Search Results for author:
Found 147 papers, 81 papers with code
Content-Consistent Matching for Domain Adaptive Semantic Segmentation
The target of CCM is to acquire those synthetic images that share similar distribution with the real ones in the target domain, so that the domain gap can be naturally alleviated by employing the content-consistent synthetic images for training.
Ranked #12 on
Semantic Segmentation
on GTAV-to-Cityscapes Labels
Behavior Backdoor for Deep Learning Models
Specifically, to adapt the optimization goal of behavior backdoor, we introduce the behavior-driven backdoor object optimizing method by a bi-target behavior backdoor training loss, thus we could guide the poisoned model optimization direction.
BGM: Background Mixup for X-ray Prohibited Items Detection
The approach introduces 1) contour information of baggage and 2) variation of material information into the original image by Mixup at patch level.
ROSE: Revolutionizing Open-Set Dense Segmentation with Patch-Wise Perceptual Large Multimodal Model
Advances in CLIP and large multimodal models (LMMs) have enabled open-vocabulary and free-text segmentation, yet existing models still require predefined category prompts, limiting free-form category self-generation.
Dual-Level Boost Network for Long-Tail Prohibited Items Detection in X-ray Security Inspection
The detection of prohibited items in X-ray security inspections is vital for ensuring public safety.
Dual-view X-ray Detection: Can AI Detect Prohibited Items from Dual-view X-ray Images like Humans?
To emulate human intelligence in dual-view detection, we propose the Auxiliary-view Enhanced Network (AENet), a novel detection framework that leverages both the main and auxiliary views of the same object.
Who Can Withstand Chat-Audio Attacks? An Evaluation Benchmark for Large Language Models
We evaluate six state-of-the-art LLMs with voice interaction capabilities, including Gemini-1. 5-Pro, GPT-4o, and others, using three distinct evaluation methods on the CAA benchmark.
Collaborative Feature-Logits Contrastive Learning for Open-Set Semi-Supervised Object Detection
However, in open-set scenarios, the unlabeled dataset contains both in-distribution (ID) classes and out-of-distribution (OOD) classes.
Foundations and Recent Trends in Multimodal Mobile Agents: A Survey
This survey provides a comprehensive review of mobile agent technologies, focusing on recent advancements that enhance real-time adaptability and multimodal interaction.
PSVMA+: Exploring Multi-granularity Semantic-visual Adaption for Generalized Zero-shot Learning
Generalized zero-shot learning (GZSL) endeavors to identify the unseen categories using knowledge from the seen domain, necessitating the intrinsic interactions between the visual features and attribute semantic features.
Bridge the Points: Graph-based Few-shot Segment Anything Semantically
Another subsequent Point-Mask Clustering module aligns the granularity of masks and selected points as a directed graph, based on mask coverage over points.
Ranked #2 on
Few-Shot Semantic Segmentation
on COCO-20i (5-shot)
Collapsed Language Models Promote Fairness
To mitigate societal biases implicitly encoded in recent successful pretrained language models, a diverse array of approaches have been proposed to encourage model fairness, focusing on prompting, data augmentation, regularized fine-tuning, and more.
SLCA++: Unleash the Power of Sequential Fine-tuning for Continual Learning with Pre-training
Considering that the overly fast representation learning and the biased classification layer constitute this particular problem, we introduce the advanced Slow Learner with Classifier Alignment (SLCA++) framework to unleash the power of Seq FT, serving as a strong baseline approach for CLPT.
DreamLCM: Towards High-Quality Text-to-3D Generation via Latent Consistency Model
Secondly, we propose a dual timestep strategy, increasing the consistency of guidance and optimizing 3D models from geometry to appearance in DreamLCM.
AppAgent v2: Advanced Agent for Flexible Mobile Interactions
In the deployment phase, RAG technology enables efficient retrieval and update from this knowledge base, thereby empowering the agent to perform tasks effectively and accurately.
Collaborative Vision-Text Representation Optimizing for Open-Vocabulary Segmentation
In this way, the vision and text representation of CLIP are optimized collaboratively, enhancing the alignment of the vision-text feature space.
