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Found 101 papers, 50 papers with code
TriDet: Temporal Action Detection with Relative Boundary Modeling
In this paper, we present a one-stage framework TriDet for temporal action detection.
Ranked #1 on
Temporal Action Localization
on EPIC-KITCHENS-100
FastPillars: A Deployment-friendly Pillar-based 3D Detector
The deployment of 3D detectors strikes one of the major challenges in real-world self-driving scenarios.
DiP: Learning Discriminative Implicit Parts for Person Re-Identification
In person re-identification (ReID) tasks, many works explore the learning of part features to improve the performance over global image features.
Ranked #2 on
Person Re-Identification
on Occluded-DukeMTMC
Multiple Object Tracking Challenge Technical Report for Team MT_IoT
This is a brief technical report of our proposed method for Multiple-Object Tracking (MOT) Challenge in Complex Environments.
Ranked #2 on
Multi-Object Tracking
on DanceTrack
(using extra training data)
AeDet: Azimuth-invariant Multi-view 3D Object Detection
However, the typical convolution ignores the radial symmetry of the BEV features and increases the difficulty of the detector optimization.
HAM: Hierarchical Attention Model with High Performance for 3D Visual Grounding
To simplify the pipeline, we carefully investigate 3D visual grounding and summarize three fundamental problems about how to develop an end-to-end model with high performance for this task.
Oscillatory cooperation prevalence emerges from misperception
Here we show that oscillatory behaviors naturally emerge if incomplete information is incorporated into the cooperation evolution of a non-Markov model.
Planning Assembly Sequence with Graph Transformer
In this paper, we present a graph-transformer based framework for the ASP problem which is trained and demonstrated on a self-collected ASP database.
Contrastive Video-Language Learning with Fine-grained Frame Sampling
However, even in paired video-text segments, only a subset of the frames are semantically relevant to the corresponding text, with the remainder representing noise; where the ratio of noisy frames is higher for longer videos.
Contextual Modeling for 3D Dense Captioning on Point Clouds
With such global and local contextual modeling strategies, our proposed model can effectively characterize the object representations and contextual information and thereby generate comprehensive and detailed descriptions of the located objects.
SoccerNet 2022 Challenges Results
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team.
Reweighting Clicks with Dwell Time in Recommendation
Precisely, we first define a new behavior named valid read, which helps to select high-quality click instances for different users and items via dwell time.
Expansion and Shrinkage of Localization for Weakly-Supervised Semantic Segmentation
To tackle with this issue, this paper proposes an Expansion and Shrinkage scheme based on the offset learning in the deformable convolution, to sequentially improve the recall and precision of the located object in the two respective stages.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Weakly Supervised Semantic Segmentation via Progressive Patch Learning
"Progressive Patch Learning" further extends the feature destruction and patch learning to multi-level granularities in a progressive manner.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection
Recent approaches aim at exploring the semantic densities of camera features through lifting points in 2D camera images (referred to as seeds) into 3D space, and then incorporate 2D semantics via cross-modal interaction or fusion techniques.
A Circular Window-based Cascade Transformer for Online Action Detection
In this paper, we advocate a novel and efficient principle for online action detection.
Chunk-aware Alignment and Lexical Constraint for Visual Entailment with Natural Language Explanations
CSI), a relation inferrer, and a Lexical Constraint-aware Generator (arr.
ReAct: Temporal Action Detection with Relational Queries
Moreover, we propose two losses to facilitate and stabilize the training of action classification.
Ranked #6 on
Temporal Action Localization
on THUMOS’14
MT-Net Submission to the Waymo 3D Detection Leaderboard
In this technical report, we introduce our submission to the Waymo 3D Detection leaderboard.
Cycle-Interactive Generative Adversarial Network for Robust Unsupervised Low-Light Enhancement
Getting rid of the fundamental limitations in fitting to the paired training data, recent unsupervised low-light enhancement methods excel in adjusting illumination and contrast of images.
Leveraging Global Binary Masks for Structure Segmentation in Medical Images
We proposed to leverage the consistency of organs' anatomical shape and position information in medical images.
