Search Results for author:
Found 35 papers, 12 papers with code
Mind the Context: The Impact of Contextualization in Neural Module Networks for Grounding Visual Referring Expressions
Our model outperforms the state-of-the-art NMN model on CLEVR-Ref+ dataset with +8. 1% improvement in accuracy on the single-referent test set and +4. 3% on the full test set.
SQLNet: Scale-Modulated Query and Localization Network for Few-Shot Class-Agnostic Counting
In the localization stage, the Scale-aware Multi-head Localization (SAML) module utilizes the query tensor to predict the confidence, location, and size of each potential object.
VisualProg Distiller: Learning to Fine-tune Non-differentiable Visual Programming Frameworks
As an interpretable and universal neuro-symbolic paradigm based on Large Language Models, visual programming (VisualProg) can execute compositional visual tasks without training, but its performance is markedly inferior compared to task-specific supervised learning models.
Towards CausalGPT: A Multi-Agent Approach for Faithful Knowledge Reasoning via Promoting Causal Consistency in LLMs
Despite advancements in LLMs, knowledge-based reasoning remains a longstanding issue due to the fragility of knowledge recall and inference.
TCGL: Temporal Contrastive Graph for Self-supervised Video Representation Learning
To overcome these limitations, we take advantage of the multi-scale temporal dependencies within videos and proposes a novel video self-supervised learning framework named Temporal Contrastive Graph Learning (TCGL), which jointly models the inter-snippet and intra-snippet temporal dependencies for temporal representation learning with a hybrid graph contrastive learning strategy.
Enhancing Prototypical Few-Shot Learning by Leveraging the Local-Level Strategy
Aiming at recognizing the samples from novel categories with few reference samples, few-shot learning (FSL) is a challenging problem.
CX-ToM: Counterfactual Explanations with Theory-of-Mind for Enhancing Human Trust in Image Recognition Models
More concretely, our CX-ToM framework generates sequence of explanations in a dialog by mediating the differences between the minds of machine and human user.
Solving Inefficiency of Self-supervised Representation Learning
In this paper, we reveal two contradictory phenomena in contrastive learning that we call under-clustering and over-clustering problems, which are major obstacles to learning efficiency.
Ranked #1 on
Self-Supervised Person Re-Identification
on SYSU-30k
Temporal Contrastive Graph Learning for Video Action Recognition and Retrieval
To model multi-scale temporal dependencies, our TCGL integrates the prior knowledge about the frame and snippet orders into graph structures, i. e., the intra-/inter- snippet temporal contrastive graphs.
Linguistically Routing Capsule Network for Out-of-Distribution Visual Question Answering
The experimental results show that our proposed method can improve current VQA models on OOD split without losing performance on the in-domain test data.
Knowledge-Routed Visual Question Reasoning: Challenges for Deep Representation Embedding
Specifically, we generate the question-answer pair based on both the Visual Genome scene graph and an external knowledge base with controlled programs to disentangle the knowledge from other biases.
Continuous Transition: Improving Sample Efficiency for Continuous Control Problems via MixUp
Although deep reinforcement learning (RL) has been successfully applied to a variety of robotic control tasks, it's still challenging to apply it to real-world tasks, due to the poor sample efficiency.
Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition
In this paper, we propose a novel framework, named Semantics-aware Adaptive Knowledge Distillation Networks (SAKDN), to enhance action recognition in vision-sensor modality (videos) by adaptively transferring and distilling the knowledge from multiple wearable sensors.
Linguistically Driven Graph Capsule Network for Visual Question Reasoning
Inspired by the property of a capsule network that can carve a tree structure inside a regular convolutional neural network (CNN), we propose a hierarchical compositional reasoning model called the "Linguistically driven Graph Capsule Network", where the compositional process is guided by the linguistic parse tree.
Towards Causality-Aware Inferring: A Sequential Discriminative Approach for Medical Diagnosis
To eliminate this bias and inspired by the propensity score matching technique with causal diagram, we propose a propensity-based patient simulator to effectively answer unrecorded inquiry by drawing knowledge from the other records; Bias (ii) inherently comes along with the passively collected data, and is one of the key obstacles for training the agent towards "learning how" rather than "remembering what".