Ranked #2 on
Open Vocabulary Semantic Segmentation
on ADE20K-847
Open Vocabulary Panoptic Segmentation
Open Vocabulary Semantic Segmentation
ACTRESS: Active Retraining for Semi-supervised Visual Grounding
Building upon this, ACTRESS consists of an active sampling strategy and a selective retraining strategy.
PVUW 2024 Challenge on Complex Video Understanding: Methods and Results
Moreover, we provide a new motion expression guided video segmentation dataset MeViS to study the natural language-guided video understanding in complex environments.
LayerMatch: Do Pseudo-labels Benefit All Layers?
Our approach, LayerMatch, which integrates these two strategies, can avoid the severe interference of noisy pseudo-labels in the linear classification layer while accelerating the clustering capability of the feature extraction layer.
Frozen CLIP: A Strong Backbone for Weakly Supervised Semantic Segmentation
Specifically, the frozen CLIP model is applied as the backbone for semantic feature extraction, and a new decoder is designed to interpret extracted semantic features for final prediction.
Instructing Prompt-to-Prompt Generation for Zero-Shot Learning
Zero-shot learning (ZSL) aims to explore the semantic-visual interactions to discover comprehensive knowledge transferred from seen categories to classify unseen categories.
Intent3D: 3D Object Detection in RGB-D Scans Based on Human Intention
To achieve detection based on human intention, it relies on humans to observe the scene, reason out the target that aligns with their intention ("pillow" in this case), and finally provide a reference to the AI system, such as "A pillow on the couch".
ClassDiffusion: More Aligned Personalization Tuning with Explicit Class Guidance
Recent text-to-image customization works have been proven successful in generating images of given concepts by fine-tuning the diffusion models on a few examples.
The SkatingVerse Workshop & Challenge: Methods and Results
The SkatingVerse Workshop & Challenge aims to encourage research in developing novel and accurate methods for human action understanding.
Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models
Building on this foundation, we propose a strategy to migrate the temporal consistency in video diffusion models to the spatial-temporal consistency required for 4D generation.
Transferable and Principled Efficiency for Open-Vocabulary Segmentation
In the context of efficient OVS, we target achieving performance that is comparable to or even better than prior OVS works based on large vision-language foundation models, by utilizing smaller models that incur lower training costs.
Learning Trimaps via Clicks for Image Matting
Despite significant advancements in image matting, existing models heavily depend on manually-drawn trimaps for accurate results in natural image scenarios.
Modeling the Label Distributions for Weakly-Supervised Semantic Segmentation
Based on the label distributions, we leverage the GMM to generate high-quality pseudo labels for more reliable supervision.
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation
Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning
Consequently, these detectors have exhibited a lack of proficiency in learning the frequency domain and tend to overfit to the artifacts present in the training data, leading to suboptimal performance on unseen sources.
Learning Hierarchical Color Guidance for Depth Map Super-Resolution
On the one hand, the low-level detail embedding module is designed to supplement high-frequency color information of depth features in a residual mask manner at the low-level stages.
Data-Independent Operator: A Training-Free Artifact Representation Extractor for Generalizable Deepfake Detection
Due to its unbias towards both the training and test sources, we define it as Data-Independent Operator (DIO) to achieve appealing improvements on unseen sources.
Region-Adaptive Transform with Segmentation Prior for Image Compression
However, there is no prior research on neural transform that focuses on specific regions.
Ranked #1 on
Image Compression
on kodak
4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency
Our pipeline facilitates controllable 4D generation, enabling users to specify the motion via monocular video or adopt image-to-video generations, thus offering superior control over content creation.
Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection
In this paper, we study the problem of generalizable synthetic image detection, aiming to detect forgery images from diverse generative methods, e. g., GANs and diffusion models.
SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process
We propose a model-agnostic solution called SegRefiner, which offers a novel perspective on this problem by interpreting segmentation refinement as a data generation process.
Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection
Recently, the proliferation of highly realistic synthetic images, facilitated through a variety of GANs and Diffusions, has significantly heightened the susceptibility to misuse.
Diffusion for Natural Image Matting
However, the presence of high computational overhead and the inconsistency of noise sampling between the training and inference processes pose significant obstacles to achieving this goal.