PromptDet: Towards Open-vocabulary Detection using Uncurated Images
The goal of this work is to establish a scalable pipeline for expanding an object detector towards novel/unseen categories, using zero manual annotations.
MORE: Multi-Order RElation Mining for Dense Captioning in 3D Scenes
3D dense captioning is a recently-proposed novel task, where point clouds contain more geometric information than the 2D counterpart.
A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach
New benchmarking results indicate that our proposed evaluation protocols can better monitor the research progress.
Suspected Object Matters: Rethinking Model's Prediction for One-stage Visual Grounding
However, exploring relationships among these suspected objects in the one-stage visual grounding paradigm is non-trivial due to two core problems: (1) no object proposals are available as the basis on which to select suspected objects and perform relationship modeling; (2) compared with those irrelevant to the text query, suspected objects are more confusing, as they may share similar semantics, be entangled with certain relationships, etc, and thereby more easily mislead the model's prediction.
Probabilistic fair behaviors spark its boost in the Ultimatum Game: the strength of good Samaritans
Behavioral experiments on the Ultimatum Game have shown that we human beings have remarkable preference in fair play, contradicting the predictions by the game theory.
Syntax Customized Video Captioning by Imitating Exemplar Sentences
Enhancing the diversity of sentences to describe video contents is an important problem arising in recent video captioning research.
Controllable Video Captioning with an Exemplar Sentence
In this paper, we investigate a novel and challenging task, namely controllable video captioning with an exemplar sentence.
Sensoring and Application of Multimodal Data for the Detection of Freezing of Gait in Parkinson's Disease
The accurate and reliable detection or prediction of freezing of gaits (FOG) is important for fall prevention in Parkinson's Disease (PD) and studying the physiological transitions during the occurrence of FOG.
Two-stage Visual Cues Enhancement Network for Referring Image Segmentation
Referring Image Segmentation (RIS) aims at segmenting the target object from an image referred by one given natural language expression.
Video Temporal Relationship Mining for Data-Efficient Person Re-identification
This paper is a technical report to our submission to the ICCV 2021 VIPriors Re-identification Challenge.
Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection
For the cross-modality interaction in feature encoder, existing methods either indiscriminately treat RGB and depth modalities, or only habitually utilize depth cues as auxiliary information of the RGB branch.
Discriminative-Generative Representation Learning for One-Class Anomaly Detection
As a kind of generative self-supervised learning methods, generative adversarial nets have been widely studied in the field of anomaly detection.
Sentence-level Online Handwritten Chinese Character Recognition
Single online handwritten Chinese character recognition~(single OLHCCR) has achieved prominent performance.
GlyphCRM: Bidirectional Encoder Representation for Chinese Character with its Glyph
Previous works indicate that the glyph of Chinese characters contains rich semantic information and has the potential to enhance the representation of Chinese characters.
Beyond Monocular Deraining: Parallel Stereo Deraining Network Via Semantic Prior
We first use a coarse deraining network to reduce the rain streaks on the input images, and then adopt a pre-trained semantic segmentation network to extract semantic features from the coarse derained image.
Relation-aware Instance Refinement for Weakly Supervised Visual Grounding
Visual grounding, which aims to build a correspondence between visual objects and their language entities, plays a key role in cross-modal scene understanding.
SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering
We show that SLAKE can be used to facilitate the development and evaluation of Med-VQA systems.
Take More Positives: An Empirical Study of Contrastive Learing in Unsupervised Person Re-Identification
By studying two unsupervised person re-ID methods in a cross-method way, we point out a hard negative problem is handled implicitly by their designs of data augmentations and PK sampler respectively.
Similarity Reasoning and Filtration for Image-Text Matching
Image-text matching plays a critical role in bridging the vision and language, and great progress has been made by exploiting the global alignment between image and sentence, or local alignments between regions and words.
Ranked #2 on
Image Retrieval
on Flickr30K 1K test
Towards Unsupervised Deep Image Enhancement with Generative Adversarial Network
In this paper, we present an unsupervised image enhancement generative adversarial network (UEGAN), which learns the corresponding image-to-image mapping from a set of images with desired characteristics in an unsupervised manner, rather than learning on a large number of paired images.
