π― What it does: Propose a two-phase sampling framework named OMG, specifically designed to address identity preservation, occlusion conflicts, and foreground-background lighting inconsistency issues in multi-concept personalized generation;
π― What it does: Propose the Omni-Recon framework, constructing a general NeRF model to achieve real-time 3D reconstruction, zero-shot field multi-task scene understanding, and rapid adaptation to downstream applications such as real-time rendering and text-guided editing.
CodeClassificationRepresentation LearningConvolutional Neural NetworkTransformerAuto EncoderContrastive LearningMultimodalityTime SeriesBenchmarkAgriculture Related
π― What it does: Propose OmniSat, a self-supervised multimodal fusion framework designed to integrate representations from different Earth observation sensors (VHR aerial imagery, optical time series, radar time series), and enhance downstream task performance through self-supervised pre-training in multimodal settings.
OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model
Runyi Li (Peking University), Jian Zhang (Peking University)
CodeSuper ResolutionDiffusion modelImage
π― What it does: This paper proposes a zero-shot panoramic image super-resolution method called OmniSSR, which utilizes Stable Diffusion for super-resolution and enhances image quality through iterative ERP and TP projection interaction with gradient decomposition correction.
CodeTransformerSupervised Fine-TuningVision Language ModelContrastive LearningMultimodality
π― What it does: Propose a new framework, Omniview-Tuning (OVT), which significantly enhances the model's robustness under three-dimensional viewpoint shifts by constructing the MVCap dataset with over four million multi-viewεΎζ pairs and performing parameter-efficient viewpoint-invariant fine-tuning on the VLP model using this dataset.
π― What it does: Under a fixed computational budget, the study systematically evaluates and compares the impact of pre-training data with different scales and distributions on the performance of self-supervised learning (SSL).
π― What it does: This paper conducts an in-depth analysis of the approximate risk of few-shot incremental learning (FSCIL) from the perspective of statistical learning theory, and proposes a series of operable training and design guidelines based on this analysis;
π― What it does: Can monocular depth estimation be used as unsupervised pre-training, followed by transferring the model to semantic segmentation tasks, while systematically evaluating the impact of different pre-training methods, data scale, network architecture, and other factors on segmentation performance?
π― What it does: This paper proposes a framework for on-the-fly category discovery (OCDSS) in LiDAR semantic segmentation, which can real-time identify and segment both known and unknown categories during testing, using only known categories for training.
π― What it does: Propose a One-Shot Diffusion Mimicker (One-DM) model that can mimic the handwriting style of any writer and generate corresponding handwritten text images using only a single sample.
π― What it does: Propose a general image restoration framework based on Transformer called OneRestore, which can simultaneously handle multiple composite degradations such as low light, fog, rain, and snow.
π― What it does: Propose OneVOS, a full-process video object segmentation framework based on Vision Transformer, which unifies traditional phased modules such as feature extraction, matching, memory management, and multi-object aggregation into a single end-to-end trainable model.
π― What it does: This paper proposes the Online Continuous General Category Discovery (OCGCD) scenario and introduces the DEAN method, which achieves online unknown category detection, pseudo-label generation, and continual learning through energy-guided discovery and variance feature enhancement.
Online Vectorized HD Map Construction using Geometry
Zhixin Zhang (Beijing Institute of Technology), Xiangyu Yue (Chinese University of Hong Kong)
CodeAutonomous DrivingTransformerImage
π― What it does: Proposes an end-to-end online vectorization framework for high-definition map construction called GeMap, which learns the Euclidean shape and relationships of map instances through geometric representation.
π― What it does: Proposed a self-supervised OP-Align method that achieves category-level articulated object pose estimation using single-frame point clouds, and realizes object-level and part-level alignment with canonical reconstruction.
