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Found 23 papers, 7 papers with code
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
Tackling Noisy Labels with Network Parameter Additive Decomposition
Given data with noisy labels, over-parameterized deep networks suffer overfitting mislabeled data, resulting in poor generalization.
Efficient Prompt Tuning of Large Vision-Language Model for Fine-Grained Ship Classification
Directly fine-tuning VLMs for RS-FGSC often encounters the challenge of overfitting the seen classes, resulting in suboptimal generalization to unseen classes, which highlights the difficulty in differentiating complex backgrounds and capturing distinct ship features.
EAFP-Med: An Efficient Adaptive Feature Processing Module Based on Prompts for Medical Image Detection
EAFP-Med can efficiently extract lesion features from various medical images based on prompts, enhancing the model's performance.
Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models
By minimalistic, we restrict our family of BVQA models to build only upon basic blocks: a video preprocessor (for aggressive spatiotemporal downsampling), a spatial quality analyzer, an optional temporal quality analyzer, and a quality regressor, all with the simplest possible instantiations.
RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation
However, it lacks proficiency in referring video object segmentation (RVOS) due to the need for precise user-interactive prompts and a limited understanding of different modalities, such as language and vision.
CoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification
Although graph neural networks (GNNs) have achieved impressive achievements in graph classification, they often need abundant task-specific labels, which could be extensively costly to acquire.
MotionTrack: Learning Motion Predictor for Multiple Object Tracking
This challenge arises from two main factors: the insufficient discriminability of ReID features and the predominant utilization of linear motion models in MOT.
Ranked #4 on
Multi-Object Tracking
on SportsMOT
Null-text Guidance in Diffusion Models is Secretly a Cartoon-style Creator
Specifically, we proposed two disturbance methods, i. e., Rollback disturbance (Back-D) and Image disturbance (Image-D), to construct misalignment between the noisy images used for predicting null-text guidance and text guidance (subsequently referred to as \textbf{null-text noisy image} and \textbf{text noisy image} respectively) in the sampling process.
MagicFusion: Boosting Text-to-Image Generation Performance by Fusing Diffusion Models
The advent of open-source AI communities has produced a cornucopia of powerful text-guided diffusion models that are trained on various datasets.
Towards Radar Emitter Recognition in Changing Environments with Domain Generalization
Analyzing radar signals from complex Electronic Warfare (EW) environment is a non-trivial task. However, in the real world, the changing EW environment results in inconsistent signal distribution, such as the pulse repetition interval (PRI) mismatch between different detected scenes. In this paper, we propose a novel domain generalization framework to improve the adaptability of signal recognition in changing environments. Specifically, we first design several noise generators to simulate varied scenes.
Domain Specified Optimization for Deployment Authorization
As a complementary of SDPA, we also propose Target-Combined Deployment Authorization (TPDA), where unauthorized domains are partially accessible, and simplify the DSO method to a perturbation operation on the pseudo predictions, referred to as Target-Dependent Domain-Specified Optimization (TDSO).
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results
The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection.
Perceptual Quality Assessment of Virtual Reality Videos in the Wild
Investigating how people perceive virtual reality (VR) videos in the wild (i. e., those captured by everyday users) is a crucial and challenging task in VR-related applications due to complex authentic distortions localized in space and time.
APT-36K: A Large-scale Benchmark for Animal Pose Estimation and Tracking
Based on APT-36K, we benchmark several representative models on the following three tracks: (1) supervised animal pose estimation on a single frame under intra- and inter-domain transfer learning settings, (2) inter-species domain generalization test for unseen animals, and (3) animal pose estimation with animal tracking.
Bilateral Dependency Optimization: Defending Against Model-inversion Attacks
To defend against MI attacks, previous work utilizes a unilateral dependency optimization strategy, i. e., minimizing the dependency between inputs (i. e., features) and outputs (i. e., labels) during training the classifier.
Learning to Purification for Unsupervised Person Re-identification
To purify the label noise, we propose to take advantage of the knowledge of teacher model in an offline scheme.
Knowledge Distillation
Unsupervised Person Re-Identification
On the Equity of Nuclear Norm Maximization in Unsupervised Domain Adaptation
Nuclear norm maximization has shown the power to enhance the transferability of unsupervised domain adaptation model (UDA) in an empirical scheme.
Meta Attention For Off-Policy Actor-Critic
In this paper, we propose a meta attention method for state-based reinforcement learning tasks, which combines attention mechanism and meta-learning based on the Off-Policy Actor-Critic framework.
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation
To this end, we propose a target orientated hypothesis adaptation network (TOHAN) to solve the FHA problem, where we generate highly-compatible unlabeled data (i. e., an intermediate domain) to help train a target-domain classifier.
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data
In this paper, we demystify assumptions behind NCD and find that high-level semantic features should be shared among the seen and unseen classes.
Improving Unsupervised Domain Adaptation by Reducing Bi-level Feature Redundancy
In this paper, we emphasize the significance of reducing feature redundancy for improving UDA in a bi-level way.
Enhancing the Association in Multi-Object Tracking via Neighbor Graph
Most modern multi-object tracking (MOT) systems follow the tracking-by-detection paradigm.
