
Temporal Difference Networks for Action Recognition
To mitigate this issue, this paper presents a new video architecture, termed as Temporal Difference Network (TDN), with a focus on capturing multi-scale temporal information for efficient action recognition.
Relaxed Transformer Decoders for Direct Action Proposal Generation
Extensive experiments on THUMOS14 and ActivityNet-1. 3 benchmarks demonstrate the effectiveness of RTD-Net, on both tasks of temporal action proposal generation and temporal action detection.
PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop
Regression-based methods have recently shown promising results in reconstructing human meshes from monocular images.
Ranked #5 on
3D Human Pose Estimation
on AGORA
(using extra training data)
3D human pose and shape estimation
3D Human Reconstruction
+2
Target Transformed Regression for Accurate Tracking
Accurate tracking is still a challenging task due to appearance variations, pose and view changes, and geometric deformations of target in videos.
Ranked #1 on
Visual Object Tracking
on VOT2019
MGSampler: An Explainable Sampling Strategy for Video Action Recognition
First, we present two different motion representations to enable us to efficiently distinguish the motion-salient frames from the background.
MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions
Spatio-temporal action detection is an important and challenging problem in video understanding.
FineAction: A Fine-Grained Video Dataset for Temporal Action Localization
Temporal action localization (TAL) is an important and challenging problem in video understanding.
SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction
Three-dimensional face dense alignment and reconstruction in the wild is a challenging problem as partial facial information is commonly missing in occluded and large pose face images.
Ranked #1 on
3D Face Reconstruction
on AFLW2000-3D
Joint Landmark and Structure Learning for Automatic Evaluation of Developmental Dysplasia of the Hip
In this study, we propose a multi-task framework to learn the relationships among landmarks and structures jointly and automatically evaluate DDH.
CGA-Net: Category Guided Aggregation for Point Cloud Semantic Segmentation
Previous point cloud semantic segmentation networks use the same process to aggregate features from neighbors of the same category and different categories.
Ranked #1 on
Semantic Segmentation
on SYNTHIA
Structured Sparse R-CNN for Direct Scene Graph Generation
The key to our method is a set of learnable triplet queries and a structured triplet detector which could be jointly optimized from the training set in an end-to-end manner.
Target Adaptive Context Aggregation for Video Scene Graph Generation
Specifically, we design an efficient method for frame-level VidSGG, termed as {\em Target Adaptive Context Aggregation Network} (TRACE), with a focus on capturing spatio-temporal context information for relation recognition.
Self Supervision to Distillation for Long-Tailed Visual Recognition
In this paper, we show that soft label can serve as a powerful solution to incorporate label correlation into a multi-stage training scheme for long-tailed recognition.
Ranked #43 on
Long-tail Learning
on CIFAR-100-LT (ρ=100)
Negative Sample Matters: A Renaissance of Metric Learning for Temporal Grounding
Instead, from a perspective on temporal grounding as a metric-learning problem, we present a Mutual Matching Network (MMN), to directly model the similarity between language queries and video moments in a joint embedding space.
Ranked #3 on
Temporal Sentence Grounding
on Charades-STA
Mutual Supervision for Dense Object Detection
In this paper, we break the convention of the same training samples for these two heads in dense detectors and explore a novel supervisory paradigm, termed as Mutual Supervision (MuSu), to respectively and mutually assign training samples for the classification and regression head to ensure this consistency.
End-to-End Dense Video Grounding via Parallel Regression
In this paper, we tackle a new problem of dense video grounding, by simultaneously localizing multiple moments with a paragraph as input.
A Closer Look at Few-Shot Video Classification: A New Baseline and Benchmark
In this paper, we aim to present an in-depth study on few-shot video classification by making three contributions.
DCAN: Improving Temporal Action Detection via Dual Context Aggregation
Specifically, we design the Multi-Path Temporal Context Aggregation (MTCA) to achieve smooth context aggregation on boundary level and precise evaluation of boundaries.
Ranked #19 on
Temporal Action Localization
on ActivityNet-1.3
Progressive Attention on Multi-Level Dense Difference Maps for Generic Event Boundary Detection
Generic event boundary detection is an important yet challenging task in video understanding, which aims at detecting the moments where humans naturally perceive event boundaries.