Ranked #1 on
Image Matting
on Distinctions-646
PixelLM: Pixel Reasoning with Large Multimodal Model
PixelLM excels across various pixel-level image reasoning and understanding tasks, outperforming well-established methods in multiple benchmarks, including MUSE, single- and multi-referring segmentation.
CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation
However, most state-of-the-art methods use the freeze strategy for stability, which compromises the model's plasticity. In contrast, releasing parameter training for plasticity could lead to the best performance for all categories, but this requires discriminative feature representation. Therefore, we prioritize the model's plasticity and propose the Contrast inter- and intra-class representations for Incremental Segmentation (CoinSeg), which pursues discriminative representations for flexible parameter tuning.
Learning Mask-aware CLIP Representations for Zero-Shot Segmentation
However, in the paper, we reveal that CLIP is insensitive to different mask proposals and tends to produce similar predictions for various mask proposals of the same image.
Open Vocabulary Semantic Segmentation
Zero Shot Segmentation
Unified Frequency-Assisted Transformer Framework for Detecting and Grounding Multi-Modal Manipulation
Moreover, to address the semantic conflicts between image and frequency domains, the forgery-aware mutual module is developed to further enable the effective interaction of disparate image and frequency features, resulting in aligned and comprehensive visual forgery representations.
StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data
However, these datasets often exhibit domain bias, potentially constraining the generative capabilities of the models.
Ranked #116 on
Visual Question Answering
on MM-Vet
CTP: Towards Vision-Language Continual Pretraining via Compatible Momentum Contrast and Topology Preservation
Regarding the growing nature of real-world data, such an offline training paradigm on ever-expanding data is unsustainable, because models lack the continual learning ability to accumulate knowledge constantly.
CLE Diffusion: Controllable Light Enhancement Diffusion Model
Low light enhancement has gained increasing importance with the rapid development of visual creation and editing.
Disentangled Pre-training for Image Matting
The pre-training task is designed in a similar manner as image matting, where random trimap and alpha matte are generated to achieve an image disentanglement objective.
Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning
Generalized Zero-Shot Learning (GZSL) identifies unseen categories by knowledge transferred from the seen domain, relying on the intrinsic interactions between visual and semantic information.
Global Knowledge Calibration for Fast Open-Vocabulary Segmentation
Recent advancements in pre-trained vision-language models, such as CLIP, have enabled the segmentation of arbitrary concepts solely from textual inputs, a process commonly referred to as open-vocabulary semantic segmentation (OVS).
Knowledge Distillation
Open Vocabulary Semantic Segmentation
+4
SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model
The goal of continual learning is to improve the performance of recognition models in learning sequentially arrived data.
Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection
The key of fake image detection is to develop a generalized representation to describe the artifacts produced by generation models.
Learning To Segment Every Referring Object Point by Point
Extensive experiments show that our model achieves 52. 06% in terms of accuracy (versus 58. 93% in fully supervised setting) on RefCOCO+@testA, when only using 1% of the mask annotations.
Adversarially Masking Synthetic To Mimic Real: Adaptive Noise Injection for Point Cloud Segmentation Adaptation
With the help of adversarial training, the masking module can learn to generate source masks to mimic the pattern of irregular target noise, thereby narrowing the domain gap.
CTP:Towards Vision-Language Continual Pretraining via Compatible Momentum Contrast and Topology Preservation
Regarding the growing nature of real-world data, such an offline training paradigm on ever-expanding data is unsustainable, because models lack the continual learning ability to accumulate knowledge constantly.
Locating Noise is Halfway Denoising for Semi-Supervised Segmentation
This work shows that locating the patch-wise noisy region is a better way to deal with noise.
Mask Matching Transformer for Few-Shot Segmentation
Typical methods follow the paradigm to firstly learn prototypical features from support images and then match query features in pixel-level to obtain segmentation results.
Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation
Our MicroSeg is based on the assumption that background regions with strong objectness possibly belong to those concepts in the historical or future stages.
