Search Results for author:
Found 33 papers, 26 papers with code
Referencing Where to Focus: Improving VisualGrounding with Referential Query
It consists of the query adaption module that can be seamlessly integrated into CLIP and generate the referential query to provide the prior context for decoder, along with a task-specific decoder.
VisionZip: Longer is Better but Not Necessary in Vision Language Models
To address this, we introduce VisionZip, a simple yet effective method that selects a set of informative tokens for input to the language model, reducing visual token redundancy and improving efficiency while maintaining model performance.
Ranked #172 on
Visual Question Answering
on MM-Vet
Typicalness-Aware Learning for Failure Detection
We observe that, with the cross-entropy loss, model predictions are optimized to align with the corresponding labels via increasing logit magnitude or refining logit direction.
Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic Segmentation
CLIP, as a vision-language model, has significantly advanced Open-Vocabulary Semantic Segmentation (OVSS) with its zero-shot capabilities.
Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Models
To address this problem efficiently, we propose the Distribution-aware Interference-free Knowledge Integration (DIKI) framework, retaining pre-trained knowledge of VLMs from a perspective of avoiding information interference.
Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs
Mathematical reasoning presents a significant challenge for Large Language Models (LLMs) due to the extensive and precise chain of reasoning required for accuracy.
Ranked #11 on
Arithmetic Reasoning
on GSM8K
(using extra training data)
Scalable Language Model with Generalized Continual Learning
In this study, we introduce the Scalable Language Model (SLM) to overcome these limitations within a more challenging and generalized setting, representing a significant advancement toward practical applications for continual learning.
Unified Language-driven Zero-shot Domain Adaptation
This paper introduces Unified Language-driven Zero-shot Domain Adaptation (ULDA), a novel task setting that enables a single model to adapt to diverse target domains without explicit domain-ID knowledge.
OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmentation
This exploration led to the creation of Omni-Adaptive 3D CNNs (OA-CNNs), a family of networks that integrates a lightweight module to greatly enhance the adaptivity of sparse CNNs at minimal computational cost.
Ranked #5 on
3D Semantic Segmentation
on SemanticKITTI
(val mIoU metric)
GroupContrast: Semantic-aware Self-supervised Representation Learning for 3D Understanding
To address this issue, we propose GroupContrast, a novel approach that combines segment grouping and semantic-aware contrastive learning.
SaCo Loss: Sample-wise Affinity Consistency for Vision-Language Pre-training
We discover that the lack of consideration for sample-wise affinity consistency across modalities in existing training objectives is the central cause.
LISA++: An Improved Baseline for Reasoning Segmentation with Large Language Model
While LISA effectively bridges the gap between segmentation and large language models to enable reasoning segmentation, it poses certain limitations: unable to distinguish different instances of the target region, and constrained by the pre-defined textual response formats.
Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Training
In contrast, such privilege has not yet fully benefited 3D deep learning, mainly due to the limited availability of large-scale 3D datasets.
Ranked #3 on
3D Semantic Segmentation
on SemanticKITTI
(val mIoU metric, using extra
training data)
Boosting Few-shot 3D Point Cloud Segmentation via Query-Guided Enhancement
Although extensive research has been conducted on 3D point cloud segmentation, effectively adapting generic models to novel categories remains a formidable challenge.
LISA: Reasoning Segmentation via Large Language Model
In this work, we propose a new segmentation task -- reasoning segmentation.
Hierarchical Dense Correlation Distillation for Few-Shot Segmentation-Extended Abstract
We hope our work can benefit broader industrial applications where novel classes with limited annotations are required to be decently identified.
Decoupled Kullback-Leibler Divergence Loss
In this paper, we delve deeper into the Kullback-Leibler (KL) Divergence loss and mathematically prove that it is equivalent to the Decoupled Kullback-Leibler (DKL) Divergence loss that consists of 1) a weighted Mean Square Error (wMSE) loss and 2) a Cross-Entropy loss incorporating soft labels.
Hierarchical Dense Correlation Distillation for Few-Shot Segmentation
Few-shot semantic segmentation (FSS) aims to form class-agnostic models segmenting unseen classes with only a handful of annotations.
Ranked #8 on
Few-Shot Semantic Segmentation
on PASCAL-5i (1-Shot)
Learning Context-aware Classifier for Semantic Segmentation
Semantic segmentation is still a challenging task for parsing diverse contexts in different scenes, thus the fixed classifier might not be able to well address varying feature distributions during testing.
Spatial Pruned Sparse Convolution for Efficient 3D Object Detection
3D scenes are dominated by a large number of background points, which is redundant for the detection task that mainly needs to focus on foreground objects.
Generalized Parametric Contrastive Learning
Based on theoretical analysis, we observe that supervised contrastive loss tends to bias high-frequency classes and thus increases the difficulty of imbalanced learning.
Ranked #7 on
Long-tail Learning
on iNaturalist 2018
Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions
Over the past few years, the rapid development of deep learning technologies for computer vision has significantly improved the performance of medical image segmentation (MedISeg).
DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation
Unsupervised domain adaptation in semantic segmentation has been raised to alleviate the reliance on expensive pixel-wise annotations.
Region Rebalance for Long-Tailed Semantic Segmentation
In this paper, we study the problem of class imbalance in semantic segmentation.
Ranked #22 on
Semantic Segmentation
on ADE20K
SEA: Bridging the Gap Between One- and Two-stage Detector Distillation via SEmantic-aware Alignment
We revisit the one- and two-stage detector distillation tasks and present a simple and efficient semantic-aware framework to fill the gap between them.
Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation
To address the high cost and challenges of 3D point-level labeling, we present a method for semi-supervised point cloud semantic segmentation to adopt unlabeled point clouds in training to boost the model performance.
PFENet++: Boosting Few-shot Semantic Segmentation with the Noise-filtered Context-aware Prior Mask
In this work, we revisit the prior mask guidance proposed in ``Prior Guided Feature Enrichment Network for Few-Shot Segmentation''.
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency
Semantic segmentation has made tremendous progress in recent years.
ResLT: Residual Learning for Long-tailed Recognition
From this perspective, the trivial solution utilizes different branches for the head, medium, and tail classes respectively, and then sums their outputs as the final results is not feasible.
Ranked #22 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
Generalized Few-shot Semantic Segmentation
Then, since context is essential for semantic segmentation, we propose the Context-Aware Prototype Learning (CAPL) that significantly improves performance by 1) leveraging the co-occurrence prior knowledge from support samples, and 2) dynamically enriching contextual information to the classifier, conditioned on the content of each query image.
Prior Guided Feature Enrichment Network for Few-Shot Segmentation
It consists of novel designs of (1) a training-free prior mask generation method that not only retains generalization power but also improves model performance and (2) Feature Enrichment Module (FEM) that overcomes spatial inconsistency by adaptively enriching query features with support features and prior masks.
Ranked #71 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
Region Refinement Network for Salient Object Detection
Albeit intensively studied, false prediction and unclear boundaries are still major issues of salient object detection.
Learning Shape-Aware Embedding for Scene Text Detection
We address the problem of detecting scene text in arbitrary shapes, which is a challenging task due to the high variety and complexity of the scene.