Instance-Aware Representation Learning and Association for Online Multi-Person Tracking
Multi-Person Tracking (MPT) is often addressed within the detection-to-association paradigm.
Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning
Face hallucination is a domain-specific super-resolution problem that aims to generate a high-resolution (HR) face image from a low-resolution~(LR) input.
Adaptively Connected Neural Networks
This paper presents a novel adaptively connected neural network (ACNet) to improve the traditional convolutional neural networks (CNNs) {in} two aspects.
Ranked #1 on
Document Classification
on Cora
3D Human Pose Machines with Self-supervised Learning
Driven by recent computer vision and robotic applications, recovering 3D human poses has become increasingly important and attracted growing interests.
Ranked #258 on
3D Human Pose Estimation
on Human3.6M
Teaching to Teach by Structured Dark Knowledge
We evaluate T2T across different learners, teachers, and tasks, which significantly demonstrates that structured knowledge can be inherited by the teachers to further benefit learners' training.
Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria
The proposed process can be compatible with mini-batch based training (i. e., using a batch of unlabeled or partially labeled data as a one-time input) for object detection.
Flow Guided Recurrent Neural Encoder for Video Salient Object Detection
Image saliency detection has recently witnessed significant progress due to deep convolutional neural networks.
Ranked #2 on
Video Salient Object Detection
on DAVSOD-Difficult20
(using extra training data)
Towards Human-Machine Cooperation: Self-supervised Sample Mining for Object Detection
Though quite challenging, leveraging large-scale unlabeled or partially labeled images in a cost-effective way has increasingly attracted interests for its great importance to computer vision.
Recurrent 3D Pose Sequence Machines
3D human articulated pose recovery from monocular image sequences is very challenging due to the diverse appearances, viewpoints, occlusions, and also the human 3D pose is inherently ambiguous from the monocular imagery.
Ranked #20 on
3D Human Pose Estimation
on HumanEva-I
Deep Co-Space: Sample Mining Across Feature Transformation for Semi-Supervised Learning
Aiming at improving performance of visual classification in a cost-effective manner, this paper proposes an incremental semi-supervised learning paradigm called Deep Co-Space (DCS).
Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning
Single image super resolution (SR), which refers to reconstruct a higher-resolution (HR) image from the observed low-resolution (LR) image, has received substantial attention due to its tremendous application potentials.
Active Self-Paced Learning for Cost-Effective and Progressive Face Identification
By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert re-certification.
Cost-Effective Active Learning for Deep Image Classification
In this paper, we propose a novel active learning framework, which is capable of building a competitive classifier with optimal feature representation via a limited amount of labeled training instances in an incremental learning manner.
Human Pose Estimation from Depth Images via Inference Embedded Multi-task Learning
In this paper, we propose a novel inference-embedded multi-task learning framework for predicting human pose from still depth images, which is implemented with a deep architecture of neural networks.
Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning
Recently, machine learning based single image super resolution (SR) approaches focus on jointly learning representations for high-resolution (HR) and low-resolution (LR) image patch pairs to improve the quality of the super-resolved images.
Dictionary Pair Classifier Driven Convolutional Neural Networks for Object Detection
Feature representation and object category classification are two key components of most object detection methods.
A Deep Structured Model with Radius-Margin Bound for 3D Human Activity Recognition
Understanding human activity is very challenging even with the recently developed 3D/depth sensors.
PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures with Edge-Preserving Coherence
In this paper, we propose a unified framework called PISA, which stands for Pixelwise Image Saliency Aggregating various bottom-up cues and priors.
3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks
Our model thus advances existing approaches in two aspects: (i) it acts directly on the raw inputs (grayscale-depth data) to conduct recognition instead of relying on hand-crafted features, and (ii) the model structure can be dynamically adjusted accounting for the temporal variations of human activities, i. e. the network configuration is allowed to be partially activated during inference.
PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures with Spatial Priors
By fusing complementary contrast measures in such a pixelwise adaptive manner, the detection effectiveness is significantly boosted.
