Search Results for author:
Found 117 papers, 70 papers with code
How to Detect and Defeat Molecular Mirage: A Metric-Driven Benchmark for Hallucination in LLM-based Molecular Comprehension
Large language models are increasingly used in scientific domains, especially for molecular understanding and analysis.
GPT-ImgEval: A Comprehensive Benchmark for Diagnosing GPT4o in Image Generation
The recent breakthroughs in OpenAI's GPT4o model have demonstrated surprisingly good capabilities in image generation and editing, resulting in significant excitement in the community.
NeuralGS: Bridging Neural Fields and 3D Gaussian Splatting for Compact 3D Representations
The compression performance of our method on original 3DGS is comparable to the dedicated Scaffold-GS-based compression methods, which demonstrate the huge potential of directly compressing original 3DGS with neural fields.
LangBridge: Interpreting Image as a Combination of Language Embeddings
Based on this insight, we propose LangBridge, a novel adapter that explicitly maps visual tokens to linear combinations of LLM vocabulary embeddings.
UPME: An Unsupervised Peer Review Framework for Multimodal Large Language Model Evaluation
Multimodal Large Language Models (MLLMs) have emerged to tackle the challenges of Visual Question Answering (VQA), sparking a new research focus on conducting objective evaluations of these models.
SwapAnyone: Consistent and Realistic Video Synthesis for Swapping Any Person into Any Video
Video body-swapping aims to replace the body in an existing video with a new body from arbitrary sources, which has garnered more attention in recent years.
WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation
Text-to-Image (T2I) models are capable of generating high-quality artistic creations and visual content.
UniGenX: Unified Generation of Sequence and Structure with Autoregressive Diffusion
We validate the effectiveness of UniGenX on material and small molecule generation tasks, achieving a significant leap in state-of-the-art performance for material crystal structure prediction and establishing new state-of-the-art results for small molecule structure prediction, de novo design, and conditional generation.
Magic 1-For-1: Generating One Minute Video Clips within One Minute
The key idea is simple: factorize the text-to-video generation task into two separate easier tasks for diffusion step distillation, namely text-to-image generation and image-to-video generation.
GPT as a Monte Carlo Language Tree: A Probabilistic Perspective
In this paper, we propose a novel perspective that any language dataset can be represented by a Monte Carlo Language Tree (abbreviated as ``Data-Tree''), where each node denotes a token, each edge denotes a token transition probability, and each sequence has a unique path.
AE-NeRF: Augmenting Event-Based Neural Radiance Fields for Non-ideal Conditions and Larger Scene
Compared to frame-based methods, computational neuromorphic imaging using event cameras offers significant advantages, such as minimal motion blur, enhanced temporal resolution, and high dynamic range.
Hierarchical Banzhaf Interaction for General Video-Language Representation Learning
As an important subfield, video-language representation learning focuses on learning representations using global semantic interactions between pre-defined video-text pairs.
RoomPainter: View-Integrated Diffusion for Consistent Indoor Scene Texturing
The core of RoomPainter features a zero-shot technique that effectively adapts a 2D diffusion model for 3D-consistent texture synthesis, along with a two-stage generation strategy that ensures both global and local consistency.
Next Patch Prediction for Autoregressive Visual Generation
Autoregressive models, built based on the Next Token Prediction (NTP) paradigm, show great potential in developing a unified framework that integrates both language and vision tasks.
Open-Sora Plan: Open-Source Large Video Generation Model
We introduce Open-Sora Plan, an open-source project that aims to contribute a large generation model for generating desired high-resolution videos with long durations based on various user inputs.
Identity-Preserving Text-to-Video Generation by Frequency Decomposition
We propose a hierarchical training strategy to leverage frequency information for identity preservation, transforming a vanilla pre-trained video generation model into an IPT2V model.
WF-VAE: Enhancing Video VAE by Wavelet-Driven Energy Flow for Latent Video Diffusion Model
However, as the resolution and duration of generated videos increase, the encoding cost of Video VAEs becomes a limiting bottleneck in training LVDMs.
Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection
AI-generated images (AIGIs), such as natural or face images, have become increasingly realistic and indistinguishable, making their detection a critical and pressing challenge.
LLaVA-CoT: Let Vision Language Models Reason Step-by-Step
Large language models have demonstrated substantial advancements in reasoning capabilities, particularly through inference-time scaling, as illustrated by models such as OpenAI's o1.
