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Found 42 papers, 13 papers with code
Enhancing LLM Reasoning with Multi-Path Collaborative Reactive and Reflection agents
Agents have demonstrated their potential in scientific reasoning tasks through large language models.
A2RNet: Adversarial Attack Resilient Network for Robust Infrared and Visible Image Fusion
Infrared and visible image fusion (IVIF) is a crucial technique for enhancing visual performance by integrating unique information from different modalities into one fused image.
TopV-Nav: Unlocking the Top-View Spatial Reasoning Potential of MLLM for Zero-shot Object Navigation
Such a goal-oriented exploration heavily relies on the ability to perceive, understand, and reason based on the spatial information of the environment.
CreDes: Causal Reasoning Enhancement and Dual-End Searching for Solving Long-Range Reasoning Problems using LLMs
Large language models (LLMs) have demonstrated limitations in handling combinatorial optimization problems involving long-range reasoning, partially due to causal hallucinations and huge search space.
Exploring Information-Theoretic Metrics Associated with Neural Collapse in Supervised Training
Experiments show that matrix entropy cannot solely describe the interaction of the information content of data representation and classification head weights but it can effectively reflect the similarity and clustering behavior of the data.
CSCE: Boosting LLM Reasoning by Simultaneous Enhancing of Causal Significance and Consistency
Chain-based reasoning methods like chain of thought (CoT) play a rising role in solving reasoning tasks for large language models (LLMs).
Synergistic Spotting and Recognition of Micro-Expression via Temporal State Transition
The analysis of micro-expressions generally involves two main tasks: spotting micro-expression intervals in long videos and recognizing the emotions associated with these intervals.
Unlocking Attributes' Contribution to Successful Camouflage: A Combined Textual and VisualAnalysis Strategy
In the domain of Camouflaged Object Segmentation (COS), despite continuous improvements in segmentation performance, the underlying mechanisms of effective camouflage remain poorly understood, akin to a black box.
Unveiling the Dynamics of Information Interplay in Supervised Learning
In this paper, we use matrix information theory as an analytical tool to analyze the dynamics of the information interplay between data representations and classification head vectors in the supervised learning process.
ODGEN: Domain-specific Object Detection Data Generation with Diffusion Models
Given a domain-specific object detection dataset, we first fine-tune a pre-trained diffusion model on both cropped foreground objects and entire images to fit target distributions.
RhythmMamba: Fast Remote Physiological Measurement with Arbitrary Length Videos
Remote photoplethysmography (rPPG) is a non-contact method for detecting physiological signals from facial videos, holding great potential in various applications such as healthcare, affective computing, and anti-spoofing.
Isolated Diffusion: Optimizing Multi-Concept Text-to-Image Generation Training-Freely with Isolated Diffusion Guidance
This paper presents a general approach for text-to-image diffusion models to address the mutual interference between different subjects and their attachments in complex scenes, pursuing better text-image consistency.
RhythmFormer: Extracting Patterned rPPG Signals based on Periodic Sparse Attention
Remote photoplethysmography (rPPG) is a non-contact method for detecting physiological signals based on facial videos, holding high potential in various applications.
Learning a Graph Neural Network with Cross Modality Interaction for Image Fusion
Finally, we merge all graph features to get the fusion result.
DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited Data
Typical diffusion models and modern large-scale conditional generative models like text-to-image generative models are vulnerable to overfitting when fine-tuned on extremely limited data.
Few-shot 3D Shape Generation
Our approach only needs the silhouettes of few-shot target samples as training data to learn target geometry distributions and achieve generated shapes with diverse topology and textures.
Enhancing Short-Term Wind Speed Forecasting using Graph Attention and Frequency-Enhanced Mechanisms
In this paper, a Graph-attentive Frequency-enhanced Spatial-Temporal Wind Speed Forecasting model based on graph attention and frequency-enhanced mechanisms, i. e., GFST-WSF, is proposed to improve the accuracy of short-term wind speed forecasting.
MotionVideoGAN: A Novel Video Generator Based on the Motion Space Learned from Image Pairs
We present MotionVideoGAN, a novel video generator synthesizing videos based on the motion space learned by pre-trained image pair generators.
Gestalt-Guided Image Understanding for Few-Shot Learning
Due to the scarcity of available data, deep learning does not perform well on few-shot learning tasks.