OCSampler: Compressing Videos to One Clip with Single-step Sampling
Recent works prefer to formulate frame sampling as a sequential decision task by selecting frames one by one according to their importance, while we present a new paradigm of learning instance-specific video condensation policies to select informative frames for representing the entire video only in a single step.
Hilbert Flattening: a Locality-Preserving Matrix Unfolding Method for Visual Discrimination
Flattening is essential in computer vision by converting multi-dimensional feature maps or images into one-dimensional vectors.
Temporal Perceiver: A General Architecture for Arbitrary Boundary Detection
Instead, in this paper, we present Temporal Perceiver, a general architecture with Transformer, offering a unified solution to the detection of arbitrary generic boundaries, ranging from shot-level, event-level, to scene-level GBDs.
Recovering 3D Human Mesh from Monocular Images: A Survey
Since the release of statistical body models, 3D human mesh recovery has been drawing broader attention.
MixFormer: End-to-End Tracking with Iterative Mixed Attention
Our core design is to utilize the flexibility of attention operations, and propose a Mixed Attention Module (MAM) for simultaneous feature extraction and target information integration.
Ranked #6 on
Visual Object Tracking
on UAV123
Semi-Supervised Video Object Segmentation
Visual Object Tracking
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
Pre-training video transformers on extra large-scale datasets is generally required to achieve premier performance on relatively small datasets.
Ranked #5 on
Self-Supervised Action Recognition
on HMDB51
Task-specific Inconsistency Alignment for Domain Adaptive Object Detection
To address this issue, in this paper, we propose Task-specific Inconsistency Alignment (TIA), by developing a new alignment mechanism in separate task spaces, improving the performance of the detector on both subtasks.
AdaMixer: A Fast-Converging Query-Based Object Detector
The recent query-based object detectors break this convention by decoding image features with a set of learnable queries.
Logit Normalization for Long-tail Object Detection
Real-world data exhibiting skewed distributions pose a serious challenge to existing object detectors.
Joint-Modal Label Denoising for Weakly-Supervised Audio-Visual Video Parsing
This paper focuses on the weakly-supervised audio-visual video parsing task, which aims to recognize all events belonging to each modality and localize their temporal boundaries.
APP-Net: Auxiliary-point-based Push and Pull Operations for Efficient Point Cloud Classification
In the existing work, each point in the cloud may inevitably be selected as the neighbors of multiple aggregation centers, as all centers will gather neighbor features from the whole point cloud independently.
Ranked #43 on
3D Point Cloud Classification
on ScanObjectNN
BasicTAD: an Astounding RGB-Only Baseline for Temporal Action Detection
Empirical results demonstrate that our PlusTAD is very efficient and significantly outperforms the previous methods on the datasets of THUMOS14 and FineAction.
Ranked #1 on
Temporal Action Localization
on THUMOS14
Cross-Architecture Self-supervised Video Representation Learning
In this paper, we present a new cross-architecture contrastive learning (CACL) framework for self-supervised video representation learning.
Submission to Generic Event Boundary Detection Challenge@CVPR 2022: Local Context Modeling and Global Boundary Decoding Approach
Local context modeling sub-network is proposed to perceive diverse patterns of generic event boundaries, and it generates powerful video representations and reliable boundary confidence.
Dynamic MDETR: A Dynamic Multimodal Transformer Decoder for Visual Grounding
The sampling module aims to select these informative patches by predicting the offsets with respect to a reference point, while the decoding module works for extracting the grounded object information by performing cross attention between image features and text features.
PointTAD: Multi-Label Temporal Action Detection with Learnable Query Points
Traditional temporal action detection (TAD) usually handles untrimmed videos with small number of action instances from a single label (e. g., ActivityNet, THUMOS).
Ranked #4 on
Temporal Action Localization
on MultiTHUMOS
Exploring State Change Capture of Heterogeneous Backbones @ Ego4D Hands and Objects Challenge 2022
Our method achieves an accuracy of 0. 796 on OSCC while achieving an absolute temporal localization error of 0. 516 on PNR.
InternVideo-Ego4D: A Pack of Champion Solutions to Ego4D Challenges
In this report, we present our champion solutions to five tracks at Ego4D challenge.
Ranked #1 on
State Change Object Detection
on Ego4D
UniFormerV2: Spatiotemporal Learning by Arming Image ViTs with Video UniFormer
UniFormer has successfully alleviated this issue, by unifying convolution and self-attention as a relation aggregator in the transformer format.