Open Vocabulary 3D Scene Understanding via Geometry Guided Self-Distillation
Pengfei Wang (Hong Kong Polytechnic University), Lei Zhang (Hong Kong Polytechnic University)
CodeSegmentationKnowledge DistillationConvolutional Neural NetworkTransformerVision Language ModelContrastive LearningPoint Cloud
π― What it does: Propose Geometry Guided Self-Distillation (GGSD), which leverages geometric priors to guide knowledge distillation from 2D pre-trained models and performs self-distillation within 3D networks to achieve open-vocabulary 3D scene understanding.
π― What it does: Proposes a multi-view 3D object detection method called OPEN, which improves detection by leveraging object-level depth information.
OpenPSG: Open-set Panoptic Scene Graph Generation via Large Multimodal Models
Zijian Zhou (Tongji University), Miaojing Shi (Delft University of Technology)
CodeSegmentationGenerationTransformerLarge Language ModelVision Language ModelMultimodality
π― What it does: Proposes an open panoramic scene graph generation (OpenPSG) based on large multimodal models, which can simultaneously perform open object segmentation and relation prediction in images.
OpenSight: A Simple Open-Vocabulary Framework for LiDAR-Based Object Detection
Hu Zhang (CSIRO DATA61), Kaicheng Yu (Westlake University)
CodeObject DetectionAutonomous DrivingTransformerLarge Language ModelVision Language ModelImageTextPoint Cloud
π― What it does: Designed an open-vocabulary LiDAR object detection framework called OpenSight, which can first identify general objects and then assign specific categories without requiring labeled annotations.
π― What it does: Proposed an end-to-end evaluation framework for large-scale open set recognition, and introduced a novel post-processing algorithm called PostMax within this framework to enhance the open set recognition performance of pre-trained closed-set classifiers.
OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation
Kwanyoung Kim (Korea Advanced Institute of Science and Technology), Jong Chul Ye (Korea Advanced Institute of Science and Technology)
CodeSegmentationTransformerVision Language ModelImageTextMultimodality
π― What it does: This paper proposes the OTSeg framework, which achieves zero-shot semantic segmentation by utilizing multi-prompt Sinkhorn attention.
π― What it does: Constructed and released the first database targeting physical domain attacks on remote photoplethysmography (rPPG) (ORPDAD), evaluating the robustness of various handcrafted features and deep learning methods against three types of attacks (illumination, motion, occlusion).
π― What it does: Propose a unified open-vocabulary 3D object detection framework, OV-Uni3DETR, which can perform 3D detection under any sensor modality (point cloud, RGB image) and supports multiple indoor and outdoor scenarios;
Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selection
Yunfeng FAN, Song Guo (Hong Kong University of Science and Technology)
CodeFederated LearningMultimodality
π― What it does: Propose a balanced modal selection framework, BMSFed, which utilizes global prototypes to enhance weak modalities and dynamically selects multi-modal and single-modal clients through sub-mode sub-functions, thereby overcoming modal bias in multi-modal federated learning.
Overcoming Distribution Mismatch in Quantizing Image Super-Resolution Networks
Cheeun Hong (Seoul National University), Kyoung Mu Lee (Seoul National University)
CodeSuper ResolutionImage
π― What it does: Propose a novel quantization-aware training framework ODM to address the distribution mismatch problem in SR networks without requiring dynamic quantization ranges.
π― What it does: This paper investigates the 'silent weights' problem during the weight sign update process in binary neural networks, and proposes the OvSW method to address this issue.
Yang You (Stanford University), Cewu Lu (University Of California San Diego)
CodePose EstimationSimultaneous Localization and MappingOptical FlowImageVideoBenchmark
π― What it does: This paper proposes a large-scale real and simulated pose annotation dataset called PACE, and analyzes the performance of existing 6D object pose estimation methods in complex occlusion and movable object scenarios through an evaluation benchmark system.
π― What it does: For unsupervised real-world image super-resolution (RWSR), the authors propose a framework based on pairwise distance distillation (PDD), which leverages existing specialized models (focused on synthetic denoising) and general-purpose models (targeting diverse denoising scenarios). By maintaining consistency in feature distances both within and across models on unannotated real low-resolution images, the framework further enhances the super-resolution quality of specialized models on real images.