Unpaired Image Enhancement with Quality-Attention Generative Adversarial Network
The key novelty of the proposed QAGAN lies in the injected QAM for the generator such that it learns domain-relevant quality attention directly from the two domains.
Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis
Also, we build a new dataset, namely iPER dataset, for the evaluation of human motion imitation, appearance transfer, and novel view synthesis.
Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning
We propose a parsimonious quantile regression framework to learn the dynamic tail behaviors of financial asset returns.
Intrinsic Relationship Reasoning for Small Object Detection
In this paper, we propose a novel context reasoning approach for small object detection which models and infers the intrinsic semantic and spatial layout relationships between objects.
Consensus-Aware Visual-Semantic Embedding for Image-Text Matching
In this paper, we propose a Consensus-aware Visual-Semantic Embedding (CVSE) model to incorporate the consensus information, namely the commonsense knowledge shared between both modalities, into image-text matching.
Deblurring by Realistic Blurring
To address this problem, we propose a new method which combines two GAN models, i. e., a learning-to-Blur GAN (BGAN) and learning-to-DeBlur GAN (DBGAN), in order to learn a better model for image deblurring by primarily learning how to blur images.
Ranked #14 on
Deblurring
on HIDE (trained on GOPRO)
STH: Spatio-Temporal Hybrid Convolution for Efficient Action Recognition
Such a design enables efficient spatio-temporal modeling and maintains a small model scale.
Weakly-Supervised Multi-Level Attentional Reconstruction Network for Grounding Textual Queries in Videos
The task of temporally grounding textual queries in videos is to localize one video segment that semantically corresponds to the given query.
Cops-Ref: A new Dataset and Task on Compositional Referring Expression Comprehension
To bridge the gap, we propose a new dataset for visual reasoning in context of referring expression comprehension with two main features.
Look Closer to Ground Better: Weakly-Supervised Temporal Grounding of Sentence in Video
In this paper, we study the problem of weakly-supervised temporal grounding of sentence in video.
Fine-grained Image-to-Image Transformation towards Visual Recognition
In this paper, we aim at transforming an image with a fine-grained category to synthesize new images that preserve the identity of the input image, which can thereby benefit the subsequent fine-grained image recognition and few-shot learning tasks.
Coupled Network for Robust Pedestrian Detection with Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling
Ablation studies have validated the effectiveness of both the proposed gated multi-layer feature extraction sub-network and the deformable occlusion handling sub-network.
LaFIn: Generative Landmark Guided Face Inpainting
It is challenging to inpaint face images in the wild, due to the large variation of appearance, such as different poses, expressions and occlusions.
EDIT: Exemplar-Domain Aware Image-to-Image Translation
The principle behind is that, for images from multiple domains, the content features can be obtained by a uniform extractor, while (re-)stylization is achieved by mapping the extracted features specifically to different purposes (domains and exemplars).
Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations
In this paper, we propose one novel model for point cloud semantic segmentation, which exploits both the local and global structures within the point cloud based on the contextual point representations.
Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos
Temporal sentence grounding in videos aims to detect and localize one target video segment, which semantically corresponds to a given sentence.
Context-Gated Convolution
As such, being aware of the global context, the modulated convolution kernel of our proposed CGC can better extract representative local patterns and compose discriminative features.
Ranked #59 on
Image Classification
on ObjectNet
(using extra training data)
Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis
In this paper, we propose to use a 3D body mesh recovery module to disentangle the pose and shape, which can not only model the joint location and rotation but also characterize the personalized body shape.
Temporally Grounding Language Queries in Videos by Contextual Boundary-aware Prediction
The task of temporally grounding language queries in videos is to temporally localize the best matched video segment corresponding to a given language (sentence).
Controllable Video Captioning with POS Sequence Guidance Based on Gated Fusion Network
In this paper, we propose to guide the video caption generation with Part-of-Speech (POS) information, based on a gated fusion of multiple representations of input videos.
