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Found 20 papers, 15 papers with code
MTMamba: Enhancing Multi-Task Dense Scene Understanding by Mamba-Based Decoders
Multi-task dense scene understanding, which learns a model for multiple dense prediction tasks, has a wide range of application scenarios.
MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs
Large language models (LLMs) have shown increasing capability in problem-solving and decision-making, largely based on the step-by-step chain-of-thought reasoning processes.
RoboCoder: Robotic Learning from Basic Skills to General Tasks with Large Language Models
To address these limitations, the RoboCoder framework integrates Large Language Models (LLMs) with a dynamic learning system that uses real-time environmental feedback to continuously update and refine action codes.
VLPose: Bridging the Domain Gap in Pose Estimation with Language-Vision Tuning
Our research aims to bridge the domain gap between natural and artificial scenarios with efficient tuning strategies.
MOODv2: Masked Image Modeling for Out-of-Distribution Detection
The crux of effective out-of-distribution (OOD) detection lies in acquiring a robust in-distribution (ID) representation, distinct from OOD samples.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
MR-GSM8K: A Meta-Reasoning Benchmark for Large Language Model Evaluation
In this work, we introduce a novel evaluation paradigm for Large Language Models (LLMs) that compels them to transition from a traditional question-answering role, akin to a student, to a solution-scoring role, akin to a teacher.
BAL: Balancing Diversity and Novelty for Active Learning
Experimental results demonstrate that, when labeling 80% of the samples, the performance of the current SOTA method declines by 0. 74%, whereas our proposed BAL achieves performance comparable to the full dataset.
MoTCoder: Elevating Large Language Models with Modular of Thought for Challenging Programming Tasks
Large Language Models (LLMs) have showcased impressive capabilities in handling straightforward programming tasks.
Ranked #3 on
Code Generation
on APPS
Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics
Defect inspection is paramount within the closed-loop manufacturing system.
Dual-Balancing for Multi-Task Learning
Multi-task learning (MTL), a learning paradigm to learn multiple related tasks simultaneously, has achieved great success in various fields.
TagCLIP: Improving Discrimination Ability of Open-Vocabulary Semantic Segmentation
Contrastive Language-Image Pre-training (CLIP) has recently shown great promise in pixel-level zero-shot learning tasks.
Rethinking Out-of-distribution (OOD) Detection: Masked Image Modeling is All You Need
The core of out-of-distribution (OOD) detection is to learn the in-distribution (ID) representation, which is distinguishable from OOD samples.
Ranked #12 on
Out-of-Distribution Detection
on ImageNet-1k vs Places
(AUROC metric)
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.
Adversarial Attacks on ML Defense Models Competition
Due to the vulnerability of deep neural networks (DNNs) to adversarial examples, a large number of defense techniques have been proposed to alleviate this problem in recent years.
Deep Structured Instance Graph for Distilling Object Detectors
Effectively structuring deep knowledge plays a pivotal role in transfer from teacher to student, especially in semantic vision tasks.
Exploring and Improving Mobile Level Vision Transformers
We analyze the reason behind this phenomenon, and propose a novel irregular patch embedding module and adaptive patch fusion module to improve the performance.
Distilling Knowledge via Knowledge Review
Knowledge distillation transfers knowledge from the teacher network to the student one, with the goal of greatly improving the performance of the student network.
Ranked #13 on
Knowledge Distillation
on CIFAR-100
Jigsaw Clustering for Unsupervised Visual Representation Learning
It is even comparable to the contrastive learning methods when only half of training batches are used.
GridMask Data Augmentation
Then we show limitation of existing information dropping algorithms and propose our structured method, which is simple and yet very effective.
