Search Results for author:
Found 41 papers, 30 papers with code
Conformer: Local Features Coupling Global Representations for Visual Recognition
Within Convolutional Neural Network (CNN), the convolution operations are good at extracting local features but experience difficulty to capture global representations.
Ranked #322 on
Image Classification
on ImageNet
Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification
To this end, we propose to preserve two types of unsupervised similarities, 1) self-similarity of an image before and after translation, and 2) domain-dissimilarity of a translated source image and a target image.
Beyond Bounding-Box: Convex-Hull Feature Adaptation for Oriented and Densely Packed Object Detection
Detecting oriented and densely packed objects remains challenging for spatial feature aliasing caused by the intersection of reception fields between objects.
Ranked #34 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxes
Object detection via inaccurate bounding boxes supervision has boosted a broad interest due to the expensive high-quality annotation data or the occasional inevitability of low annotation quality (\eg tiny objects).
CPR++: Object Localization via Single Coarse Point Supervision
CPR reduces the semantic variance by selecting a semantic centre point in a neighbourhood region to replace the initial annotated point.
Weakly Supervised Instance Segmentation using Class Peak Response
Motivated by this, we first design a process to stimulate peaks to emerge from a class response map.
Ranked #11 on
Image-level Supervised Instance Segmentation
on PASCAL VOC 2012 val
(using extra training data)
General Classification
Image-level Supervised Instance Segmentation
+3
Oriented Response Networks
DCNNs using ARFs, referred to as Oriented Response Networks (ORNs), can produce within-class rotation-invariant deep features while maintaining inter-class discrimination for classification tasks.
Ranked #83 on
Image Classification
on CIFAR-100
(using extra training data)
Soft Proposal Networks for Weakly Supervised Object Localization
Weakly supervised object localization remains challenging, where only image labels instead of bounding boxes are available during training.
Ranked #2 on
Weakly Supervised Object Detection
on MS COCO
Prototype Mixture Models for Few-shot Semantic Segmentation
Few-shot segmentation is challenging because objects within the support and query images could significantly differ in appearance and pose.
Fast-iTPN: Integrally Pre-Trained Transformer Pyramid Network with Token Migration
iTPN is born with two elaborated designs: 1) The first pre-trained feature pyramid upon vision transformer (ViT).
SIXray : A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images
In particular, the advantage of CHR is more significant in the scenarios with fewer positive training samples, which demonstrates its potential application in real-world security inspection.
Adaptive Linear Span Network for Object Skeleton Detection
Conventional networks for object skeleton detection are usually hand-crafted.
Generic-to-Specific Distillation of Masked Autoencoders
Lightweight ViT models limited by the model capacity, however, benefit little from those pre-training mechanisms.
SRN: Side-output Residual Network for Object Symmetry Detection in the Wild
By stacking RUs in a deep-to-shallow manner, SRN exploits the 'flow' of errors among multiple scales to ease the problems of fitting complex outputs with limited layers, suppressing the complex backgrounds, and effectively matching object symmetry of different scales.
SRN: Side-output Residual Network for Object Reflection Symmetry Detection and Beyond
The end-to-end deep learning approach, referred to as a side-output residual network (SRN), leverages the output residual units (RUs) to fit the errors between the object ground-truth symmetry and the side-outputs of multiple stages.
C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection
Weakly supervised object detection (WSOD) is a challenging task when provided with image category supervision but required to simultaneously learn object locations and object detectors.
GraFormer: Graph Convolution Transformer for 3D Pose Estimation
Exploiting relations among 2D joints plays a crucial role yet remains semi-developed in 2D-to-3D pose estimation.
ChatterBox: Multi-round Multimodal Referring and Grounding
In this study, we establish a baseline for a new task named multimodal multi-round referring and grounding (MRG), opening up a promising direction for instance-level multimodal dialogues.
Min-Entropy Latent Model for Weakly Supervised Object Detection
Weakly supervised object detection is a challenging task when provided with image category supervision but required to learn, at the same time, object locations and object detectors.
Semantic-Aware Generation for Self-Supervised Visual Representation Learning
In this paper, we propose a self-supervised visual representation learning approach which involves both generative and discriminative proxies, where we focus on the former part by requiring the target network to recover the original image based on the mid-level features.