VLG: General Video Recognition with Web Textual Knowledge
Our VLG is first pre-trained on video and language datasets to learn a shared feature space, and then devises a flexible bi-modal attention head to collaborate high-level semantic concepts under different settings.
InternVideo: General Video Foundation Models via Generative and Discriminative Learning
Specifically, InternVideo efficiently explores masked video modeling and video-language contrastive learning as the pretraining objectives, and selectively coordinates video representations of these two complementary frameworks in a learnable manner to boost various video applications.
Ranked #1 on
Action Recognition
on Something-Something V1
(using extra training data)
UniFormerV2: Unlocking the Potential of Image ViTs for Video Understanding
The prolific performances of Vision Transformers (ViTs) in image tasks have prompted research into adapting the image ViTs for video tasks.
MixFormer: End-to-End Tracking with Iterative Mixed Attention
Our core design is to utilize the flexibility of attention operations, and propose a Mixed Attention Module (MAM) for simultaneous feature extraction and target information integration.
Ranked #1 on
Visual Object Tracking
on TrackingNet
CoMAE: Single Model Hybrid Pre-training on Small-Scale RGB-D Datasets
Our CoMAE presents a curriculum learning strategy to unify the two popular self-supervised representation learning algorithms: contrastive learning and masked image modeling.
Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation
In this paper, we propose a novel module to explicitly extract motion and appearance information via a unifying operation.
Ranked #1 on
Video Frame Interpolation
on MSU Video Frame Interpolation
(PSNR metric)
Learning Optical Flow and Scene Flow with Bidirectional Camera-LiDAR Fusion
To fuse dense image features and sparse point features, we propose a learnable operator named bidirectional camera-LiDAR fusion module (Bi-CLFM).
Ranked #1 on
Scene Flow Estimation
on KITTI 2015 Scene Flow Test
PDPP:Projected Diffusion for Procedure Planning in Instructional Videos
In this sense, we model the whole intermediate action sequence distribution with a diffusion model (PDPP), and thus transform the planning problem to a sampling process from this distribution.
LinK: Linear Kernel for LiDAR-based 3D Perception
Extending the success of 2D Large Kernel to 3D perception is challenging due to: 1. the cubically-increasing overhead in processing 3D data; 2. the optimization difficulties from data scarcity and sparsity.
CycleACR: Cycle Modeling of Actor-Context Relations for Video Action Detection
Existing studies model each actor and scene relation to improve action recognition.
Unmasked Teacher: Towards Training-Efficient Video Foundation Models
Previous VFMs rely on Image Foundation Models (IFMs), which face challenges in transferring to the video domain.
Ranked #1 on
Video Retrieval
on SSv2-template retrieval
(using extra training data)
VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking
Finally, we successfully train a video ViT model with a billion parameters, which achieves a new state-of-the-art performance on the datasets of Kinetics (90. 0% on K400 and 89. 9% on K600) and Something-Something (68. 7% on V1 and 77. 0% on V2).
Ranked #1 on
Self-Supervised Action Recognition
on UCF101
(using extra training data)
SparseFormer: Sparse Visual Recognition via Limited Latent Tokens
In this paper, we challenge this dense paradigm and present a new method, coined SparseFormer, to imitate human's sparse visual recognition in an end-to-end manner.
Sparse Representation-based Classification
Video Classification
StageInteractor: Query-based Object Detector with Cross-stage Interaction
Most of these detectors are trained with one-to-many label assignment strategies.
SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes
We expect SportsMOT to encourage the MOT trackers to promote in both motion-based association and appearance-based association.
Ranked #3 on
Multi-Object Tracking
on SportsMOT
(using extra training data)
Efficient Video Action Detection with Token Dropout and Context Refinement
Our EVAD consists of two specialized designs for video action detection.
Progressive Visual Prompt Learning with Contrastive Feature Re-formation
To the best of our knowledge, we are the first to demonstrate the superior performance of visual prompts in V-L models to previous prompt-based methods in downstream tasks.
An Evidential Real-Time Multi-Mode Fault Diagnosis Approach Based on Broad Learning System
Fault diagnosis is a crucial area of research in industry.
InternGPT: Solving Vision-Centric Tasks by Interacting with ChatGPT Beyond Language
Different from existing interactive systems that rely on pure language, by incorporating pointing instructions, the proposed iGPT significantly improves the efficiency of communication between users and chatbots, as well as the accuracy of chatbots in vision-centric tasks, especially in complicated visual scenarios where the number of objects is greater than 2.