Sparse Orthogonal Parameters Tuning for Continual Learning
We hypothesize that the effectiveness of SoTU lies in the transformation of knowledge learned from multiple domains into the fusion of orthogonal delta parameters.
Efficiently Training Time-to-First-Spike Spiking Neural Networks from Scratch
To address these challenges, we propose a training framework incorporating parameter initialization, training normalization, temporal output decoding, and pooling layer re-evaluation.
Spatial-Temporal Search for Spiking Neural Networks
Drawing inspiration from the heterogeneity of biological neural networks, we propose a differentiable approach to optimize SNN on both spatial and temporal dimensions.
Few-Shot Joint Multimodal Entity-Relation Extraction via Knowledge-Enhanced Cross-modal Prompt Model
This method can effectively address the problem of insufficient information in the few-shot setting by guiding a large language model to generate supplementary background knowledge.
MoH: Multi-Head Attention as Mixture-of-Head Attention
We show that multi-head attention can be expressed in the summation form.
Is Parameter Collision Hindering Continual Learning in LLMs?
Large Language Models (LLMs) often suffer from catastrophic forgetting when learning multiple tasks sequentially, making continual learning (CL) essential for their dynamic deployment.
MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
In this work, we aim to simultaneously enhance the effectiveness and efficiency of Mixture-of-Experts (MoE) methods.
ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis
Our method takes advantage of the powerful generation capabilities of video diffusion model and the coarse 3D clues offered by point-based representation to generate high-quality video frames with precise camera pose control.
OD-VAE: An Omni-dimensional Video Compressor for Improving Latent Video Diffusion Model
With the same reconstruction quality, the more sufficient the VAE's compression for videos is, the more efficient the LVDMs are.
Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning
To reproduce FFD, we then propose a novel Video-level Blending data (VB), where VB is implemented by blending the original image and its warped version frame-by-frame, serving as a hard negative sample to mine more general artifacts.
Cycle3D: High-quality and Consistent Image-to-3D Generation via Generation-Reconstruction Cycle
Recent 3D large reconstruction models typically employ a two-stage process, including first generate multi-view images by a multi-view diffusion model, and then utilize a feed-forward model to reconstruct images to 3D content. However, multi-view diffusion models often produce low-quality and inconsistent images, adversely affecting the quality of the final 3D reconstruction.
HoloDreamer: Holistic 3D Panoramic World Generation from Text Descriptions
3D scene generation is in high demand across various domains, including virtual reality, gaming, and the film industry.
Local Action-Guided Motion Diffusion Model for Text-to-Motion Generation
Specifically, we provide an automated method for reference local action sampling and leverage graph attention networks to assess the guiding weight of each local action in the overall motion synthesis.
ChronoMagic-Bench: A Benchmark for Metamorphic Evaluation of Text-to-Time-lapse Video Generation
We propose a novel text-to-video (T2V) generation benchmark, ChronoMagic-Bench, to evaluate the temporal and metamorphic capabilities of the T2V models (e. g. Sora and Lumiere) in time-lapse video generation.
LOOK-M: Look-Once Optimization in KV Cache for Efficient Multimodal Long-Context Inference
Long-context Multimodal Large Language Models (MLLMs) demand substantial computational resources for inference as the growth of their multimodal Key-Value (KV) cache, in response to increasing input lengths, challenges memory and time efficiency.
Point Tree Transformer for Point Cloud Registration
To overcome these limitations, we propose the Point Tree Transformer (PTT), a novel transformer-based approach for point cloud registration that efficiently extracts comprehensive local and global features while maintaining linear computational complexity.
DF40: Toward Next-Generation Deepfake Detection
In this work, we found the dataset (both train and test) can be the "primary culprit" due to: (1) forgery diversity: Deepfake techniques are commonly referred to as both face forgery and entire image synthesis.
ShareGPT4Video: Improving Video Understanding and Generation with Better Captions
To this end, we meticulously designed a differential video captioning strategy, which is stable, scalable, and efficient for generating captions for videos with arbitrary resolution, aspect ratios, and length.
EvaGaussians: Event Stream Assisted Gaussian Splatting from Blurry Images
Capitalizing on the high temporal resolution and dynamic range offered by the event camera, we leverage the event streams to explicitly model the formation process of motion-blurred images and guide the deblurring reconstruction of 3D-GS.
RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter
Text-Video Retrieval (TVR) aims to align relevant video content with natural language queries.