MemeFaceGenerator: Adversarial Synthesis of Chinese Meme-face from Natural Sentences
Chinese meme-face is a special kind of internet subculture widely spread in Chinese Social Community Networks.
Sentence Specified Dynamic Video Thumbnail Generation
With the tremendous growth of videos over the Internet, video thumbnails, providing video content previews, are becoming increasingly crucial to influencing users' online searching experiences.
Position Focused Attention Network for Image-Text Matching
Then, an attention mechanism is proposed to model the relations between the image region and blocks and generate the valuable position feature, which will be further utilized to enhance the region expression and model a more reliable relationship between the visual image and the textual sentence.
Weakly-Supervised Spatio-Temporally Grounding Natural Sentence in Video
In this paper, we address a novel task, namely weakly-supervised spatio-temporally grounding natural sentence in video.
Reconstruct and Represent Video Contents for Captioning via Reinforcement Learning
Unlike previous video captioning work mainly exploiting the cues of video contents to make a language description, we propose a reconstruction network (RecNet) in a novel encoder-decoder-reconstructor architecture, which leverages both forward (video to sentence) and backward (sentence to video) flows for video captioning.
Image Deformation Meta-Networks for One-Shot Learning
Humans can robustly learn novel visual concepts even when images undergo various deformations and lose certain information.
Hallucinating Optical Flow Features for Video Classification
However, extracting motion information, specifically in the form of optical flow features, is extremely computationally expensive, especially for large-scale video classification.
Spatio-temporal Video Re-localization by Warp LSTM
The need for efficiently finding the video content a user wants is increasing because of the erupting of user-generated videos on the Web.
PFLD: A Practical Facial Landmark Detector
Being accurate, efficient, and compact is essential to a facial landmark detector for practical use.
Hierarchical Photo-Scene Encoder for Album Storytelling
In this paper, we propose a novel model with a hierarchical photo-scene encoder and a reconstructor for the task of album storytelling.
Real-Time Referring Expression Comprehension by Single-Stage Grounding Network
Experiments on RefCOCO, RefCOCO+, and RefCOCOg datasets demonstrate that our proposed SSG without relying on any region proposals can achieve comparable performance with other advanced models.
Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation
In this paper, we present a deep convolutional neural network to capture the inherent properties of image degradation, which can handle different kernels and saturated pixels in a unified framework.
Multi-granularity Generator for Temporal Action Proposal
In this paper, we propose a multi-granularity generator (MGG) to perform the temporal action proposal from different granularity perspectives, relying on the video visual features equipped with the position embedding information.
Ranked #2 on
Action Recognition
on THUMOS’14
Unsupervised Image Captioning
Instead of relying on manually labeled image-sentence pairs, our proposed model merely requires an image set, a sentence corpus, and an existing visual concept detector.
Temporally Grounding Natural Sentence in Video
We introduce an effective and efficient method that grounds (i. e., localizes) natural sentences in long, untrimmed video sequences.
Non-local NetVLAD Encoding for Video Classification
This paper describes our solution for the 2$^\text{nd}$ YouTube-8M video understanding challenge organized by Google AI.
Baidu Apollo Auto-Calibration System - An Industry-Level Data-Driven and Learning based Vehicle Longitude Dynamic Calibrating Algorithm
Since manual calibration is not sustainable once entering into mass production stage for industrial purposes, we here introduce a machine-learning based auto-calibration system for autonomous driving vehicles.
Video Re-localization
We first exploit and reorganize the videos in ActivityNet to form a new dataset for video re-localization research, which consists of about 10, 000 videos of diverse visual appearances associated with localized boundary information.
Recurrent Fusion Network for Image Captioning
In this paper, in order to exploit the complementary information from multiple encoders, we propose a novel Recurrent Fusion Network (RFNet) for tackling image captioning.
Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks
Recent studies on unsupervised image-to-image translation have made a remarkable progress by training a pair of generative adversarial networks with a cycle-consistent loss.
Hierarchical Attention-Based Recurrent Highway Networks for Time Series Prediction
Time series prediction has been studied in a variety of domains.
