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Found 23 papers, 13 papers with code
kNN-CLIP: Retrieval Enables Training-Free Segmentation on Continually Expanding Large Vocabularies
Rapid advancements in continual segmentation have yet to bridge the gap of scaling to large continually expanding vocabularies under compute-constrained scenarios.
SynArtifact: Classifying and Alleviating Artifacts in Synthetic Images via Vision-Language Model
To alleviate artifacts and improve quality of synthetic images, we fine-tune Vision-Language Model (VLM) as artifact classifier to automatically identify and classify a wide range of artifacts and provide supervision for further optimizing generative models.
RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model
Recent advancements in Multi-Modal Large Language models (MLLMs) have shown promising potential in enhancing the explainability as a driving agent by producing control predictions along with natural language explanations.
CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor
Mask labels are labor-intensive, which limits the number of categories in segmentation datasets.
Real-Fake: Effective Training Data Synthesis Through Distribution Matching
Synthetic training data has gained prominence in numerous learning tasks and scenarios, offering advantages such as dataset augmentation, generalization evaluation, and privacy preservation.
OxfordTVG-HIC: Can Machine Make Humorous Captions from Images?
Hence, humour generation and understanding can serve as a new task for evaluating the ability of deep-learning methods to process abstract and subjective information.
ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation
This paper presents a new mechanism to facilitate the training of mask transformers for efficient panoptic segmentation, democratizing its deployment.
LUMix: Improving Mixup by Better Modelling Label Uncertainty
LUMix is simple as it can be implemented in just a few lines of code and can be universally applied to any deep networks \eg CNNs and Vision Transformers, with minimal computational cost.
Is synthetic data from generative models ready for image recognition?
Recent text-to-image generation models have shown promising results in generating high-fidelity photo-realistic images.
Knowledge Distillation as Efficient Pre-training: Faster Convergence, Higher Data-efficiency, and Better Transferability
Large-scale pre-training has been proven to be crucial for various computer vision tasks.
Slot-VPS: Object-centric Representation Learning for Video Panoptic Segmentation
We encode all panoptic entities in a video, including both foreground instances and background semantics, with a unified representation called panoptic slots.
TransMix: Attend to Mix for Vision Transformers
The confidence of the label will be larger if the corresponding input image is weighted higher by the attention map.
Vision Transformer with Progressive Sampling
As a typical example, the Vision Transformer (ViT) directly applies a pure transformer architecture on image classification, by simply splitting images into tokens with a fixed length, and employing transformers to learn relations between these tokens.
Visual Parser: Representing Part-whole Hierarchies with Transformers
To model the representations of the two levels, we first encode the information from the whole into part vectors through an attention mechanism, then decode the global information within the part vectors back into the whole representation.
Ranked #313 on
Image Classification
on ImageNet
Aggregation With Feature Detection
Aggregating features from different depths of a network is widely adopted to improve the network capability.
Learning to Sample the Most Useful Training Patches from Images
Some image restoration tasks like demosaicing require difficult training samples to learn effective models.
Exploring the Hierarchy in Relation Labels for Scene Graph Generation
By assigning each relationship a single label, current approaches formulate the relationship detection as a classification problem.
Robust Multi-Modality Multi-Object Tracking
Multi-sensor perception is crucial to ensure the reliability and accuracy in autonomous driving system, while multi-object tracking (MOT) improves that by tracing sequential movement of dynamic objects.
Ranked #10 on
Multiple Object Tracking
on KITTI Tracking test
MMDetection: Open MMLab Detection Toolbox and Benchmark
In this paper, we introduce the various features of this toolbox.
Hybrid Task Cascade for Instance Segmentation
In exploring a more effective approach, we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation.
Ranked #32 on
Object Detection
on COCO-O
FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction
The basic principles in designing convolutional neural network (CNN) structures for predicting objects on different levels, e. g., image-level, region-level, and pixel-level are diverging.
Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition
In this study, we introduce a novel compact motion representation for video action recognition, named Optical Flow guided Feature (OFF), which enables the network to distill temporal information through a fast and robust approach.
Ranked #36 on
Action Recognition
on UCF101
Spindle Net: Person Re-Identification With Human Body Region Guided Feature Decomposition and Fusion
Person re-identification (ReID) is an important task in video surveillance and has various applications.