GraCo: Granularity-Controllable Interactive Segmentation
In this work, we introduce Granularity-Controllable Interactive Segmentation (GraCo), a novel approach that allows precise control of prediction granularity by introducing additional parameters to input.
Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models
Recent advancements in general-purpose or domain-specific multimodal large language models (LLMs) have witnessed remarkable progress for medical decision-making.
UNIAA: A Unified Multi-modal Image Aesthetic Assessment Baseline and Benchmark
To further evaluate the IAA capability of MLLMs, we construct the UNIAA-Bench, which consists of three aesthetic levels: Perception, Description, and Assessment.
MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators
Recent advances in Text-to-Video generation (T2V) have achieved remarkable success in synthesizing high-quality general videos from textual descriptions.
QKFormer: Hierarchical Spiking Transformer using Q-K Attention
ii) We incorporate the hierarchical structure, which significantly benefits the performance of both the brain and artificial neural networks, into spiking transformers to obtain multi-scale spiking representation.
Mora: Enabling Generalist Video Generation via A Multi-Agent Framework
Existing open-source methods struggle to achieve comparable performance, often hindered by ineffective agent collaboration and inadequate training data quality.
Envision3D: One Image to 3D with Anchor Views Interpolation
To address this issue, we propose a novel cascade diffusion framework, which decomposes the challenging dense views generation task into two tractable stages, namely anchor views generation and anchor views interpolation.
A Logical Pattern Memory Pre-trained Model for Entailment Tree Generation
LMPM incorporates an external memory structure to learn and store the latent representations of logical patterns, which aids in generating logically consistent conclusions.
Optimal ANN-SNN Conversion with Group Neurons
For instance, while converting artificial neural networks (ANNs) to SNNs circumvents the need for direct training of SNNs, it encounters issues related to conversion errors and high inference time delays.
LLMBind: A Unified Modality-Task Integration Framework
In the multi-modal domain, the dependence of various models on specific input formats leads to user confusion and hinders progress.
PiCO: Peer Review in LLMs based on the Consistency Optimization
Existing large language models (LLMs) evaluation methods typically focus on testing the performance on some closed-environment and domain-specific benchmarks with human annotations.
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
In this work, we propose a simple yet effective training strategy MoE-Tuning for LVLMs.
Ranked #150 on
Visual Question Answering
on MM-Vet
Spikformer V2: Join the High Accuracy Club on ImageNet with an SNN Ticket
To the best of our knowledge, this is the first time that the SNN achieves 80+% accuracy on ImageNet.
SynSP: Synergy of Smoothness and Precision in Pose Sequences Refinement
To leverage this quality information we propose a motion refinement network termed SynSP to achieve a Synergy of Smoothness and Precision in the sequence refinement tasks.
Machine Mindset: An MBTI Exploration of Large Language Models
We present a novel approach for integrating Myers-Briggs Type Indicator (MBTI) personality traits into large language models (LLMs), addressing the challenges of personality consistency in personalized AI.
Repaint123: Fast and High-quality One Image to 3D Generation with Progressive Controllable 2D Repainting
The core idea is to combine the powerful image generation capability of the 2D diffusion model and the texture alignment ability of the repainting strategy for generating high-quality multi-view images with consistency.
FreestyleRet: Retrieving Images from Style-Diversified Queries
In this paper, we propose the Style-Diversified Query-Based Image Retrieval task, which enables retrieval based on various query styles.
Regressor-Segmenter Mutual Prompt Learning for Crowd Counting
In this study, we propose mutual prompt learning (mPrompt), which leverages a regressor and a segmenter as guidance for each other, solving bias and inaccuracy caused by annotation variance while distinguishing foreground from background.
Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models
Video-based large language models (Video-LLMs) have been recently introduced, targeting both fundamental improvements in perception and comprehension, and a diverse range of user inquiries.
Advancing Vision Transformers with Group-Mix Attention
The attention map is computed based on the mixtures of tokens and group proxies and used to re-combine the tokens and groups in Value.
Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
In this work, we unify visual representation into the language feature space to advance the foundational LLM towards a unified LVLM.
Ranked #9 on
Zero-Shot Video Question Answer
on TGIF-QA
Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding
Large language models have demonstrated impressive universal capabilities across a wide range of open-ended tasks and have extended their utility to encompass multimodal conversations.
Image-based Generative Performance Benchmarking
Language Modeling
+11
Progressive3D: Progressively Local Editing for Text-to-3D Content Creation with Complex Semantic Prompts
Recent text-to-3D generation methods achieve impressive 3D content creation capacity thanks to the advances in image diffusion models and optimizing strategies.