π― What it does: Propose a UDC image restoration framework based on panel-specific degradation representation embedded in Transformer, and collect raw UDC datasets from two commercial phone panels (ZTE Axon 30 5G, Samsung Galaxy Z-Fold 3).
PanGu-Draw: Advancing Resource-Efficient Text-to-Image Synthesis with Time-Decoupled Training and Reusable Coop-Diffusion
Guansong Lu (Huawei Noah's Ark Lab), Hang Xu (Huawei Noah's Ark Lab)
CodeGenerationData SynthesisTransformerVision Language ModelDiffusion modelAuto EncoderImageTextMultimodality
π― What it does: Propose PanGu-Draw, a resource-efficient text-to-image generation model based on a time-decoupled training strategy and the Coop-Diffusion algorithm.
PanoVOS: Bridging Non-panoramic and Panoramic Views with Transformer for Video Segmentation
Shilin Yan (Fudan University), Wei Zhang (Fudan University)
CodeSegmentationTransformerVideo
π― What it does: This paper proposes the PanoVOS task for panoramic video object segmentation, constructs a dataset of 150 high-resolution panoramic videos, and designs a Transformer network called PSCFormer aimed at panoramic spatial consistency.
π― What it does: Propose a lightweight convolutional network (PaPr) scheme for accelerating inference of vision Transformers, convolutional networks, and hybrid Transformers, which is training-agnostic and capable of one-time pruning of image/video patches;
π― What it does: Designed a lightweight point cloud registration network called PARE-Net based on position-aware rotation equivariant convolution, which can learn rotation equivariant features and achieve efficient and robust registration without relying on rotation data augmentation.
π― What it does: Constructed the PartImageNet++ (PIN++) dataset and proposed a multi-scale part-supervised model (MPM) based on this dataset to enhance adversarial robustness in image classification and robustness to common noise and out-of-distribution (OOD) samples.
π― What it does: Propose the PartSTAD method, leveraging GLIP's 2D detection and SAM's masks, using a lightweight MLP to predict boundary weights, achieving 3D part segmentation with few samples;
π― What it does: Proposes an unsupervised part discovery framework called PDiscoFormer based on Vision Transformer, which utilizes total variation prior to relax constraints on part size and shape, achieving automatic localization of semantic parts in fine-grained images.
PDT Uav Target Detection Dataset for Pests and Diseases Tree
Mingle Zhou (Qilu University of Technology (Shandong Academy of Sciences)), Gang Li (Qilu University of Technology (Shandong Academy of Sciences))
CodeObject DetectionConvolutional Neural NetworkImageAgriculture Related
π― What it does: Constructed a high-resolution and low-resolution image dataset for UAVs (PDT) and integrated public data to generate a multi-class crop and weed dataset (CWC). Subsequently, a specialized YOLO-DP model for dense small target detection was designed based on YOLOv5s; compared the performance of existing detection models on three datasets.
Personalized Privacy Protection Mask Against Unauthorized Facial Recognition
Ka-Ho Chow (University of Hong Kong), Ling Liu (Georgia Institute of Technology)
CodeRecognitionSafty and PrivacyImage
π― What it does: Propose a personalized privacy protection mask called P3-Mask for each user to instantly protect user facial photos from unauthorized face recognition without requiring manual per-image optimization.
π― What it does: This paper proposes a personalized video relighting algorithm that utilizes a user's home computer monitor as a light source, capable of performing high-quality and temporally consistent relighting on videos with arbitrary poses, expressions, and environmental lighting conditions in real-time (β45fps).
π― What it does: Propose the PFedEdit framework, which automatically selects personalized layers through model editing to enhance model performance in non-i.i.d. environments within federated learning.
π― What it does: Integrate sparse point cloud rendering with 3D Gaussian Splatting to propose the PFGS framework, achieving high-fidelity, 3D-consistent image rendering.
