Search Results for author:
Found 67 papers, 47 papers with code
Uncertainty-o: One Model-agnostic Framework for Unveiling Uncertainty in Large Multimodal Models
Large Multimodal Models (LMMs), harnessing the complementarity among diverse modalities, are often considered more robust than pure Language Large Models (LLMs); yet do LMMs know what they do not know?
SEED: A Benchmark Dataset for Sequential Facial Attribute Editing with Diffusion Models
However, sequential editing introduces significant challenges in edit attribution and detection robustness, further complicated by the lack of large-scale, finely annotated benchmarks tailored explicitly for this task.
Echo Planning for Autonomous Driving: From Current Observations to Future Trajectories and Back
Modern end-to-end autonomous driving systems suffer from a critical limitation: their planners lack mechanisms to enforce temporal consistency between predicted trajectories and evolving scene dynamics.
CAMeL: Cross-modality Adaptive Meta-Learning for Text-based Person Retrieval
Due to the high cost of annotation and privacy protection, researchers resort to synthesized data for the paradigm of pretraining and fine-tuning.
Every Painting Awakened: A Training-free Framework for Painting-to-Animation Generation
Our approach introduces synthetic proxy images through two key innovations: (1) Dual-path score distillation: We employ a dual-path architecture to distill motion priors from both real and synthetic data, preserving static details from the original painting while learning dynamic characteristics from synthetic frames.
EQ-TAA: Equivariant Traffic Accident Anticipation via Diffusion-Based Accident Video Synthesis
Traffic Accident Anticipation (TAA) in traffic scenes is a challenging problem for achieving zero fatalities in the future.
Scale Up Composed Image Retrieval Learning via Modification Text Generation
To address this issue, this paper proposes to synthesize the training triplets to augment the training resource for the CIR problem.
A Large-scale Interpretable Multi-modality Benchmark for Facial Image Forgery Localization
Leveraging the MMTT dataset, we develop ForgeryTalker, an architecture designed for concurrent forgery localization and interpretation.
Relative Distance Guided Dynamic Partition Learning for Scale-Invariant UAV-View Geo-Localization
Specifically, we propose a distance guided dynamic partition learning strategy~(DGDPL), consisting of a square partition strategy and a distance-guided adjustment strategy.
CLIP-SR: Collaborative Linguistic and Image Processing for Super-Resolution
Our proposed multi-modal collaborative framework enables the production of realistic and high-quality SR images at significant up-scaling factors.
Towards Rich Emotions in 3D Avatars: A Text-to-3D Avatar Generation Benchmark
T3DEM is the most crucial step in determining the quality of Emo3D generation and encompasses three key challenges: Expression Diversity, Emotion-Content Consistency, and Expression Fluidity.
RIGI: Rectifying Image-to-3D Generation Inconsistency via Uncertainty-aware Learning
Given a single image of a target object, image-to-3D generation aims to reconstruct its texture and geometric shape.
Beyond Walking: A Large-Scale Image-Text Benchmark for Text-based Person Anomaly Search
To enable the training and evaluation of this new task, we construct a large-scale image-text Pedestrian Anomaly Behavior (PAB) benchmark, featuring a broad spectrum of actions, e. g., running, performing, playing soccer, and the corresponding anomalies, e. g., lying, being hit, and falling of the same identity.
Video2BEV: Transforming Drone Videos to BEVs for Video-based Geo-localization
To address these limitations, we formulate a new video-based drone geo-localization task and propose the Video2BEV paradigm.
VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimation
Given the higher information load processed by large vision-language models (LVLMs) compared to single-modal LLMs, detecting LVLM hallucinations requires more human and time expense, and thus rise a wider safety concerns.
Ctrl-U: Robust Conditional Image Generation via Uncertainty-aware Reward Modeling
Based on the uncertainty estimation, we regularize the model training by adaptively rectifying the reward.
Harnessing Uncertainty-aware Bounding Boxes for Unsupervised 3D Object Detection
(2) Based on the assessed uncertainty, we adaptively adjust the weight of every 3D bbox coordinate via uncertainty regularization, refining the training process on pseudo bboxes.
PiPa++: Towards Unification of Domain Adaptive Semantic Segmentation via Self-supervised Learning
Unsupervised domain adaptive segmentation aims to improve the segmentation accuracy of models on target domains without relying on labeled data from those domains.
Approaching Outside: Scaling Unsupervised 3D Object Detection from 2D Scene
In this paper, we are among the early attempts to integrate LiDAR data with 2D images for unsupervised 3D detection and introduce a new method, dubbed LiDAR-2D Self-paced Learning (LiSe).
From Data Deluge to Data Curation: A Filtering-WoRA Paradigm for Efficient Text-based Person Search
In text-based person search endeavors, data generation has emerged as a prevailing practice, addressing concerns over privacy preservation and the arduous task of manual annotation.