Safe Element Screening for Submodular Function Minimization
Relying on this study, we subsequently propose a novel safe screening method to quickly identify the elements guaranteed to be included (we refer to them as active) or excluded (inactive) in the final optimal solution of SFM during the optimization process.
Long-Term Human Motion Prediction by Modeling Motion Context and Enhancing Motion Dynamic
Human motion prediction aims at generating future frames of human motion based on an observed sequence of skeletons.
Fine-grained Video Attractiveness Prediction Using Multimodal Deep Learning on a Large Real-world Dataset
First, we demonstrate that video attractiveness and different engagements present different relationships.
Learning to Guide Decoding for Image Captioning
Recently, much advance has been made in image captioning, and an encoder-decoder framework has achieved outstanding performance for this task.
Gated Fusion Network for Single Image Dehazing
The proposed algorithm hinges on an end-to-end trainable neural network that consists of an encoder and a decoder.
Ranked #13 on
Image Dehazing
on SOTS Outdoor
Bidirectional Attentive Fusion with Context Gating for Dense Video Captioning
We propose a bidirectional proposal method that effectively exploits both past and future contexts to make proposal predictions.
Reconstruction Network for Video Captioning
Unlike previous video captioning work mainly exploiting the cues of video contents to make a language description, we propose a reconstruction network (RecNet) with a novel encoder-decoder-reconstructor architecture, which leverages both the forward (video to sentence) and backward (sentence to video) flows for video captioning.
Regularizing RNNs for Caption Generation by Reconstructing The Past with The Present
Recently, caption generation with an encoder-decoder framework has been extensively studied and applied in different domains, such as image captioning, code captioning, and so on.
Adversarial Spatio-Temporal Learning for Video Deblurring
To tackle the second challenge, we leverage the developed DBLRNet as a generator in the GAN (generative adversarial network) architecture, and employ a content loss in addition to an adversarial loss for efficient adversarial training.
Neural Stereoscopic Image Style Transfer
While each view of the stereoscopic pair is processed in an individual path, a novel feature aggregation strategy is proposed to effectively share information between the two paths.
Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks
The first stage generates videos of realistic contents for each frame.
Normalized Direction-preserving Adam
Adaptive optimization algorithms, such as Adam and RMSprop, have shown better optimization performance than stochastic gradient descent (SGD) in some scenarios.
Real-Time Neural Style Transfer for Videos
More specifically, a hybrid loss is proposed to capitalize on the content information of input frames, the style information of a given style image, and the temporal information of consecutive frames.
Adaptive Neighboring Selection Algorithm Based on Curvature Prediction in Manifold Learning
By verifying the proposed algorithm on embedding Swiss roll from R3 to R2 based on LLE and ISOMAP algorithm, the simulation results show that the proposed adaptive neighboring selection algorithm is feasible and able to find the optimal value of K, making the residual variance relatively small and better visualization of the results.
Joint Semi-supervised RSS Dimensionality Reduction and Fingerprint Based Algorithm for Indoor Localization
Another significant innovation of this paper is jointing the fingerprint based algorithm with CM-SDE algorithm to improve the localization accuracy of indoor localization.
Local Subspace Collaborative Tracking
To address this, this paper presents a local subspace collaborative tracking method for robust visual tracking, where multiple linear and nonlinear subspaces are learned to better model the nonlinear relationship of object appearances.
Multiple Feature Fusion via Weighted Entropy for Visual Tracking
It is desirable to combine multiple feature descriptors to improve the visual tracking performance because different features can provide complementary information to describe objects of interest.
Learning to Answer Questions From Image Using Convolutional Neural Network
We demonstrate the efficacy of our proposed model on the DAQUAR and COCO-QA datasets, which are two benchmark datasets for the image QA, with the performances significantly outperforming the state-of-the-art.
Multimodal Convolutional Neural Networks for Matching Image and Sentence
In this paper, we propose multimodal convolutional neural networks (m-CNNs) for matching image and sentence.
Ranked #15 on
Image Retrieval
on Flickr30K 1K test