Distribution Aligned Feature Clustering for Zero-Shot Sketch-Based Image Retrieval
Zero-Shot Sketch-Based Image Retrieval (ZS-SBIR) is a challenging cross-modal retrieval task.
Few-shot Image Generation via Masked Discrimination
It strengthens global image discrimination and guides adapted GANs to preserve more information learned from source domains for higher image quality.
Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems
We motivate our paradigm through a variational perspective, where the learning objective can be decomposed into two terms: task learning on the current task distribution, and curriculum update to a new task distribution.
Video Frame Interpolation via Structure-Motion based Iterative Fusion
Inspired by the observation that audiences have different visual preferences on foreground and background objects, we for the first time propose to use saliency masks in the evaluation processes of the task of video frame interpolation.
Defending Against Universal Adversarial Patches by Clipping Feature Norms
Physical-world adversarial attacks based on universal adversarial patches have been proved to be able to mislead deep convolutional neural networks (CNNs), exposing the vulnerability of real-world visual classification systems based on CNNs.
Unsupervised segmentation via semantic-apparent feature fusion
In order to solve this problem, the research proposes an unsupervised foreground segmentation method based on semantic-apparent feature fusion (SAFF).
Realizing Pixel-Level Semantic Learning in Complex Driving Scenes based on Only One Annotated Pixel per Class
Semantic segmentation tasks based on weakly supervised condition have been put forward to achieve a lightweight labeling process.
Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning
In this paper, we propose an iterative algorithm to learn such pairwise relations, which consists of two branches, a unary segmentation network which learns the label probabilities for each pixel, and a pairwise affinity network which learns affinity matrix and refines the probability map generated from the unary network.
Semantic Head Enhanced Pedestrian Detection in a Crowd
Pedestrian detection in the crowd is a challenging task because of intra-class occlusion.
Occluded Pedestrian Detection with Visible IoU and Box Sign Predictor
Training a robust classifier and an accurate box regressor are difficult for occluded pedestrian detection.
WSOD with PSNet and Box Regression
To solve this problem, we added the box regression module to the weakly supervised object detection network and proposed a proposal scoring network (PSNet) to supervise it.
Ranked #22 on
Weakly Supervised Object Detection
on PASCAL VOC 2007
Pretrain Soft Q-Learning with Imperfect Demonstrations
Pretraining reinforcement learning methods with demonstrations has been an important concept in the study of reinforcement learning since a large amount of computing power is spent on online simulations with existing reinforcement learning algorithms.
Weakly-Supervised Semantic Segmentation by Iteratively Mining Common Object Features
Then in the top-down step, the refined object regions are used as supervision to train the segmentation network and to predict object masks.
Driving maneuvers prediction based on cognition-driven and data-driven method
To overcome this challenge, we propose a novel method that combines both the cognition-driven model and the data-driven model.
Pretraining Deep Actor-Critic Reinforcement Learning Algorithms With Expert Demonstrations
We apply our method to two of the typical actor-critic reinforcement learning algorithms, DDPG and ACER, and demonstrate with experiments that our method not only outperforms the RL algorithms without pretraining process, but also is more simulation efficient.
Single Image Action Recognition using Semantic Body Part Actions
Second, for each body parts, a Part Action Res-Net is used to predict semantic body part actions.
Multi-View 3D Object Detection Network for Autonomous Driving
We encode the sparse 3D point cloud with a compact multi-view representation.
Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection
In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection.
3D Object Proposals using Stereo Imagery for Accurate Object Class Detection
We then exploit a CNN on top of these proposals to perform object detection.
Monocular 3D Object Detection for Autonomous Driving
The focus of this paper is on proposal generation.
Ranked #8 on
Vehicle Pose Estimation
on KITTI Cars Hard
3D Object Proposals for Accurate Object Class Detection
The goal of this paper is to generate high-quality 3D object proposals in the context of autonomous driving.
Ranked #10 on
Vehicle Pose Estimation
on KITTI Cars Hard
Improving Object Proposals With Multi-Thresholding Straddling Expansion
Based on the characteristics of superpixel tightness distribution, we propose an effective method, namely multi-thresholding straddling expansion (MTSE) to reduce localization bias via fast diversification.