Class-Incremental Semantic Segmentation
Continual Learning
+1
VMFormer: End-to-End Video Matting with Transformer
In this paper, we propose VMFormer: a transformer-based end-to-end method for video matting.
Instance As Identity: A Generic Online Paradigm for Video Instance Segmentation
In this work, we propose a new online VIS paradigm named Instance As Identity (IAI), which models temporal information for both detection and tracking in an efficient way.
SiRi: A Simple Selective Retraining Mechanism for Transformer-based Visual Grounding
Particularly, SiRi conveys a significant principle to the research of visual grounding, i. e., a better initialized vision-language encoder would help the model converge to a better local minimum, advancing the performance accordingly.
Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval
Our goal in this research is to study a more realistic environment in which we can conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories.
FisheyeEX: Polar Outpainting for Extending the FoV of Fisheye Lens
For the distortion synthesis, we propose a spiral distortion-aware perception module, in which the learning path keeps consistent with the distortion prior of the fisheye image.
Cylin-Painting: Seamless {360\textdegree} Panoramic Image Outpainting and Beyond
Motivated by this analysis, we present a Cylin-Painting framework that involves meaningful collaborations between inpainting and outpainting and efficiently fuses the different arrangements, with a view to leveraging their complementary benefits on a seamless cylinder.
L2G: A Simple Local-to-Global Knowledge Transfer Framework for Weakly Supervised Semantic Segmentation
Our framework conducts the global network to learn the captured rich object detail knowledge from a global view and thereby produces high-quality attention maps that can be directly used as pseudo annotations for semantic segmentation networks.
Ranked #17 on
Weakly-Supervised Semantic Segmentation
on PASCAL VOC 2012 test
(using extra training data)
Scalable Video Object Segmentation with Identification Mechanism
This paper delves into the challenges of achieving scalable and effective multi-object modeling for semi-supervised Video Object Segmentation (VOS).
Large-Scale Video Panoptic Segmentation in the Wild: A Benchmark
In contrast, our large-scale VIdeo Panoptic Segmentation in the Wild (VIPSeg) dataset provides 3, 536 videos and 84, 750 frames with pixel-level panoptic annotations, covering a wide range of real-world scenarios and categories.
Clicking Matters:Towards Interactive Human Parsing
In this work, we focus on Interactive Human Parsing (IHP), which aims to segment a human image into multiple human body parts with guidance from users' interactions.
M5Product: Self-harmonized Contrastive Learning for E-commercial Multi-modal Pretraining
Despite the potential of multi-modal pre-training to learn highly discriminative feature representations from complementary data modalities, current progress is being slowed by the lack of large-scale modality-diverse datasets.
Understanding and Accelerating Neural Architecture Search with Training-Free and Theory-Grounded Metrics
This work targets designing a principled and unified training-free framework for Neural Architecture Search (NAS), with high performance, low cost, and in-depth interpretation.
Generating Superpixels for High-resolution Images with Decoupled Patch Calibration
Superpixel segmentation has recently seen important progress benefiting from the advances in differentiable deep learning.
Product1M: Towards Weakly Supervised Instance-Level Product Retrieval via Cross-modal Pretraining
In this paper, we investigate a more realistic setting that aims to perform weakly-supervised multi-modal instance-level product retrieval among fine-grained product categories.
LayerCAM: Exploring Hierarchical Class Activation Maps for Localization
To evaluate the quality of the class activation maps produced by LayerCAM, we apply them to weakly-supervised object localization and semantic segmentation.
ReGO: Reference-Guided Outpainting for Scenery Image
We aim to tackle the challenging yet practical scenery image outpainting task in this work.
Automated Deepfake Detection
In this paper, we propose to utilize Automated Machine Learning to adaptively search a neural architecture for deepfake detection.
VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild
To the best of our knowledge, our VSPW is the first attempt to tackle the challenging video scene parsing task in the wild by considering diverse scenarios.
Affinity Attention Graph Neural Network for Weakly Supervised Semantic Segmentation
More importantly, our approach can be readily applied to bounding box supervised instance segmentation task or other weakly supervised semantic segmentation tasks, with state-of-the-art or comparable performance among almot all weakly supervised tasks on PASCAL VOC or COCO dataset.