Phase Concentration and Shortcut Suppression for Weakly Supervised Semantic Segmentation
Hoyong Kwon (Korea Advanced Institute of Science and Technology), Kuk-Jin Yoon (Korea Advanced Institute of Science and Technology)
CodeSegmentationTransformerImageBenchmark
π― What it does: Proposes two modules based on the frequency domain: Magnitude-mixing-based Phase Concentration (MPC) and Frequency Shortcut Suppression (FSS), to enhance the Class Activation Map (CAM) quality of Vision Transformers (ViT) in weakly supervised semantic segmentation, reducing boundary blurriness and erroneous activation.
Haiqian Han (Tsinghua University), Xiangyang Ji (Xiaomi Mobile Software Co., Ltd)
CodeData SynthesisVideoPhysics Related
π― What it does: This paper proposes a physics-based event camera simulator (PECS) that can directly generate high-fidelity event streams from 3D scenes;
π― What it does: Proposes a novel edge federated continual learning framework called Pick-a-back, which enables selective and customized knowledge transfer between devices, helping local models learn faster and more generalizable when facing private heterogeneous tasks.
π― What it does: Proposes a neural implicit surface method called PISR for 3D reconstruction of textureless and specular objects using polarized images.
π― What it does: Propose the Pixel-Aware Stable Diffusion (PASD) network, which leverages pre-trained Stable Diffusion and integrates pixel-level cross-attention, denoising modules, adjustable noise scheduling, etc., to achieve real image super-resolution and personalized style transfer.
π― What it does: Proposed a PixOOD framework based on pixel-level unsupervised anomaly detection to identify anomalies not present in the training data.
Placing Objects in Context via Inpainting for Out-of-distribution Segmentation
Pau de Jorge Aranda, Gregory Rogez
CodeSegmentationDomain AdaptationAnomaly DetectionVision Language ModelDiffusion modelImage
π― What it does: Proposes the Placing Objects in Context (POC) process, which uses diffusion models to realistically insert arbitrary objects into images, generating a high-quality out-of-distribution (OOD) segmentation dataset.
π― What it does: Proposed and implemented the unified architecture of the Platypus model for reading text from images in various forms, including natural scenes, documents, handwritten text, and formulas.
π― What it does: Proposed a Plug-and-Play Domain Adapter (PnPDA), achieving non-destructive alignment of intermediate features between heterogeneous deep learning models, and enabling new models to seamlessly integrate into collaborative perception networks through two-step semantic transformation.
π― What it does: Proposed a Progressive Mean Teacher (PMT) framework that alternately trains two isomorphic Mean Teacher models at different training iteration stages to continuously generate diverse, high-quality pseudo labels, thereby enhancing semi-supervised learning for medical image segmentation.
π― What it does: A single pre-training process that utilizes an elastic student branch to simultaneously generate subnetworks of different scales, including ViT, Swin Transformer, and ResNet, for direct use in downstream tasks.
POET: Prompt Offset Tuning for Continual Human Action Adaptation
Prachi Garg, Fernando de la Torre
CodeRecognitionSafty and PrivacyMeta LearningGraph Neural NetworkPrompt EngineeringAuto EncoderGraph
π― What it does: Propose the POET method, utilizing Prompt Offset Tuning to achieve privacy-friendly, few-shot continual learning in skeletal action recognition models;
PointLLM: Empowering Large Language Models to Understand Point Clouds
Runsen Xu (Chinese University of Hong Kong), Dahua Lin (Chinese University of Hong Kong)
CodeGenerationTransformerLarge Language ModelSupervised Fine-TuningVision Language ModelTextMultimodalityPoint Cloud
π― What it does: Studied a multi-modal model called PointLLM that integrates large language models with point clouds, enabling it to understand and generate descriptions of colored point cloud objects based on human instructions.
π― What it does: Developed a deep generation and correction framework based on diffusion models (PointRegGPT), which can automatically generate realistic overlapping point cloud pairs from a single depth image for training 3D point cloud registration models.
