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Found 71 papers, 35 papers with code
Instance-Aware Embedding for Point Cloud Instance Segmentation
However, these methods are unaware of the instance context and fail to realize the boundary and geometric information of an instance, which are critical to separate adjacent objects.
DRepMRec: A Dual Representation Learning Framework for Multimodal Recommendation
After obtaining separate behavior and modal representations, we design a Behavior-Modal Alignment Module (BMA) to align and fuse the dual representations to solve the misalignment problem.
An Aligning and Training Framework for Multimodal Recommendations
In AlignRec, the recommendation objective is decomposed into three alignments, namely alignment within contents, alignment between content and categorical ID, and alignment between users and items.
Endora: Video Generation Models as Endoscopy Simulators
In a nutshell, Endora marks a notable breakthrough in the deployment of generative AI for clinical endoscopy research, setting a substantial stage for further advances in medical content generation.
Diffusion Model-Based Image Editing: A Survey
In this survey, we provide an exhaustive overview of existing methods using diffusion models for image editing, covering both theoretical and practical aspects in the field.
Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models
Large Language Models (LLMs) have demonstrated remarkable performance across diverse tasks and exhibited impressive reasoning abilities by applying zero-shot Chain-of-Thought (CoT) prompting.
EndoGaussian: Real-time Gaussian Splatting for Dynamic Endoscopic Scene Reconstruction
To adapt 3DGS for endoscopic scenes, we propose two strategies, Holistic Gaussian Initialization (HGI) and Spatio-temporal Gaussian Tracking (SGT), to handle the non-trivial Gaussian initialization and tissue deformation problems, respectively.
ICGNet: A Unified Approach for Instance-Centric Grasping
Executing a successful grasp in a cluttered environment requires multiple levels of scene understanding: First, the robot needs to analyze the geometric properties of individual objects to find feasible grasps.
Inter-X: Towards Versatile Human-Human Interaction Analysis
We also equip Inter-X with versatile annotations of more than 34K fine-grained human part-level textual descriptions, semantic interaction categories, interaction order, and the relationship and personality of the subjects.
Bag of Tricks: Semi-Supervised Cross-domain Crater Detection with Poor Data Quality
To obtain a better robust model with better cross-domain generalization in the presence of poor data quality, we propose the SCPQ model, in which we first propose a method for fusing shallow information using attention mechanism (FSIAM), which utilizes feature maps fused with deep convolved feature maps after fully extracting the global sensory field of shallow information via the attention mechanism module, which can fully fit the data to obtain a better sense of the domain in the presence of poor data, and thus better multiscale adaptability.
HumanRecon: Neural Reconstruction of Dynamic Human Using Geometric Cues and Physical Priors
These priors ensure the color rendered along rays to be robust to view direction and reduce the inherent ambiguities of density estimated along rays.
GPT4Motion: Scripting Physical Motions in Text-to-Video Generation via Blender-Oriented GPT Planning
To tackle these issues, we propose GPT4Motion, a training-free framework that leverages the planning capability of large language models such as GPT, the physical simulation strength of Blender, and the excellent image generation ability of text-to-image diffusion models to enhance the quality of video synthesis.
High-Fidelity 3D Head Avatars Reconstruction through Spatially-Varying Expression Conditioned Neural Radiance Field
One crucial aspect of 3D head avatar reconstruction lies in the details of facial expressions.
Seal2Real: Prompt Prior Learning on Diffusion Model for Unsupervised Document Seal Data Generation and Realisation
To address the lack of labelled datasets for these seal-related tasks, we propose Seal2Real, a generative method that generates a large amount of labelled document seal data, and construct a Seal-DB dataset containing 20K images with labels.
KV Inversion: KV Embeddings Learning for Text-Conditioned Real Image Action Editing
Text-conditioned image editing is a recently emerged and highly practical task, and its potential is immeasurable.
Bootstrap Diffusion Model Curve Estimation for High Resolution Low-Light Image Enhancement
Learning-based methods have attracted a lot of research attention and led to significant improvements in low-light image enhancement.
