Search Results for author:
Found 16 papers, 9 papers with code
Domain-Rectifying Adapter for Cross-Domain Few-Shot Segmentation
Instead, our key idea is to adapt a small adapter for rectifying diverse target domain styles to the source domain.
ONNXPruner: ONNX-Based General Model Pruning Adapter
However, the practical application of these algorithms across various models and platforms remains a significant challenge.
InternLM2 Technical Report
The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI).
Ranked #5 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
DARNet: Bridging Domain Gaps in Cross-Domain Few-Shot Segmentation with Dynamic Adaptation
Moreover, recognizing the variability across target domains, an Adaptive Refine Self-Matching (ARSM) method is also proposed to adjust the matching threshold and dynamically refine the prediction result with the self-matching method, enhancing accuracy.
Stable Segment Anything Model
Thus, our solution, termed Stable-SAM, offers several advantages: 1) improved SAM's segmentation stability across a wide range of prompt qualities, while 2) retaining SAM's powerful promptable segmentation efficiency and generality, with 3) minimal learnable parameters (0. 08 M) and fast adaptation (by 1 training epoch).
Selective Feature Adapter for Dense Vision Transformers
Specifically, SFA consists of external adapters and internal adapters which are sequentially operated over a transformer model.
Learning Noise-Robust Joint Representation for Multimodal Emotion Recognition under Realistic Incomplete Data Scenarios
Multimodal emotion recognition (MER) in practical scenarios presents a significant challenge due to the presence of incomplete data, such as missing or noisy data.
UniBoost: Unsupervised Unimodal Pre-training for Boosting Zero-shot Vision-Language Tasks
Our thorough studies validate that models pre-trained as such can learn rich representations of both modalities, improving their ability to understand how images and text relate to each other.
Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts
Thus, we propose to perturb the channel statistics of source domain features to synthesize various latent styles, so that the trained deep model can perceive diverse potential domains and generalizes well even without observations of target domain data in training.
Self-Support Few-Shot Semantic Segmentation
Motivated by the simple Gestalt principle that pixels belonging to the same object are more similar than those to different objects of same class, we propose a novel self-support matching strategy to alleviate this problem, which uses query prototypes to match query features, where the query prototypes are collected from high-confidence query predictions.
Ranked #12 on
Few-Shot Semantic Segmentation
on PASCAL-5i (5-Shot)
GCoNet+: A Stronger Group Collaborative Co-Salient Object Detector
In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in natural scenes.
Ranked #1 on
Co-Salient Object Detection
on CoCA
Few-Shot Video Object Detection
We introduce Few-Shot Video Object Detection (FSVOD) with three contributions to real-world visual learning challenge in our highly diverse and dynamic world: 1) a large-scale video dataset FSVOD-500 comprising of 500 classes with class-balanced videos in each category for few-shot learning; 2) a novel Tube Proposal Network (TPN) to generate high-quality video tube proposals for aggregating feature representation for the target video object which can be highly dynamic; 3) a strategically improved Temporal Matching Network (TMN+) for matching representative query tube features with better discriminative ability thus achieving higher diversity.
Group Collaborative Learning for Co-Salient Object Detection
We present a novel group collaborative learning framework (GCoNet) capable of detecting co-salient objects in real time (16ms), by simultaneously mining consensus representations at group level based on the two necessary criteria: 1) intra-group compactness to better formulate the consistency among co-salient objects by capturing their inherent shared attributes using our novel group affinity module; 2) inter-group separability to effectively suppress the influence of noisy objects on the output by introducing our new group collaborating module conditioning the inconsistent consensus.
Ranked #5 on
Co-Salient Object Detection
on CoCA
Commonality-Parsing Network across Shape and Appearance for Partially Supervised Instance Segmentation
We propose to learn the underlying class-agnostic commonalities that can be generalized from mask-annotated categories to novel categories.
Ranked #79 on
Instance Segmentation
on COCO test-dev
Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector
To train our network, we contribute a new dataset that contains 1000 categories of various objects with high-quality annotations.
Ranked #21 on
Few-Shot Object Detection
on MS-COCO (10-shot)
Real-Time Influence Maximization on Dynamic Social Streams
Influence maximization (IM), which selects a set of $k$ users (called seeds) to maximize the influence spread over a social network, is a fundamental problem in a wide range of applications such as viral marketing and network monitoring.
Social and Information Networks Data Structures and Algorithms
