Search Results for author:
Found 44 papers, 23 papers with code
OmniTracker: Unifying Object Tracking by Tracking-with-Detection
Object tracking (OT) aims to estimate the positions of target objects in a video sequence.
Layer Grafted Pre-training: Bridging Contrastive Learning And Masked Image Modeling For Label-Efficient Representations
This motivates us to shift the paradigm from combining loss at the end, to choosing the proper learning method per network layer.
Generalized Decoding for Pixel, Image, and Language
We present X-Decoder, a generalized decoding model that can predict pixel-level segmentation and language tokens seamlessly.
Ranked #3 on
Instance Segmentation
on ADE20K val
(using extra training data)
Look Before You Match: Instance Understanding Matters in Video Object Segmentation
Towards this goal, we present a two-branch network for VOS, where the query-based instance segmentation (IS) branch delves into the instance details of the current frame and the VOS branch performs spatial-temporal matching with the memory bank.
Ranked #1 on
Semi-Supervised Video Object Segmentation
on Long Video Dataset
(using extra training data)
Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learning
For the choice of teacher models, we observe that students taught by video teachers perform better on temporally-heavy video tasks, while image teachers transfer stronger spatial representations for spatially-heavy video tasks.
Ranked #1 on
Action Recognition
on AVA v2.2
(using extra training data)
Self-Supervised Learning based on Heat Equation
When transferring to object detection with frozen backbone, QB-Heat outperforms MoCo-v2 and supervised pre-training on ImageNet by 7. 9 and 4. 5 AP respectively.
Video Mobile-Former: Video Recognition with Efficient Global Spatial-temporal Modeling
To avoid significant computational cost incurred by computing self-attention between the large number of local patches in videos, we propose to use very few global tokens (e. g., 6) for a whole video in Transformers to exchange information with 3D-CNNs with a cross-attention mechanism.
Should All Proposals be Treated Equally in Object Detection?
We formulate this as a learning problem where the goal is to assign operators to proposals, in the detection head, so that the total computational cost is constrained and the precision is maximized.
GLIPv2: Unifying Localization and Vision-Language Understanding
We present GLIPv2, a grounded VL understanding model, that serves both localization tasks (e. g., object detection, instance segmentation) and Vision-Language (VL) understanding tasks (e. g., VQA, image captioning).
Ranked #1 on
Phrase Grounding
on Flickr30k Entities Test
(using extra training data)
Detection Hub: Unifying Object Detection Datasets via Query Adaptation on Language Embedding
We design a detection hub to dynamically adapt queries on category embedding based on the different distributions of datasets.
Visual Clues: Bridging Vision and Language Foundations for Image Paragraph Captioning
Thanks to the strong zero-shot capability of foundation models, we start by constructing a rich semantic representation of the image (e. g., image tags, object attributes / locations, captions) as a structured textual prompt, called visual clues, using a vision foundation model.
Reduce Information Loss in Transformers for Pluralistic Image Inpainting
The indices of quantized pixels are used as tokens for the inputs and prediction targets of transformer.
Ranked #6 on
Seeing Beyond the Visible
on KITTI360-EX
Multimodal Adaptive Distillation for Leveraging Unimodal Encoders for Vision-Language Tasks
Experiments demonstrate that MAD leads to consistent gains in the low-shot, domain-shifted, and fully-supervised conditions on VCR, SNLI-VE, and VQA, achieving SOTA performance on VCR compared to other single models pretrained with image-text data.
Ranked #3 on
Visual Question Answering (VQA)
on VCR (Q-A) test
Residual Mixture of Experts
In this paper, we propose Residual Mixture of Experts (RMoE), an efficient training pipeline for MoE vision transformers on downstream tasks, such as segmentation and detection.
Focal Modulation Networks
For semantic segmentation with UPerNet, FocalNet base at single-scale outperforms Swin by 2. 4, and beats Swin at multi-scale (50. 5 v. s.
Ranked #5 on
Object Detection
on COCO minival
(using extra training data)
CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks
Experiments demonstrate that our proposed CLIP-TD leads to exceptional gains in the low-shot (up to 51. 9%) and domain-shifted (up to 71. 3%) conditions of VCR, while simultaneously improving performance under standard fully-supervised conditions (up to 2%), achieving state-of-art performance on VCR compared to other single models that are pretrained with image-text data only.
RegionCLIP: Region-based Language-Image Pretraining
However, we show that directly applying such models to recognize image regions for object detection leads to poor performance due to a domain shift: CLIP was trained to match an image as a whole to a text description, without capturing the fine-grained alignment between image regions and text spans.
Ranked #4 on
Open Vocabulary Object Detection
on MSCOCO
(using extra training data)
BEVT: BERT Pretraining of Video Transformers
This design is motivated by two observations: 1) transformers learned on image datasets provide decent spatial priors that can ease the learning of video transformers, which are often times computationally-intensive if trained from scratch; 2) discriminative clues, i. e., spatial and temporal information, needed to make correct predictions vary among different videos due to large intra-class and inter-class variations.
Ranked #4 on
Action Recognition
on Diving-48
Focal Attention for Long-Range Interactions in Vision Transformers
With focal attention, we propose a new variant of Vision Transformer models, called Focal Transformers, which achieve superior performance over the state-of-the-art (SoTA) Vision Transformers on a range of public image classification and object detection benchmarks.
Florence: A New Foundation Model for Computer Vision
Computer vision foundation models, which are trained on diverse, large-scale dataset and can be adapted to a wide range of downstream tasks, are critical for this mission to solve real-world computer vision applications.