Ranked #2 on
Text based Person Retrieval
on ICFG-PEDES
Instilling Multi-round Thinking to Text-guided Image Generation
This paper delves into the text-guided image editing task, focusing on modifying a reference image according to user-specified textual feedback to embody specific attributes.
Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matching
Navigating drones through natural language commands remains challenging due to the dearth of accessible multi-modal datasets and the stringent precision requirements for aligning visual and textual data.
Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation
Based on such observation, we propose a depth-aware framework to explicitly leverage depth estimation to mix the categories and facilitate the two complementary tasks, i. e., segmentation and depth learning in an end-to-end manner.
Progressive Text-to-3D Generation for Automatic 3D Prototyping
We aspire for our work to pave the way for automatic 3D prototyping via natural language descriptions.
Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark
To verify the feasibility of learning from the generated data, we develop a new joint Attribute Prompt Learning and Text Matching Learning (APTM) framework, considering the shared knowledge between attribute and text.
Ranked #1 on
Text based Person Retrieval
on ICFG-PEDES
(using extra training data)
Collaborative Group: Composed Image Retrieval via Consensus Learning from Noisy Annotations
Triplet ambiguity refers to a type of semantic ambiguity that arises between the reference image, the relative caption, and the target image.
Content-Based Image Retrieval
Image Retrieval with Multi-Modal Query
+2
Actively Discovering New Slots for Task-oriented Conversation
Existing task-oriented conversational search systems heavily rely on domain ontologies with pre-defined slots and candidate value sets.
Learnable Pillar-based Re-ranking for Image-Text Retrieval
Image-text retrieval aims to bridge the modality gap and retrieve cross-modal content based on semantic similarities.
Are Binary Annotations Sufficient? Video Moment Retrieval via Hierarchical Uncertainty-Based Active Learning
Moreover, we treat the uncertainty score of frames in a video as a whole, and estimate the difficulty of each video, which can further relieve the burden of video selection.
Context-Aware Pretraining for Efficient Blind Image Decomposition
(2) The conventional paradigm usually focuses on mining the abnormal pattern of a superimposed image to separate the noise, which de facto conflicts with the primary image restoration task.
StepNet: Spatial-temporal Part-aware Network for Isolated Sign Language Recognition
As its name suggests, it is made up of two modules: Part-level Spatial Modeling and Part-level Temporal Modeling.
Ranked #1 on
Sign Language Recognition
on BOBSL
Cognitive Accident Prediction in Driving Scenes: A Multimodality Benchmark
In particular, the text description provides a dense semantic description guidance for the primary context of the traffic scene, while the driver attention provides a traction to focus on the critical region closely correlating with safe driving.
PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation
In an attempt to fill this gap, we propose a unified pixel- and patch-wise self-supervised learning framework, called PiPa, for domain adaptive semantic segmentation that facilitates intra-image pixel-wise correlations and patch-wise semantic consistency against different contexts.
Ranked #1 on
Semantic Segmentation
on SYNTHIA-to-Cityscapes
Composed Image Retrieval with Text Feedback via Multi-grained Uncertainty Regularization
The key idea underpinning the proposed method is to integrate fine- and coarse-grained retrieval as matching data points with small and large fluctuations, respectively.
Composed Image Retrieval (CoIR)
Image Retrieval with Multi-Modal Query
+1
Learning Cross-view Geo-localization Embeddings via Dynamic Weighted Decorrelation Regularization
Cross-view geo-localization aims to spot images of the same location shot from two platforms, e. g., the drone platform and the satellite platform.
DMRNet++: Learning Discriminative Features with Decoupled Networks and Enriched Pairs for One-Step Person Search
Person search aims at localizing and recognizing query persons from raw video frames, which is a combination of two sub-tasks, i. e., pedestrian detection and person re-identification.
Ranked #4 on
Person Search
on PRW
Jointly Harnessing Prior Structures and Temporal Consistency for Sign Language Video Generation
We optimize the two losses and keypoint detector network in an end-to-end manner.
Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization
Inspired by the human visual system for mining local patterns, we propose a new framework called RK-Net to jointly learn the discriminative Representation and detect salient Keypoints with a single Network.
Ranked #2 on
Drone navigation
on University-1652
3D Magic Mirror: Clothing Reconstruction from a Single Image via a Causal Perspective
This research aims to study a self-supervised 3D clothing reconstruction method, which recovers the geometry shape and texture of human clothing from a single image.
Ranked #1 on
Single-View 3D Reconstruction
on CUB-200-2011
Multiple-environment Self-adaptive Network for Aerial-view Geo-localization
This task is mostly regarded as an image retrieval problem.
Multi-View Consistent Generative Adversarial Networks for 3D-aware Image Synthesis
To address this challenge, we propose Multi-View Consistent Generative Adversarial Networks (MVCGAN) for high-quality 3D-aware image synthesis with geometry constraints.
Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes
Albeit simple, the pre-trained encoder can capture the key points of an unseen point cloud and surpasses the encoder trained from scratch on downstream tasks.