IDRNet: Intervention-Driven Relation Network for Semantic Segmentation
Next, a deletion diagnostics procedure is conducted to model relations of these semantic-level representations via perceiving the network outputs and the extracted relations are utilized to guide the semantic-level representations to interact with each other.
HiFi-123: Towards High-fidelity One Image to 3D Content Generation
Our contributions are twofold: First, we propose a Reference-Guided Novel View Enhancement (RGNV) technique that significantly improves the fidelity of diffusion-based zero-shot novel view synthesis methods.
LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment
We thus propose VIDAL-10M with Video, Infrared, Depth, Audio and their corresponding Language, naming as VIDAL-10M.
Ranked #1 on
Zero-shot Audio Classification
on VGG-Sound
(using extra training data)
LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples
This phenomenon forces us to revisit that \emph{hallucination may be another view of adversarial examples}, and it shares similar characteristics with conventional adversarial examples as a basic property of LLMs.
Adversarial Attacks on Video Object Segmentation with Hard Region Discovery
Particularly, the gradients from the segmentation model are exploited to discover the easily confused region, in which it is difficult to identify the pixel-wise objects from the background in a frame.
Triple-View Knowledge Distillation for Semi-Supervised Semantic Segmentation
To alleviate the expensive human labeling, semi-supervised semantic segmentation employs a few labeled images and an abundant of unlabeled images to predict the pixel-level label map with the same size.
Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation
Unsupervised Video Object Segmentation (VOS) aims at identifying the contours of primary foreground objects in videos without any prior knowledge.
Semantic Segmentation
Unsupervised Video Object Segmentation
+1
Fully Transformer-Equipped Architecture for End-to-End Referring Video Object Segmentation
Referring Video Object Segmentation (RVOS) requires segmenting the object in video referred by a natural language query.
Learning Sparse Neural Networks with Identity Layers
Applying such theory, we propose a plug-and-play CKA-based Sparsity Regularization for sparse network training, dubbed CKA-SR, which utilizes CKA to reduce feature similarity between layers and increase network sparsity.
Spike-driven Transformer
In this paper, we incorporate the spike-driven paradigm into Transformer by the proposed Spike-driven Transformer with four unique properties: 1) Event-driven, no calculation is triggered when the input of Transformer is zero; 2) Binary spike communication, all matrix multiplications associated with the spike matrix can be transformed into sparse additions; 3) Self-attention with linear complexity at both token and channel dimensions; 4) The operations between spike-form Query, Key, and Value are mask and addition.
Chatlaw: A Multi-Agent Collaborative Legal Assistant with Knowledge Graph Enhanced Mixture-of-Experts Large Language Model
AI legal assistants based on Large Language Models (LLMs) can provide accessible legal consulting services, but the hallucination problem poses potential legal risks.
Auto-Spikformer: Spikformer Architecture Search
The integration of self-attention mechanisms into Spiking Neural Networks (SNNs) has garnered considerable interest in the realm of advanced deep learning, primarily due to their biological properties.
ChatFace: Chat-Guided Real Face Editing via Diffusion Latent Space Manipulation
Despite the successful image reconstruction achieved by diffusion-based methods, there are still challenges in effectively manipulating fine-gained facial attributes with textual instructions. To address these issues and facilitate convenient manipulation of real facial images, we propose a novel approach that conduct text-driven image editing in the semantic latent space of diffusion model.
Temporal Contrastive Learning for Spiking Neural Networks
However, in this work, we find the method above is not ideal for the SNNs training as it omits the temporal dynamics of SNNs and degrades the performance quickly with the decrease of inference time steps.
Album Storytelling with Iterative Story-aware Captioning and Large Language Models
One natural approach is to use caption models to describe each photo in the album, and then use LLMs to summarize and rewrite the generated captions into an engaging story.
Text-Video Retrieval with Disentangled Conceptualization and Set-to-Set Alignment
In this paper, we propose the Disentangled Conceptualization and Set-to-set Alignment (DiCoSA) to simulate the conceptualizing and reasoning process of human beings.
PointGPT: Auto-regressively Generative Pre-training from Point Clouds
Large language models (LLMs) based on the generative pre-training transformer (GPT) have demonstrated remarkable effectiveness across a diverse range of downstream tasks.