VideoChat: Chat-Centric Video Understanding
In this paper, we initiate an attempt of developing an end-to-end chat-centric video understanding system, coined as VideoChat.
Ranked #6 on
Video Question Answering
on MVBench
Video-based Generative Performance Benchmarking (Consistency)
Video-based Generative Performance Benchmarking (Contextual Understanding)
+5
VideoLLM: Modeling Video Sequence with Large Language Models
Building upon this insight, we propose a novel framework called VideoLLM that leverages the sequence reasoning capabilities of pre-trained LLMs from natural language processing (NLP) for video sequence understanding.
MixFormerV2: Efficient Fully Transformer Tracking
Our key design is to introduce four special prediction tokens and concatenate them with the tokens from target template and search areas.
AlphaBlock: Embodied Finetuning for Vision-Language Reasoning in Robot Manipulation
We propose a novel framework for learning high-level cognitive capabilities in robot manipulation tasks, such as making a smiley face using building blocks.
Transferring Foundation Models for Generalizable Robotic Manipulation
In this paper, we propose a novel paradigm that effectively leverages language-reasoning segmentation mask generated by internet-scale foundation models, to condition robot manipulation tasks.
InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation
Specifically, we utilize a multi-scale approach to generate video-related descriptions.
MeMOTR: Long-Term Memory-Augmented Transformer for Multi-Object Tracking
Experimental results on DanceTrack show that MeMOTR impressively surpasses the state-of-the-art method by 7. 9% and 13. 0% on HOTA and AssA metrics, respectively.
Ranked #6 on
Multi-Object Tracking
on SportsMOT
Memory-and-Anticipation Transformer for Online Action Understanding
Based on this idea, we present Memory-and-Anticipation Transformer (MAT), a memory-anticipation-based approach, to address the online action detection and anticipation tasks.
Ranked #1 on
Action Detection
on THUMOS' 14
Is Self-Supervised Pretraining Good for Extrapolation in Molecular Property Prediction?
In material science, the prediction of unobserved values, commonly referred to as extrapolation, is particularly critical for property prediction as it enables researchers to gain insight into materials beyond the limits of available data.
On Data Imbalance in Molecular Property Prediction with Pre-training
In this study, we propose an effective pre-training method that addresses the imbalance in input data.
Deep Equilibrium Object Detection
To be more specific to object decoding, we use a two-step unrolled equilibrium equation to explicitly capture the query vector refinement.
SparseBEV: High-Performance Sparse 3D Object Detection from Multi-Camera Videos
Dense detectors typically follow a two-stage pipeline by first constructing a dense BEV feature and then performing object detection in BEV space, which suffers from complex view transformations and high computation cost.
Ranked #5 on
3D Object Detection
on nuScenes Camera Only
DPL: Decoupled Prompt Learning for Vision-Language Models
Prompt learning has emerged as an efficient and effective approach for transferring foundational Vision-Language Models (e. g., CLIP) to downstream tasks.
MGMAE: Motion Guided Masking for Video Masked Autoencoding
Based on this masking volume, we can track the unmasked tokens in time and sample a set of temporal consistent cubes from videos.
Joint Modeling of Feature, Correspondence, and a Compressed Memory for Video Object Segmentation
To overcome these issues, we propose a unified VOS framework, coined as JointFormer, for joint modeling the three elements of feature, correspondence, and a compressed memory.
ZeroI2V: Zero-Cost Adaptation of Pre-trained Transformers from Image to Video
In this paper, our goal is to present a zero-cost adaptation paradigm (ZeroI2V) to transfer the image transformers to video recognition tasks (i. e., introduce zero extra cost to the adapted models during inference).
Ranked #5 on
Action Recognition
on UCF101
(using extra training data)
Bridging The Gaps Between Token Pruning and Full Pre-training via Masked Fine-tuning
Despite the success of transformers on various computer vision tasks, they suffer from excessive memory and computational cost.
Harvest Video Foundation Models via Efficient Post-Pretraining
Building video-language foundation models is costly and difficult due to the redundant nature of video data and the lack of high-quality video-language datasets.
Asymmetric Masked Distillation for Pre-Training Small Foundation Models
And AMD achieves 73. 3% classification accuracy using the ViT-B model on the Something-in-Something V2 dataset, a 3. 7% improvement over the original ViT-B model from VideoMAE.