Power Variable Projection for Initialization-Free Large-Scale Bundle Adjustment
Simon Weber (Technical University of Munich), Daniel Cremers (Technical University of Munich)
CodePose EstimationOptimizationSimultaneous Localization and MappingImageBenchmark
π― What it does: Proposed the Power Variable Projection (PoVar) method, achieving initialization-agnostic large-scale bundle adjustment, and combined with Riemannian PoBA for projective improvements.
Powerful and Flexible: Personalized Text-to-Image Generation via Reinforcement Learning
Fanyue Wei (University of Electronic Science and Technology of China), Wen Li (University of Electronic Science and Technology of China)
CodeGenerationReinforcement LearningVision Language ModelDiffusion modelImageTextBenchmark
π― What it does: Propose a method that utilizes the deterministic policy gradient (DPG) reinforcement learning framework to fine-tune the Stable Diffusion model for personalized text-to-image generation.
π― What it does: Propose an end-to-end autonomous driving framework called PPAD, which simulates dynamic interactions among traffic participants by alternately performing motion prediction (Prediction) and motion planning (Planning) at each time step.
π― What it does: PreLAR pre-trains an action-conditioned world model by learning learnable implicit action representations from unlabeled videos and fine-tunes it on downstream visual control tasks.
π― What it does: Construct a static prior using city-scale NeRF from historical driving data to enhance the robustness and accuracy of online perception models.
PRET: Planning with Directed Fidelity Trajectory for Vision and Language Navigation
Renjie Lu (Sun Yat-sen University), WEI-SHI ZHENG
CodeOptimizationTransformerVision Language ModelVision-Language-Action ModelContrastive LearningMultimodality
π― What it does: This paper proposes a new vision-language navigation method called PRET, which uses directed graphs and oriented faithful trajectories for global planning. It directly evaluates the alignment between instructions and different trajectories to determine the next navigation target.
π― What it does: Propose a memory network-based Deformable-DETR (MD-DETR) for continuous object detection, fine-tuning new tasks while retaining prior task knowledge.
π― What it does: Proposes the FGSM-PCO algorithm, which effectively prevents and corrects catastrophic overfitting by adaptively fusing historical and current adversarial examples in the fast adversarial training (FAT) process, combined with a novel regularization loss.
Prioritized Semantic Learning for Zero-shot Instance Navigation
xinyu sun, Junwei Liang (Hong Kong University of Science and Technology)
CodeRobotic IntelligenceVision Language ModelImageTextMultimodality
π― What it does: This paper proposes a Priority Semantic Learning (PSL) framework for zero-shot instance navigation, addressing the issue of neglecting semantic information in traditional ImageNav pre-training tasks.
CodeRetrievalDomain AdaptationFederated LearningSafty and PrivacyTransformerContrastive LearningImage
π― What it does: Proposes a federated learning framework named Fed-Protoid for distributed unsupervised domain adaptation (DUDA-Rid) in person re-identification without transferring images.
Probabilistic Weather Forecasting with Deterministic Guidance-based Diffusion Model
Donggeun Yoon (Korea Electronics Technology Institute), Donghyeon Cho (Hanyang University)
CodeDiffusion modelImageTime SeriesBenchmarkPhysics Related
π― What it does: Proposes the Deterministic Guidance-based Diffusion Model (DGDM), combining a non-autoregressive deterministic prediction branch with a probabilistic branch of Brownian Bridge diffusion to achieve high-accuracy and diverse weather forecasting.
π― What it does: Proposed a progressive adaptation framework named PCFEA that deeply couples the classifier and feature extractor for unsupervised point cloud domain adaptation.
π― What it does: Propose a three-stage Progressive Pretext Task Learning (PPT) framework. First, short-term motion patterns are learned by progressively predicting the next position (Task-I). Then, long-term dependencies are learned through goal prediction (Task-II). Finally, a complete future trajectory is predicted by combining both (Task-III), with cross-task knowledge distillation used to prevent forgetting.