Video Task Decathlon: Unifying Image and Video Tasks in Autonomous Driving
VTD is a promising new direction for exploring the unification of perception tasks in autonomous driving.
CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models
With the emergence of Large Language Models (LLMs), there has been a significant improvement in the programming capabilities of models, attracting growing attention from researchers.
Object Detection Difficulty: Suppressing Over-aggregation for Faster and Better Video Object Detection
The ODD score enhances the VOD system in two ways: 1) it enables the VOD system to select superior global reference frames, thereby improving overall accuracy; and 2) it serves as an indicator in the newly designed ODD Scheduler to eliminate the aggregation of frames that are easy to detect, thus accelerating the VOD process.
BHSD: A 3D Multi-Class Brain Hemorrhage Segmentation Dataset
Intracranial hemorrhage (ICH) is a pathological condition characterized by bleeding inside the skull or brain, which can be attributed to various factors.
SegPrompt: Boosting Open-world Segmentation via Category-level Prompt Learning
In this work, we propose a novel training mechanism termed SegPrompt that uses category information to improve the model's class-agnostic segmentation ability for both known and unknown categories.
Category Feature Transformer for Semantic Segmentation
Aggregation of multi-stage features has been revealed to play a significant role in semantic segmentation.
Dynamic Token Pruning in Plain Vision Transformers for Semantic Segmentation
Experiments suggest that the proposed DToP architecture reduces on average $20\% - 35\%$ of computational cost for current semantic segmentation methods based on plain vision transformers without accuracy degradation.
CTVIS: Consistent Training for Online Video Instance Segmentation
The discrimination of instance embeddings plays a vital role in associating instances across time for online video instance segmentation (VIS).
Ranked #2 on
Video Instance Segmentation
on Youtube-VIS 2022 Validation
(using extra training data)
BPKD: Boundary Privileged Knowledge Distillation For Semantic Segmentation
Current knowledge distillation approaches in semantic segmentation tend to adopt a holistic approach that treats all spatial locations equally.
SegViTv2: Exploring Efficient and Continual Semantic Segmentation with Plain Vision Transformers
This paper investigates the capability of plain Vision Transformers (ViTs) for semantic segmentation using the encoder-decoder framework and introduces \textbf{SegViTv2}.
Ranked #16 on
Semantic Segmentation
on ADE20K
A Dynamic Feature Interaction Framework for Multi-task Visual Perception
In particular, one feature merge branch is designed for instance-level recognition the other for dense predictions.
Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification
Prompt learning has become a popular approach for adapting large vision-language models, such as CLIP, to downstream tasks.
Segment Anything in High Quality
HQ-SAM is only trained on the introduced detaset of 44k masks, which takes only 4 hours on 8 GPUs.
Ranked #1 on
Zero-Shot Instance Segmentation
on LVIS v1.0 val
Towards Hierarchical Policy Learning for Conversational Recommendation with Hypergraph-based Reinforcement Learning
Conversational recommendation systems (CRS) aim to timely and proactively acquire user dynamic preferred attributes through conversations for item recommendation.
Multi-Stage Coarse-to-Fine Contrastive Learning for Conversation Intent Induction
The key to automatic intention induction is that, for any given set of new data, the sentence representation obtained by the model can be well distinguished from different labels.
The state-of-the-art 3D anisotropic intracranial hemorrhage segmentation on non-contrast head CT: The INSTANCE challenge
It is intended to resolve the above-mentioned problems and promote the development of both intracranial hemorrhage segmentation and anisotropic data processing.
FedPD: Federated Open Set Recognition with Parameter Disentanglement
To this end, we aim to study a novel problem of federated open-set recognition (FedOSR), which learns an open-set recognition (OSR) model under federated paradigm such that it classifies seen classes while at the same time detects unknown classes.
3DPPE: 3D Point Positional Encoding for Transformer-based Multi-Camera 3D Object Detection
Although 3D measurements are not available at the inference time of monocular 3D object detection, 3DPPE uses predicted depth to approximate the real point positions.
ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation
Recently, CLIP has been applied to pixel-level zero-shot learning tasks via a two-stage scheme.
3DPPE: 3D Point Positional Encoding for Multi-Camera 3D Object Detection Transformers
Although 3D measurements are not available at the inference time of monocular 3D object detection, 3DPPE uses predicted depth to approximate the real point positions.
Prior-enhanced Temporal Action Localization using Subject-aware Spatial Attention
Temporal action localization (TAL) aims to detect the boundary and identify the class of each action instance in a long untrimmed video.
SegViT: Semantic Segmentation with Plain Vision Transformers
We explore the capability of plain Vision Transformers (ViTs) for semantic segmentation and propose the SegVit.
Ranked #4 on
Semantic Segmentation
on COCO-Stuff test
FAKD: Feature Augmented Knowledge Distillation for Semantic Segmentation
Inspired by the recent progress on semantic directions on feature-space, we propose to include augmentations in feature space for efficient distillation.
Towards Accurate Reconstruction of 3D Scene Shape from A Single Monocular Image
To do so, we propose a two-stage framework that first predicts depth up to an unknown scale and shift from a single monocular image, and then exploits 3D point cloud data to predict the depth shift and the camera's focal length that allow us to recover 3D scene shapes.
Semi-supervised Semantic Segmentation with Mutual Knowledge Distillation
More specifically, we use the pseudo-labels generated by a mean teacher to supervise the student network to achieve a mutual knowledge distillation between the two branches.
Improving Personality Consistency in Conversation by Persona Extending
Endowing chatbots with a consistent personality plays a vital role for agents to deliver human-like interactions.
Semantic-guided Multi-Mask Image Harmonization
Experiments have been conducted on our constructed benchmarks to verify that our proposed operator mask-based framework can locate and modify the inharmonious regions in more complex scenes.
Controllable Shadow Generation Using Pixel Height Maps
It can be used to calculate hard shadows in a 2D image based on the projective geometry, providing precise control of the shadows' direction and shape.
Ultra-sensitive Flexible Sponge-Sensor Array for Muscle Activities Detection and Human Limb Motion Recognition
Human limb motion tracking and recognition plays an important role in medical rehabilitation training, lower limb assistance, prosthetics design for amputees, feedback control for assistive robots, etc.
Efficient Video Segmentation Models with Per-frame Inference
To this end, we perform inference at each frame.
The devil is in the labels: Semantic segmentation from sentences
The resulting merged semantic segmentation dataset of over 2 Million images enables training a model that achieves performance equal to that of state-of-the-art supervised methods on 7 benchmark datasets, despite not using any images therefrom.
Ranked #1 on
Semantic Segmentation
on WildDash
Elevation Angle-Dependent 3D Trajectory Design for Aerial RIS-aided Communication
This paper investigates an aerial reconfigurable intelligent surface (RIS)-aided communication system under the probabilistic line-of-sight (LoS) channel, where an unmanned aerial vehicle (UAV) equipped with an RIS is deployed to assist two ground nodes in their information exchange.
A Generative Adversarial Framework for Optimizing Image Matting and Harmonization Simultaneously
Image matting, aiming to achieve foreground boundary details, and image harmonization, aiming to make the background compatible with the foreground, are both promising yet challenging tasks.
Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation
This neural representation enables our decoder to leverage the smoothness prior in the semantic label space, and thus makes our decoder more efficient.
X-GGM: Graph Generative Modeling for Out-of-Distribution Generalization in Visual Question Answering
In this paper, we formulate OOD generalization in VQA as a compositional generalization problem and propose a graph generative modeling-based training scheme (X-GGM) to implicitly model the problem.
Full-Dimensional Rate Enhancement for UAV-Enabled Communications via Intelligent Omni-Surface
This paper investigates the achievable rate maximization problem of a downlink unmanned aerial vehicle (UAV)-enabled communication system aided by an intelligent omni-surface (IOS).
