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Found 20 papers, 11 papers with code
Implicit Bias Injection Attacks against Text-to-Image Diffusion Models
The proliferation of text-to-image diffusion models (T2I DMs) has led to an increased presence of AI-generated images in daily life.
TarPro: Targeted Protection against Malicious Image Editing
The rapid advancement of image editing techniques has raised concerns about their misuse for generating Not-Safe-for-Work (NSFW) content.
Redistribute Ensemble Training for Mitigating Memorization in Diffusion Models
In this paper, we propose a novel method for diffusion models from the perspective of visual modality, which is more generic and fundamental for mitigating memorization.
Image Regeneration: Evaluating Text-to-Image Model via Generating Identical Image with Multimodal Large Language Models
We use GPT4V to bridge the gap between the reference image and the text input for the T2I model, allowing T2I models to understand image content.
Iterative Ensemble Training with Anti-Gradient Control for Mitigating Memorization in Diffusion Models
In this paper, we propose a novel training framework for diffusion models from the perspective of visual modality, which is more generic and fundamental for mitigating memorization.
Product-Level Try-on: Characteristics-preserving Try-on with Realistic Clothes Shading and Wrinkles
Image-based virtual try-on systems, which fit new garments onto human portraits, are gaining research attention. An ideal pipeline should preserve the static features of clothes(like textures and logos)while also generating dynamic elements(e. g. shadows, folds)that adapt to the model's pose and environment. Previous works fail specifically in generating dynamic features, as they preserve the warped in-shop clothes trivially with predicted an alpha mask by composition. To break the dilemma of over-preserving and textures losses, we propose a novel diffusion-based Product-level virtual try-on pipeline,\ie PLTON, which can preserve the fine details of logos and embroideries while producing realistic clothes shading and wrinkles. The main insights are in three folds:1)Adaptive Dynamic Rendering:We take a pre-trained diffusion model as a generative prior and tame it with image features, training a dynamic extractor from scratch to generate dynamic tokens that preserve high-fidelity semantic information.
Logic-induced Diagnostic Reasoning for Semi-supervised Semantic Segmentation
Recent advances in semi-supervised semantic segmentation have been heavily reliant on pseudo labeling to compensate for limited labeled data, disregarding the valuable relational knowledge among semantic concepts.
FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation
In this work, we propose FedSeg, a basic federated learning approach for class-heterogeneous semantic segmentation.
Distilling DETR with Visual-Linguistic Knowledge for Open-Vocabulary Object Detection
Current methods for open-vocabulary object detection (OVOD) rely on a pre-trained vision-language model (VLM) to acquire the recognition ability.
WINNER: Weakly-Supervised hIerarchical decompositioN and aligNment for Spatio-tEmporal Video gRounding
WINNER first builds the language decomposition tree in a bottom-up manner, upon which the structural attention mechanism and top-down feature backtracking jointly build a multi-modal decomposition tree, permitting a hierarchical understanding of unstructured videos.
GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models
Going beyond this, we propose GMMSeg, a new family of segmentation models that rely on a dense generative classifier for the joint distribution p(pixel feature, class).
Ranked #4 on
Out-of-Distribution Detection
on ADE-OoD
MHR-Net: Multiple-Hypothesis Reconstruction of Non-Rigid Shapes from 2D Views
We propose MHR-Net, a novel method for recovering Non-Rigid Shapes from Motion (NRSfM).
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).
Local-Global Context Aware Transformer for Language-Guided Video Segmentation
We explore the task of language-guided video segmentation (LVS).
Ranked #7 on
Referring Expression Segmentation
on A2D Sentences
Referring Expression Segmentation
Referring Video Object Segmentation
+5
End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding
To achieve effective grounding under a limited annotation budget, we investigate one-shot video grounding, and learn to ground natural language in all video frames with solely one frame labeled, in an end-to-end manner.
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.
H2FA R-CNN: Holistic and Hierarchical Feature Alignment for Cross-Domain Weakly Supervised Object Detection
How to align the source and target domains is critical to the CDWSOD accuracy.
Ranked #1 on
Weakly Supervised Object Detection
on Clipart1k
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.
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)
Pose-Guided Feature Alignment for Occluded Person Re-Identification
Our method largely outperforms existing person re-id methods on three occlusion datasets, while remains top performance on two holistic datasets.
Ranked #30 on
Person Re-Identification
on Occluded-DukeMTMC