Ranked #20 on
Action Recognition
on Something-Something V2
MVBench: A Comprehensive Multi-modal Video Understanding Benchmark
With the rapid development of Multi-modal Large Language Models (MLLMs), a number of diagnostic benchmarks have recently emerged to evaluate the comprehension capabilities of these models.
Ranked #1 on
Zero-Shot Video Question Answer
on STAR Benchmark
VBench: Comprehensive Benchmark Suite for Video Generative Models
We will open-source VBench, including all prompts, evaluation methods, generated videos, and human preference annotations, and also include more video generation models in VBench to drive forward the field of video generation.
Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering
Neural rendering methods have significantly advanced photo-realistic 3D scene rendering in various academic and industrial applications.
Adapting Short-Term Transformers for Action Detection in Untrimmed Videos
To this end, we design effective cross-snippet propagation modules to gradually exchange short-term video information among different snippets from two levels.
BIVDiff: A Training-Free Framework for General-Purpose Video Synthesis via Bridging Image and Video Diffusion Models
Now text-to-image foundation models are widely applied to various downstream image synthesis tasks, such as controllable image generation and image editing, while downstream video synthesis tasks are less explored for several reasons.
MoVQA: A Benchmark of Versatile Question-Answering for Long-Form Movie Understanding
While several long-form VideoQA datasets have been introduced, the length of both videos used to curate questions and sub-clips of clues leveraged to answer those questions have not yet reached the criteria for genuine long-form video understanding.
Fully Sparse 3D Occupancy Prediction
Occupancy prediction plays a pivotal role in autonomous driving.
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents.
Data-efficient Event Camera Pre-training via Disentangled Masked Modeling
Our pre-training overcomes the limitations of previous methods, which either sacrifice temporal information by converting event sequences into 2D images for utilizing pre-trained image models or directly employ paired image data for knowledge distillation to enhance the learning of event streams.
StableDrag: Stable Dragging for Point-based Image Editing
Point-based image editing has attracted remarkable attention since the emergence of DragGAN.
VideoMamba: State Space Model for Efficient Video Understanding
Addressing the dual challenges of local redundancy and global dependencies in video understanding, this work innovatively adapts the Mamba to the video domain.
Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding
We categorize Mamba into four roles for modeling videos, deriving a Video Mamba Suite composed of 14 models/modules, and evaluating them on 12 video understanding tasks.
Ranked #1 on
Temporal Action Localization
on FineAction
Contextual AD Narration with Interleaved Multimodal Sequence
With video feature, text, character bank and context information as inputs, the generated ADs are able to correspond to the characters by name and provide reasonable, contextual descriptions to help audience understand the storyline of movie.
InternVideo2: Scaling Video Foundation Models for Multimodal Video Understanding
We introduce InternVideo2, a new video foundation model (ViFM) that achieves the state-of-the-art performance in action recognition, video-text tasks, and video-centric dialogue.
Ranked #1 on
Audio Classification
on ESC-50
(using extra training data)
EgoExoLearn: A Dataset for Bridging Asynchronous Ego- and Exo-centric View of Procedural Activities in Real World
Along with the videos we record high-quality gaze data and provide detailed multimodal annotations, formulating a playground for modeling the human ability to bridge asynchronous procedural actions from different viewpoints.
Multiple Object Tracking as ID Prediction
In Multiple Object Tracking (MOT), tracking-by-detection methods have stood the test for a long time, which split the process into two parts according to the definition: object detection and association.
Ranked #1 on
Multi-Object Tracking
on DanceTrack
(using extra training data)
Dual DETRs for Multi-Label Temporal Action Detection
To address this issue, we propose a new Dual-level query-based TAD framework, namely DualDETR, to detect actions from both instance-level and boundary-level.
SportsHHI: A Dataset for Human-Human Interaction Detection in Sports Videos
We hope that SportsHHI can stimulate research on human interaction understanding in videos and promote the development of spatio-temporal context modeling techniques in video visual relation detection.
Sparse Global Matching for Video Frame Interpolation with Large Motion
Large motion poses a critical challenge in Video Frame Interpolation (VFI) task.
STMixer: A One-Stage Sparse Action Detector
First, we present a query-based adaptive feature sampling module, which endows the detector with the flexibility of mining a group of discriminative features from the entire spatio-temporal domain.