A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data Augmentation
Previous works [3, 27] fail to employ strong augmentation in pseudo label learning efficiently, as the large distribution change caused by strong augmentation harms the batch normalisation statistics.
Generic Perceptual Loss for Modeling Structured Output Dependencies
We hope that this simple, extended perceptual loss may serve as a generic structured-output loss that is applicable to most structured output learning tasks.
Virtual Normal: Enforcing Geometric Constraints for Accurate and Robust Depth Prediction
In this work, we show the importance of the high-order 3D geometric constraints for depth prediction.
A Driving Behavior Recognition Model with Bi-LSTM and Multi-Scale CNN
In autonomous driving, perceiving the driving behaviors of surrounding agents is important for the ego-vehicle to make a reasonable decision.
Channel-wise Knowledge Distillation for Dense Prediction
Observing that in semantic segmentation, some layers' feature activations of each channel tend to encode saliency of scene categories (analogue to class activation mapping), we propose to align features channel-wise between the student and teacher networks.
Representative Graph Neural Network
In this paper, we present a Representative Graph (RepGraph) layer to dynamically sample a few representative features, which dramatically reduces redundancy.
Crowd Counting via Hierarchical Scale Recalibration Network
Extensive experiments on crowd counting datasets (ShanghaiTech, MALL, WorldEXPO'10, and UCSD) show that our HSRNet can deliver superior results over all state-of-the-art approaches.
Efficient Semantic Video Segmentation with Per-frame Inference
For semantic segmentation, most existing real-time deep models trained with each frame independently may produce inconsistent results for a video sequence.
Ranked #2 on
Video Semantic Segmentation
on CamVid
DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data
Compared with previous learning objectives, i. e., learning metric depth or relative depth, we propose to learn the affine-invariant depth using our diverse dataset to ensure both generalization and high-quality geometric shapes of scenes.
Auxiliary Learning for Deep Multi-task Learning
The most current methods can be categorized as either: (i) hard parameter sharing where a subset of the parameters is shared among tasks while other parameters are task-specific; or (ii) soft parameter sharing where all parameters are task-specific but they are jointly regularized.
MobileFAN: Transferring Deep Hidden Representation for Face Alignment
To this end, we propose an effective lightweight model, namely Mobile Face Alignment Network (MobileFAN), using a simple backbone MobileNetV2 as the encoder and three deconvolutional layers as the decoder.
Enforcing geometric constraints of virtual normal for depth prediction
Monocular depth prediction plays a crucial role in understanding 3D scene geometry.
Ranked #10 on
Depth Estimation
on NYU-Depth V2
AiFu at SemEval-2019 Task 10: A Symbolic and Sub-symbolic Integrated System for SAT Math Question Answering
AiFu has won the first place in the SemEval-2019 Task 10 - {''}Math Question Answering{''}competition.
Structured Knowledge Distillation for Semantic Segmentation
We further propose to distill the structured knowledge from cumbersome networks into compact networks, which is motivated by the fact that semantic segmentation is a structured prediction problem.
Structured Knowledge Distillation for Dense Prediction
Here we propose to distill structured knowledge from large networks to compact networks, taking into account the fact that dense prediction is a structured prediction problem.
Pixel Level Data Augmentation for Semantic Image Segmentation using Generative Adversarial Networks
Semantic segmentation is one of the basic topics in computer vision, it aims to assign semantic labels to every pixel of an image.
Data Augmentation in Emotion Classification Using Generative Adversarial Networks
In this paper, we propose a data augmentation method using generative adversarial networks (GAN).
Logical Parsing from Natural Language Based on a Neural Translation Model
Under our learning policy, the Seq2Seq model can learn mappings gradually with noises.
Auto-painter: Cartoon Image Generation from Sketch by Using Conditional Generative Adversarial Networks
Learning to generate colorful cartoon images from black-and-white sketches is not only an interesting research problem, but also a potential application in digital entertainment.