π― What it does: This paper proposes a Progressive Proxy Anchor Propagation (PPAP) strategy to improve positive and negative sample selection in contrastive learning by progressively migrating proxy anchors to semantically similar sample dense regions and defining fuzzy areas in unsupervised semantic segmentation.
π― What it does: Propose a no-training test-time defogging pipeline called PTTD, which narrows the domain gap between synthetic and real fog images by fine-tuning the pre-trained model's encoder feature statistics (mean, variance);
CodeKnowledge DistillationTransformerPrompt EngineeringVision Language ModelImage
π― What it does: Propose the PromptFusion method, which separates the stability and plasticity issues in continual learning. Stability is addressed by the Stabilizer module (based on CoOp text prompts), while plasticity is handled by the Booster module (based on VPT visual prompts). Further, PromptFusion-Lite is introduced, dynamically selecting whether to activate the Booster at the input level to reduce computational overhead.
Prompting Language-Informed Distribution for Compositional Zero-Shot Learning
Wentao Bao (Michigan State University), Yu Kong (Michigan State University)
CodeClassificationRepresentation LearningTransformerLarge Language ModelPrompt EngineeringVision Language ModelContrastive LearningImageText
π― What it does: Propose the PLID method, which leverages a large language model to generate diverse and information-rich sentence descriptions, constructs a language information distribution, prompts CLIP, and combines visual-lingual primitive decomposition with logarithmic hybrid fusion to achieve compositional zero-shot learning.
ProSub: Probabilistic Open-Set Semi-Supervised Learning with Subspace-Based Out-of-Distribution Detection
Erik Wallin (Saab AB), Lars Hammarstrand (Chalmers University of Technology)
CodeAnomaly DetectionImage
π― What it does: Propose the ProSub framework in open semi-supervised learning, achieving probabilistic discrimination of ID/OOD by calculating the angle scores between sample features and the ID subspace and estimating their beta distribution.
Protecting NeRFs' Copyright via Plug-And-Play Watermarking Base Model
Qi Song (Hong Kong Baptist University), Renjie Wan (Hong Kong Baptist University)
CodeSafty and PrivacyNeural Radiance FieldImage
π― What it does: This paper proposes a plug-and-play NeRF copyright protection method called NeRFProtector, which directly embeds binary watermarks during the NeRF generation process, avoiding post-training tuning windows and reducing the risk of malicious tampering;
π― What it does: This paper proposes a point cloud completion method called ProtoComp based on controllable prototypes, which completes partial point clouds by first generating a rough semantic prototype and then refining geometric details.
ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation
Mengcheng Lan (Nanyang Technological University), Wayne Zhang (SenseTime Research)
CodeSegmentationPrompt EngineeringVision Language ModelImageText
π― What it does: Studies how to improve CLIP for open-vocabulary semantic segmentation by leveraging the spatial consistency characteristics of vision foundation models (VFM) to enhance segmentation accuracy.
PSALM: Pixelwise Segmentation with Large Multi-modal Model
Zheng Zhang (Huazhong University of Science and Technology), Xiang Bai (Huazhong University of Science and Technology)
CodeSegmentationTransformerLarge Language ModelPrompt EngineeringVision Language ModelImageVideoMultimodality
π― What it does: PSALM achieves unified multiple pixel-level segmentation tasks within a single model by externally adding a mask decoder to a large multimodal model (LMM) and designing a four-input scheme (image, task instruction, conditional prompt, and mask token).
Pseudo-Embedding for Generalized Few-Shot Point Cloud Segmentation
Chih-Jung Tsai (National Tsing Hua University), Tyng-Luh Liu (Academia Sinica)
CodeSegmentationMeta LearningGraph Neural NetworkTransformerVision Language ModelPoint Cloud
π― What it does: Propose a general few-shot 3D segmentation framework that utilizes background context for pseudo embeddings, capable of simultaneously identifying base classes and novel classes.