Ranked #46 on
3D Part Segmentation
on ShapeNet-Part
SPG-VTON: Semantic Prediction Guidance for Multi-pose Virtual Try-on
Third, a Try-on Synthesis Module (TSM) combines the coarse result and the warped clothes to generate the final virtual try-on image, preserving details of the desired clothes and under the desired pose.
Less is More: Sparse Sampling for Dense Reaction Predictions
Obtaining viewer responses from videos can be useful for creators and streaming platforms to analyze the video performance and improve the future user experience.
Connecting Language and Vision for Natural Language-Based Vehicle Retrieval
In this paper, we apply one new modality, i. e., the language description, to search the vehicle of interest and explore the potential of this task in the real-world scenario.
Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation
Domain adaptation is to transfer the shared knowledge learned from the source domain to a new environment, i. e., target domain.
Decoupled and Memory-Reinforced Networks: Towards Effective Feature Learning for One-Step Person Search
Specifically, to reconcile the conflicts of multiple objectives, we simplify the standard tightly coupled pipelines and establish a deeply decoupled multi-task learning framework.
Ranked #9 on
Person Search
on PRW
Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective
We argue that the first phase equals building the k-nearest neighbor graph, while the second phase can be viewed as spreading the message within the graph.
Ranked #1 on
Image Retrieval
on Oxford5k
Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization
Existing methods usually concentrate on mining the fine-grained feature of the geographic target in the image center, but underestimate the contextual information in neighbor areas.
Ranked #3 on
Drone navigation
on University-1652
Parameter-Efficient Person Re-identification in the 3D Space
To our knowledge, we are among the first attempts to conduct person re-identification in the 3D space.
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
Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation
This paper focuses on the unsupervised domain adaptation of transferring the knowledge from the source domain to the target domain in the context of semantic segmentation.
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
Progressive Local Filter Pruning for Image Retrieval Acceleration
To verify this, we evaluate our method on two widely-used image retrieval datasets, i. e., Oxford5k and Paris6K, and one person re-identification dataset, i. e., Market-1501.
Unsupervised Scene Adaptation with Memory Regularization in vivo
We consider the unsupervised scene adaptation problem of learning from both labeled source data and unlabeled target data.
Ranked #1 on
Domain Adaptation
on SYNTHIA-to-Cityscapes Labels
Unsupervised Eyeglasses Removal in the Wild
Given two facial images with and without eyeglasses, the proposed model learns to swap the eye area in two faces.
Joint Discriminative and Generative Learning for Person Re-identification
To this end, we propose a joint learning framework that couples re-id learning and data generation end-to-end.
Ranked #1 on
Person Re-Identification
on UAV-Human
Image-to-Image Translation
Unsupervised Domain Adaptation
+1
Query Attack via Opposite-Direction Feature:Towards Robust Image Retrieval
Opposite-Direction Feature Attack (ODFA) effectively exploits feature-level adversarial gradients and takes advantage of feature distance in the representation space.
Macro-Micro Adversarial Network for Human Parsing
To address the two kinds of inconsistencies, this paper proposes the Macro-Micro Adversarial Net (MMAN).
Ranked #12 on
Semantic Segmentation
on LIP val
Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification
This paper considers the task of thorax disease classification on chest X-ray images.
Multi-pseudo Regularized Label for Generated Data in Person Re-Identification
Unlike the traditional label which usually is a single integral number, the virtual label proposed in this work is a set of weight-based values each individual of which is a number in (0, 1] called multi-pseudo label and reflects the degree of relation between each generated data to every pre-defined class of real data.
Camera Style Adaptation for Person Re-identification
In this paper, we explicitly consider this challenge by introducing camera style (CamStyle) adaptation.
Ranked #75 on
Person Re-Identification
on DukeMTMC-reID
Dual-Path Convolutional Image-Text Embeddings with Instance Loss
In this paper, we propose a new system to discriminatively embed the image and text to a shared visual-textual space.
Ranked #1 on
Cross-Modal Retrieval
on CUHK-PEDES
Pedestrian Alignment Network for Large-scale Person Re-identification
This task aims to search a query person in a large image pool.
Ranked #1 on
Person Re-Identification
on CUHK03 (detected)
Improving Person Re-identification by Attribute and Identity Learning
Person re-identification (re-ID) and attribute recognition share a common target at learning pedestrian descriptions.
Ranked #79 on
Person Re-Identification
on DukeMTMC-reID
Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
We verify the proposed method on a practical problem: person re-identification (re-ID).
Ranked #4 on
Person Re-Identification
on CUHK03
Fine-Grained Image Classification
Generative Adversarial Network
+2
A Discriminatively Learned CNN Embedding for Person Re-identification
We revisit two popular convolutional neural networks (CNN) in person re-identification (re-ID), i. e, verification and classification models.
Ranked #1 on
Person Re-Identification
on Market-1501+500k