Ranked #1 on
Few-Shot 3D Point Cloud Classification
on ModelNet40 5-way (10-shot)
(using extra training data)
Img2Vec: A Teacher of High Token-Diversity Helps Masked AutoEncoders
To study which type of deep features is appropriate for MIM as a learning target, we propose a simple MIM framework with serials of well-trained self-supervised models to convert an Image to a feature Vector as the learning target of MIM, where the feature extractor is also known as a teacher model.
Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning
However, in this work, we find that the CE loss is not ideal for the base session training as it suffers poor class separation in terms of representations, which further degrades generalization to novel classes.
Video-Text as Game Players: Hierarchical Banzhaf Interaction for Cross-Modal Representation Learning
Contrastive learning-based video-language representation learning approaches, e. g., CLIP, have achieved outstanding performance, which pursue semantic interaction upon pre-defined video-text pairs.
Ranked #8 on
Video Question Answering
on MSRVTT-QA
Multi-granularity Interaction Simulation for Unsupervised Interactive Segmentation
Interactive segmentation enables users to segment as needed by providing cues of objects, which introduces human-computer interaction for many fields, such as image editing and medical image analysis.
DiffusionRet: Generative Text-Video Retrieval with Diffusion Model
Existing text-video retrieval solutions are, in essence, discriminant models focused on maximizing the conditional likelihood, i. e., p(candidates|query).
Ranked #15 on
Video Retrieval
on MSVD
Parallel Vertex Diffusion for Unified Visual Grounding
An intuitive materialization of our paradigm is Parallel Vertex Diffusion (PVD) to directly set vertex coordinates as the generation target and use a diffusion model to train and infer.
MADAv2: Advanced Multi-Anchor Based Active Domain Adaptation Segmentation
Unsupervised domain adaption has been widely adopted in tasks with scarce annotated data.
Rethinking Point Cloud Registration as Masking and Reconstruction
In this paper, a critical observation is made that the invisible parts of each point cloud can be directly utilized as inherent masks, and the aligned point cloud pair can be regarded as the reconstruction target.
Joint Multimodal Entity-Relation Extraction Based on Edge-enhanced Graph Alignment Network and Word-pair Relation Tagging
This paper is the first to propose jointly performing MNER and MRE as a joint multimodal entity-relation extraction task (JMERE).
Out-of-Candidate Rectification for Weakly Supervised Semantic Segmentation
Weakly supervised semantic segmentation is typically inspired by class activation maps, which serve as pseudo masks with class-discriminative regions highlighted.
Optical Character Recognition (OCR)
Weakly supervised Semantic Segmentation
+1
ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation
Recently, self-supervised large-scale visual pre-training models have shown great promise in representing pixel-level semantic relationships, significantly promoting the development of unsupervised dense prediction tasks, e. g., unsupervised semantic segmentation (USS).
Spikformer: When Spiking Neural Network Meets Transformer
Spikformer (66. 3M parameters) with comparable size to SEW-ResNet-152 (60. 2M, 69. 26%) can achieve 74. 81% top1 accuracy on ImageNet using 4 time steps, which is the state-of-the-art in directly trained SNNs models.
Locality Guidance for Improving Vision Transformers on Tiny Datasets
Therefore, our locality guidance approach is very simple and efficient, and can serve as a basic performance enhancement method for VTs on tiny datasets.
Improving Vision Transformers by Revisiting High-frequency Components
Inspired by this finding, we first investigate the effects of existing techniques for improving ViT models from a new frequency perspective, and find that the success of some techniques (e. g., RandAugment) can be attributed to the better usage of the high-frequency components.
Ranked #2 on
Domain Generalization
on Stylized-ImageNet
Masked Autoencoders for Point Cloud Self-supervised Learning
Then, a standard Transformer based autoencoder, with an asymmetric design and a shifting mask tokens operation, learns high-level latent features from unmasked point patches, aiming to reconstruct the masked point patches.
Ranked #2 on
Point Cloud Segmentation
on PointCloud-C
3D Part Segmentation
Few-Shot 3D Point Cloud Classification
+2
DynaMixer: A Vision MLP Architecture with Dynamic Mixing
In contrast with vision transformers and CNNs, the success of MLP-like models shows that simple information fusion operations among tokens and channels can yield a good representation power for deep recognition models.
Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction
Recent Transformer-based methods have achieved advanced performance in point cloud registration by utilizing advantages of the Transformer in order-invariance and modeling dependency to aggregate information.