Ranked #1 on
Action Recognition In Videos
on Kinetics-600
UFO: A UniFied TransfOrmer for Vision-Language Representation Learning
In this paper, we propose a single UniFied transfOrmer (UFO), which is capable of processing either unimodal inputs (e. g., image or language) or multimodal inputs (e. g., the concatenation of the image and the question), for vision-language (VL) representation learning.
MicroNet: Improving Image Recognition with Extremely Low FLOPs
This paper aims at addressing the problem of substantial performance degradation at extremely low computational cost (e. g. 5M FLOPs on ImageNet classification).
Mobile-Former: Bridging MobileNet and Transformer
This structure leverages the advantages of MobileNet at local processing and transformer at global interaction.
Focal Self-attention for Local-Global Interactions in Vision Transformers
With focal self-attention, we propose a new variant of Vision Transformer models, called Focal Transformer, which achieves superior performance over the state-of-the-art vision Transformers on a range of public image classification and object detection benchmarks.
Ranked #14 on
Instance Segmentation
on COCO test-dev
Efficient Self-supervised Vision Transformers for Representation Learning
This paper investigates two techniques for developing efficient self-supervised vision transformers (EsViT) for visual representation learning.
Ranked #3 on
Self-Supervised Image Classification
on ImageNet
Representation Learning
Self-Supervised Image Classification
Dynamic Head: Unifying Object Detection Heads with Attentions
In this paper, we present a novel dynamic head framework to unify object detection heads with attentions.
Ranked #1 on
Object Detection
on COCO 2017 val
(AP75 metric)
Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding
This paper presents a new Vision Transformer (ViT) architecture Multi-Scale Vision Longformer, which significantly enhances the ViT of \cite{dosovitskiy2020image} for encoding high-resolution images using two techniques.
Ranked #32 on
Instance Segmentation
on COCO minival
CvT: Introducing Convolutions to Vision Transformers
We present in this paper a new architecture, named Convolutional vision Transformer (CvT), that improves Vision Transformer (ViT) in performance and efficiency by introducing convolutions into ViT to yield the best of both designs.
Ranked #2 on
Image Classification
on Flowers-102
(using extra training data)
Revisiting Dynamic Convolution via Matrix Decomposition
It has two limitations: (a) it increases the number of convolutional weights by K-times, and (b) the joint optimization of dynamic attention and static convolution kernels is challenging.
Stronger NAS with Weaker Predictors
We propose a paradigm shift from fitting the whole architecture space using one strong predictor, to progressively fitting a search path towards the high-performance sub-space through a set of weaker predictors.
Dynamic DETR: End-to-End Object Detection With Dynamic Attention
To mitigate the second limitation of learning difficulty, we introduce a dynamic decoder by replacing the cross-attention module with a ROI-based dynamic attention in the Transformer decoder.
Weak NAS Predictor Is All You Need
Rather than expecting a single strong predictor to model the whole space, we seek a progressive line of weak predictors that can connect a path to the best architecture, thus greatly simplifying the learning task of each predictor.
MicroNet: Towards Image Recognition with Extremely Low FLOPs
In this paper, we present MicroNet, which is an efficient convolutional neural network using extremely low computational cost (e. g. 6 MFLOPs on ImageNet classification).
DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search
One common way is searching on a smaller proxy dataset (e. g., CIFAR-10) and then transferring to the target task (e. g., ImageNet).
Dynamic Convolution: Attention over Convolution Kernels
Light-weight convolutional neural networks (CNNs) suffer performance degradation as their low computational budgets constrain both the depth (number of convolution layers) and the width (number of channels) of CNNs, resulting in limited representation capability.
Ranked #801 on
Image Classification
on ImageNet
TAN: Temporal Aggregation Network for Dense Multi-label Action Recognition
We present Temporal Aggregation Network (TAN) which decomposes 3D convolutions into spatial and temporal aggregation blocks.
MAN: Moment Alignment Network for Natural Language Moment Retrieval via Iterative Graph Adjustment
In this paper, we present Moment Alignment Network (MAN), a novel framework that unifies the candidate moment encoding and temporal structural reasoning in a single-shot feed-forward network.
Dynamic Temporal Pyramid Network: A Closer Look at Multi-Scale Modeling for Activity Detection
(3) We further exploit the temporal context of activities by appropriately fusing multi-scale feature maps, and demonstrate that both local and global temporal contexts are important.
S3D: Single Shot multi-Span Detector via Fully 3D Convolutional Networks
In this paper, we present a novel Single Shot multi-Span Detector for temporal activity detection in long, untrimmed videos using a simple end-to-end fully three-dimensional convolutional (Conv3D) network.
Deep Motion Boundary Detection
In this paper, we propose the first dedicated end-to-end deep learning approach for motion boundary detection, which we term as MoBoNet.
Temporal Context Network for Activity Localization in Videos
For each temporal segment inside a proposal, features are uniformly sampled at a pair of scales and are input to a temporal convolutional neural network for classification.
Ranked #7 on
Action Recognition
on THUMOS’14
FASON: First and Second Order Information Fusion Network for Texture Recognition
We then build a multi-level deep architecture to exploit the first and second order information within different convolutional layers.
Parameterizing Region Covariance: An Efficient Way To Apply Sparse Codes On Second Order Statistics
Sparse representations have been successfully applied to signal processing, computer vision and machine learning.