Box-supervised Instance Segmentation
Graph Neural Network
+4
Domain Consensus Clustering for Universal Domain Adaptation
To better exploit the intrinsic structure of the target domain, we propose Domain Consensus Clustering (DCC), which exploits the domain consensus knowledge to discover discriminative clusters on both common samples and private ones.
Ranked #4 on
Partial Domain Adaptation
on Office-31
Few-Shot Segmentation via Cycle-Consistent Transformer
Directly performing cross-attention may aggregate these features from support to query and bias the query features.
Ranked #55 on
Few-Shot Semantic Segmentation
on PASCAL-5i (1-Shot)
Associating Objects with Transformers for Video Object Segmentation
The state-of-the-art methods learn to decode features with a single positive object and thus have to match and segment each target separately under multi-object scenarios, consuming multiple times computing resources.
Ranked #2 on
Video Object Segmentation
on DAVIS 2017 (test-dev)
(using extra training data)
Rethinking Cross-modal Interaction from a Top-down Perspective for Referring Video Object Segmentation
Referring video object segmentation (RVOS) aims to segment video objects with the guidance of natural language reference.
Cross-Modal Progressive Comprehension for Referring Segmentation
In this paper, we propose a Cross-Modal Progressive Comprehension (CMPC) scheme to effectively mimic human behaviors and implement it as a CMPC-I (Image) module and a CMPC-V (Video) module to improve referring image and video segmentation models.
Ranked #7 on
Referring Expression Segmentation
on J-HMDB
Decoupled Spatial Temporal Graphs for Generic Visual Grounding
Visual grounding is a long-lasting problem in vision-language understanding due to its diversity and complexity.
AINet: Association Implantation for Superpixel Segmentation
However, simply applying a series of convolution operations with limited receptive fields can only implicitly perceive the relations between the pixel and its surrounding grids.
Towards Complete Scene and Regular Shape for Distortion Rectification by Curve-Aware Extrapolation
To our knowledge, we are the first to tackle the challenging rectification via outpainting, and our curve-aware strategy can reach a rectification construction with complete content and regular shape.
Consistent Structural Relation Learning for Zero-Shot Segmentation
Concretely, by exploring the pair-wise and list-wise structures, we impose the relations of generated visual features to be consistent with their counterparts in the semantic word embedding space.
Delving Deep into Label Smoothing
Experiments demonstrate that based on the same classification models, the proposed approach can effectively improve the classification performance on CIFAR-100, ImageNet, and fine-grained datasets.
Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation
The conventional solution to this task is to minimize the discrepancy between source and target to enable effective knowledge transfer.
Ranked #25 on
Synthetic-to-Real Translation
on SYNTHIA-to-Cityscapes
LID 2020: The Learning from Imperfect Data Challenge Results
The purpose of the Learning from Imperfect Data (LID) workshop is to inspire and facilitate the research in developing novel approaches that would harness the imperfect data and improve the data-efficiency during training.
Collaborative Video Object Segmentation by Multi-Scale Foreground-Background Integration
This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation.
Referring Image Segmentation via Cross-Modal Progressive Comprehension
In addition to the CMPC module, we further leverage a simple yet effective TGFE module to integrate the reasoned multimodal features from different levels with the guidance of textual information.
DONet: Dual Objective Networks for Skin Lesion Segmentation
Skin lesion segmentation is a crucial step in the computer-aided diagnosis of dermoscopic images.
Inter-Image Communication for Weakly Supervised Localization
We learn a feature center for each category and realize the global feature consistency by forcing the object features to approach class-specific centers.
Sketch-Guided Scenery Image Outpainting
In this work, we take the image outpainting one step forward by allowing users to harvest personal custom outpainting results using sketches as the guidance.
Rethinking Localization Map: Towards Accurate Object Perception with Self-Enhancement Maps
To fulfill the direct evaluation, we annotate pixel-level object masks on the ILSVRC validation set.
Focus Longer to See Better:Recursively Refined Attention for Fine-Grained Image Classification
In this paper, we tried to focus on these marginal differences to extract more representative features.