Ranked #63 on
Semantic Segmentation
on Cityscapes test
Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers
The past year has witnessed a rapid development of masked image modeling (MIM).
Rethinking Sampling Strategies for Unsupervised Person Re-identification
While extensive research has focused on the framework design and loss function, this paper shows that sampling strategy plays an equally important role.
Discretization-Aware Architecture Search
The search cost of neural architecture search (NAS) has been largely reduced by weight-sharing methods.
Vision-Dialog Navigation by Exploring Cross-modal Memory
Benefiting from the collaborative learning of the L-mem and the V-mem, our CMN is able to explore the memory about the decision making of historical navigation actions which is for the current step.
Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation
Encouraging progress in few-shot semantic segmentation has been made by leveraging features learned upon base classes with sufficient training data to represent novel classes with few-shot examples.
Ranked #68 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
Harmonic Feature Activation for Few-Shot Semantic Segmentation
HFA is formulated as a bilinear model, which takes charge of the pixel-wise dense correlation (bilinear feature activation) between query and support images in a systematic way.
Long-tailed Distribution Adaptation
We propose to jointly optimize empirical risks of the unbalanced and balanced domains and approximate their domain divergence by intra-class and inter-class distances, with the aim to adapt models trained on the long-tailed distribution to general distributions in an interpretable way.
Towards Precise 3D Human Pose Estimation with Multi-Perspective Spatial-Temporal Relational Transformers
Due to the challenges in data collection, mainstream datasets of 3D human pose estimation are primarily composed of multi-view video data collected in laboratory environments, which contains rich spatial-temporal correlation information besides the image frame content.
BadRL: Sparse Targeted Backdoor Attack Against Reinforcement Learning
Our algorithm, BadRL, strategically chooses state observations with high attack values to inject triggers during training and testing, thereby reducing the chances of detection.
Feature-Gate Coupling for Dynamic Network Pruning
Gating modules have been widely explored in dynamic network pruning to reduce the run-time computational cost of deep neural networks while preserving the representation of features.
A scalable convolutional neural network for task-specified scenarios via knowledge distillation
In this paper, we explore the redundancy in convolutional neural network, which scales with the complexity of vision tasks.
Similarity-preserving Image-image Domain Adaptation for Person Re-identification
It first preserves two types of unsupervised similarity, namely, self-similarity of an image before and after translation, and domain-dissimilarity of a translated source image and a target image.
Domain Alignment with Triplets
When aligning the distributions in the embedding space, SCA enforces a similarity-preserving constraint to maintain class-level relations among the source and target images, i. e., if a source image and a target image are of the same class label, their corresponding embeddings are supposed to be aligned nearby, and vise versa.
Self-Motivated Communication Agent for Real-World Vision-Dialog Navigation
Vision-Dialog Navigation (VDN) requires an agent to ask questions and navigate following the human responses to find target objects.
Exploring Complicated Search Spaces with Interleaving-Free Sampling
In this paper, we build the search algorithm upon a complicated search space with long-distance connections, and show that existing weight-sharing search algorithms mostly fail due to the existence of \textbf{interleaved connections}.
P2P-Loc: Point to Point Tiny Person Localization
As a result, we propose a point self-refinement approach that iteratively updates point annotations in a self-paced way.
DQnet: Cross-Model Detail Querying for Camouflaged Object Detection
Optimizing a convolutional neural network (CNN) for camouflaged object detection (COD) tends to activate local discriminative regions while ignoring complete object extent, causing the partial activation issue which inevitably leads to missing or redundant regions of objects.
P2RBox: A Single Point is All You Need for Oriented Object Detection
In this study, we introduce the P2RBox network, which leverages point annotations and a mask generator to create mask proposals, followed by filtration through our Inspector Module and Constrainer Module.
Semantic-aware SAM for Point-Prompted Instance Segmentation
In this paper, we introduce a cost-effective category-specific segmenter using SAM.
Self-supervised Pretraining for Decision Foundation Model: Formulation, Pipeline and Challenges
Decision-making is a dynamic process requiring perception, memory, and reasoning to make choices and find optimal policies.
P2Seg: Pointly-supervised Segmentation via Mutual Distillation
Nevertheless, weakly supervised semantic segmentation methods are proficient in utilizing intra-class feature consistency to capture the boundary contours of the same semantic regions.