Pseudo-RIS: Distinctive Pseudo-supervision Generation for Referring Image Segmentation
Seonghoon Yu (GIST), Jeany Son (GIST)
CodeSegmentationData SynthesisTransformerPrompt EngineeringVision Language ModelContrastive LearningMultimodality
π― What it does: Propose a pseudo-supervised generation framework without manual annotation, automatically generating high-quality masks using a segmentation model and producing discriminative representative expressions corresponding to the masks via a text generation model, thereby providing scalable training data for RIS models.
π― What it does: Proposes the PYRA method, which merges tokens in Vision Transformers through parallel generation of modulation weights and reactivation mechanisms, balancing training and inference efficiency.
π― What it does: Studied a pyramid discrete diffusion model (PDD) that generates high-quality 3D large-scale scenes through a coarse-to-fine approach.
π― What it does: Proposed an 8-bit integer quantization Winograd convolution transformation scheme, first using particle swarm optimization to find a quantization-friendly transformation matrix, then fine-tuning it as a learnable parameter during training to significantly reduce quantization error.
π― What it does: Propose a query-based controllable fish-eye image distortion correction network called QueryCDR, which can handle fish-eye images with different distortion levels without retraining.
R.A.C.E.: Robust Adversarial Concept Erasure for Secure Text-to-Image Diffusion Model
Changhoon Kim (Arizona State University), Yezhou Yang (Arizona State University)
CodeGenerationSafty and PrivacyAdversarial AttackConvolutional Neural NetworkVision Language ModelDiffusion modelImageText
π― What it does: RACE enhances the robustness of text-to-image diffusion models against concept elimination through adversarial training on text prompts, preventing the reconstruction of sensitive content.
π― What it does: Propose an anomaly segmentation method called RWPM based on pixel manifold random walks, which reshapes pixel embeddings through random walks to enhance segmentation accuracy.
π― What it does: This paper proposes a LiDAR point cloud generation method called RangeLDM based on latent diffusion models, which can quickly generate high-quality 3D point clouds and support point cloud upsampling and filling.
π― What it does: This paper proposes the RAPiD (Range-Aware Pointwise Distance Distribution) feature and the RAPiD-Seg network for 3D LiDAR point cloud semantic segmentation.
RAVE: Residual Vector Embedding for CLIP-Guided Backlit Image Enhancement
Tatiana Gaintseva (Queen Mary University of London), Gregory Slabaugh (Queen Mary University of London)
CodeRestorationConvolutional Neural NetworkTransformerVision Language ModelContrastive LearningImage
π― What it does: This paper proposes two improved CLIP-guided methods for single-image backlit enhancement tasksβCLIP-LIT-Latent and RAVE, achieving higher quality image enhancement.
π― What it does: Propose Rawformer, a fully unsupervised Transformer-based raw-to-raw translation framework that can map raw images captured by one camera to the raw domain of another camera, enabling pre-trained neural ISPs to directly process raw images from new cameras without the need to re-collect paired data.
π― What it does: Propose the Ray Denoising method, which samples depth-aware hard negative samples along camera rays in multi-view 3D detection, helping the model distinguish real objects from pseudo targets generated by depth ambiguity.
π― What it does: Designed a novel Prompt training method called RCS-Prompt for Prompt-Based lifelong learning without example archives, aiming to reduce overlap between old and new class spaces by learning to rearrange the category space, thereby enhancing classification boundaries.
π― What it does: Proposed and made public the ReALFRED benchmark, which uses multi-room environments with real 3D scans combined with interactive objects and free-form natural language instructions, aiming to train robots to complete long-term daily household tasks.
π― What it does: Studies the attention mechanisms in real-time video super-resolution (RWVSR), exploring the sensitivity of spatial attention and channel attention on real-world degraded videos, and proposes an improved solution to address the feature redundancy caused by channel attention. Based on this research, designs RealViformer: a unidirectional recursive Transformer structure that employs channel attention fusion and improved channel attention (ICA), enhancing RWVSR performance without introducing a large number of parameters.