Omni-supervised Facial Expression Recognition via Distilled Data
However, the performance of the current state-of-the-art facial expression recognition (FER) approaches is directly related to the labeled data for training.
The 1st Agriculture-Vision Challenge: Methods and Results
The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset.
Referring Image Segmentation by Generative Adversarial Learning
To this end, we propose to train the referring image segmentation model in a generative adversarial fashion, which well addresses the distribution similarity problem.
VehicleNet: Learning Robust Visual Representation for Vehicle Re-identification
This stage relaxes the full alignment between the training and testing domains, as it is agnostic to the target vehicle domain.
Ranked #1 on
Vehicle Re-Identification
on VehicleID
Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation Method for Semantic Segmentation
A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i. e. reducing domain shift.
Memory Aggregation Networks for Efficient Interactive Video Object Segmentation
Interactive video object segmentation (iVOS) aims at efficiently harvesting high-quality segmentation masks of the target object in a video with user interactions.
Ranked #5 on
Interactive Video Object Segmentation
on DAVIS 2017
(AUC-J metric)
Laplacian Denoising Autoencoder
This can be naturally generalized to span multiple scales with a Laplacian pyramid representation of the input data.
Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation
We consider the problem of unsupervised domain adaptation for semantic segmentation by easing the domain shift between the source domain (synthetic data) and the target domain (real data) in this work.
Ranked #8 on
Semantic Segmentation
on DensePASS
Collaborative Video Object Segmentation by Foreground-Background Integration
This paper investigates the principles of embedding learning to tackle the challenging semi-supervised video object segmentation.
Ranked #8 on
Video Object Segmentation
on YouTube-VOS 2019
University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization
To our knowledge, University-1652 is the first drone-based geo-localization dataset and enables two new tasks, i. e., drone-view target localization and drone navigation.
Ranked #6 on
Drone navigation
on University-1652
AlignSeg: Feature-Aligned Segmentation Networks
Aggregating features in terms of different convolutional blocks or contextual embeddings has been proven to be an effective way to strengthen feature representations for semantic segmentation.
Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis
To encourage research in computer vision for agriculture, we present Agriculture-Vision: a large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns.
Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation Approach
Such reliable regions are then directly served as ground-truth labels for the parallel segmentation branch, where a newly designed dense energy loss function is adopted for optimization.
Ranked #22 on
Semantic Segmentation
on PASCAL VOC 2012 val
Self-Correction for Human Parsing
To tackle the problem of learning with label noises, this work introduces a purification strategy, called Self-Correction for Human Parsing (SCHP), to progressively promote the reliability of the supervised labels as well as the learned models.
Ranked #2 on
Human Part Segmentation
on PASCAL-Part
SPGNet: Semantic Prediction Guidance for Scene Parsing
The multi-scale context module refers to the operations to aggregate feature responses from a large spatial extent, while the single-stage encoder-decoder structure encodes the high-level semantic information in the encoder path and recovers the boundary information in the decoder path.
CCNet: Criss-Cross Attention for Semantic Segmentation
Compared with the non-local block, the proposed recurrent criss-cross attention module requires 11x less GPU memory usage.
Ranked #7 on
Semantic Segmentation
on FoodSeg103
(using extra training data)
Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification
Upon our SSG, we further introduce a clustering-guided semisupervised approach named SSG ++ to conduct the one-shot domain adaption in an open set setting (i. e. the number of independent identities from the target domain is unknown).
A Simple Non-i.i.d. Sampling Approach for Efficient Training and Better Generalization
While training on samples drawn from independent and identical distribution has been a de facto paradigm for optimizing image classification networks, humans learn new concepts in an easy-to-hard manner and on the selected examples progressively.
STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification
Thus, a more robust clip-level feature representation can be generated according to a weighted sum operation guided by the mined 2-D attention score matrix.
Large-Scale Person Re-Identification
Video-Based Person Re-Identification
Weakly Supervised Scene Parsing with Point-based Distance Metric Learning
Semantic scene parsing is suffering from the fact that pixel-level annotations are hard to be collected.
Self-Erasing Network for Integral Object Attention
To test the quality of the generated attention maps, we employ the mined object regions as heuristic cues for learning semantic segmentation models.
SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation
In this way, the possibilities embedded in the produced similarity maps can be adapted to guide the process of segmenting objects.
Ranked #92 on
Few-Shot Semantic Segmentation
on PASCAL-5i (5-Shot)
Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection
In particular, DCR places a separate classification network in parallel with the localization network (base detector).
Devil in the Details: Towards Accurate Single and Multiple Human Parsing
Human parsing has received considerable interest due to its wide application potentials.
Ranked #2 on
Person Re-Identification
on Market-1501-C
Self-produced Guidance for Weakly-supervised Object Localization
A stagewise approach is proposed to incorporate high confident object regions to learn the SPG masks.
Ranked #1 on
Weakly-Supervised Object Localization
on ILSVRC 2015
TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection
This work provides a simple approach to discover tight object bounding boxes with only image-level supervision, called Tight box mining with Surrounding Segmentation Context (TS2C).
Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation
Despite remarkable progress, weakly supervised segmentation methods are still inferior to their fully supervised counterparts.
Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation
It can produce dense and reliable object localization maps and effectively benefit both weakly- and semi- supervised semantic segmentation.
Adversarial Complementary Learning for Weakly Supervised Object Localization
With such an adversarial learning, the two parallel-classifiers are forced to leverage complementary object regions for classification and can finally generate integral object localization together.
Ranked #2 on
Weakly-Supervised Object Localization
on ILSVRC 2016
Horizontal Pyramid Matching for Person Re-identification
Despite the remarkable recent progress, person re-identification (Re-ID) approaches are still suffering from the failure cases where the discriminative body parts are missing.
Ranked #58 on
Person Re-Identification
on DukeMTMC-reID
Left-Right Comparative Recurrent Model for Stereo Matching
Left-right consistency check is an effective way to enhance the disparity estimation by referring to the information from the opposite view.
Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
Recent region-based object detectors are usually built with separate classification and localization branches on top of shared feature extraction networks.
Transferable Semi-supervised Semantic Segmentation
The performance of deep learning based semantic segmentation models heavily depends on sufficient data with careful annotations.
Learning to Segment Human by Watching YouTube
An intuition on human segmentation is that when a human is moving in a video, the video-context (e. g., appearance and motion clues) may potentially infer reasonable mask information for the whole human body.
Regional Interactive Image Segmentation Networks
The interactive image segmentation model allows users to iteratively add new inputs for refinement until a satisfactory result is finally obtained.
Ranked #10 on
Interactive Segmentation
on SBD
(NoC@85 metric)
Self-explanatory Deep Salient Object Detection
Through visualizing the differences, we can interpret the capability of different deep neural networks based saliency detection models and demonstrate that our proposed model indeed uses more reasonable structure for salient object detection.
Perceptual Generative Adversarial Networks for Small Object Detection
In this work, we address the small object detection problem by developing a single architecture that internally lifts representations of small objects to "super-resolved" ones, achieving similar characteristics as large objects and thus more discriminative for detection.
Multiple-Human Parsing in the Wild
To address the multi-human parsing problem, we introduce a new multi-human parsing (MHP) dataset and a novel multi-human parsing model named MH-Parser.
Ranked #3 on
Multi-Human Parsing
on MHP v1.0
IAN: The Individual Aggregation Network for Person Search
In addition, to relieve the negative effect caused by varying visual appearances of the same individual, IAN introduces a novel center loss that can increase the intra-class compactness of feature representations.
Deep Self-Taught Learning for Weakly Supervised Object Localization
To overcome this issue, we propose a deep self-taught learning approach, which makes the detector learn the object-level features reliable for acquiring tight positive samples and afterwards re-train itself based on them.
Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach
We investigate a principle way to progressively mine discriminative object regions using classification networks to address the weakly-supervised semantic segmentation problems.
Bottom-Up Top-Down Cues for Weakly-Supervised Semantic Segmentation
We focus on the following three aspects of EM: (i) initialization; (ii) latent posterior estimation (E-step) and (iii) the parameter update (M-step).
Weakly supervised Semantic Segmentation
Weakly-Supervised Semantic Segmentation