PnP-DETR: Towards Efficient Visual Analysis with Transformers
Recently, DETR pioneered the solution of vision tasks with transformers, it directly translates the image feature map into the object detection result.
VOLO: Vision Outlooker for Visual Recognition
Though recently the prevailing vision transformers (ViTs) have shown great potential of self-attention based models in ImageNet classification, their performance is still inferior to that of the latest SOTA CNNs if no extra data are provided.
Ranked #1 on
Image Classification
on VizWiz-Classification
Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition
By realizing the importance of the positional information carried by 2D feature representations, unlike recent MLP-like models that encode the spatial information along the flattened spatial dimensions, Vision Permutator separately encodes the feature representations along the height and width dimensions with linear projections.
Continual Learning via Bit-Level Information Preserving
Continual learning tackles the setting of learning different tasks sequentially.
All Tokens Matter: Token Labeling for Training Better Vision Transformers
In this paper, we present token labeling -- a new training objective for training high-performance vision transformers (ViTs).
Ranked #3 on
Efficient ViTs
on ImageNet-1K (With LV-ViT-S)
Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization
It is well-known that stochastic gradient noise (SGN) acts as implicit regularization for deep learning and is essentially important for both optimization and generalization of deep networks.
Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet
To overcome such limitations, we propose a new Tokens-To-Token Vision Transformer (T2T-ViT), which incorporates 1) a layer-wise Tokens-to-Token (T2T) transformation to progressively structurize the image to tokens by recursively aggregating neighboring Tokens into one Token (Tokens-to-Token), such that local structure represented by surrounding tokens can be modeled and tokens length can be reduced; 2) an efficient backbone with a deep-narrow structure for vision transformer motivated by CNN architecture design after empirical study.
Ranked #437 on
Image Classification
on ImageNet
Graph Attention Network with Memory Fusion for Aspect-level Sentiment Analysis
Recent studies used attention-based methods that can effectively improve the performance of aspect-level sentiment analysis.
Fooling the primate brain with minimal, targeted image manipulation
Here we propose an array of methods for creating minimal, targeted image perturbations that lead to changes in both neuronal activity and perception as reflected in behavior.
Toward Accurate Person-level Action Recognition in Videos of Crowded Scenes
Prior works always fail to deal with this problem in two aspects: (1) lacking utilizing information of the scenes; (2) lacking training data in the crowd and complex scenes.
A Simple Baseline for Pose Tracking in Videos of Crowded Scenes
This paper presents our solution to ACM MM challenge: Large-scale Human-centric Video Analysis in Complex Events\cite{lin2020human}; specifically, here we focus on Track3: Crowd Pose Tracking in Complex Events.
Towards Accurate Human Pose Estimation in Videos of Crowded Scenes
In this paper, we focus on improving human pose estimation in videos of crowded scenes from the perspectives of exploiting temporal context and collecting new data.
Exploring global diverse attention via pairwise temporal relation for video summarization
In this paper, we propose an efficient convolutional neural network architecture for video SUMmarization via Global Diverse Attention called SUM-GDA, which adapts attention mechanism in a global perspective to consider pairwise temporal relations of video frames.
YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion Analysis
In recent years, the growing ubiquity of Internet memes on social media platforms, such as Facebook, Instagram, and Twitter, has become a topic of immense interest.
Revisiting Knowledge Distillation via Label Smoothing Regularization
Without any extra computation cost, Tf-KD achieves up to 0. 65\% improvement on ImageNet over well-established baseline models, which is superior to label smoothing regularization.
Central Similarity Quantization for Efficient Image and Video Retrieval
In this work, we propose a new \emph{global} similarity metric, termed as \emph{central similarity}, with which the hash codes of similar data pairs are encouraged to approach a common center and those for dissimilar pairs to converge to different centers, to improve hash learning efficiency and retrieval accuracy.
Distilling Object Detectors with Fine-grained Feature Imitation
To address the challenge of distilling knowledge in detection model, we propose a fine-grained feature imitation method exploiting the cross-location discrepancy of feature response.
Cycle-SUM: Cycle-consistent Adversarial LSTM Networks for Unsupervised Video Summarization
The evaluator defines a learnable information preserving metric between original video and summary video and "supervises" the selector to identify the most informative frames to form the summary video.
Ranked #7 on
Unsupervised Video Summarization
on TvSum
Few-shot Adaptive Faster R-CNN
To address these challenges, we first introduce a pairing mechanism over source and target features to alleviate the issue of insufficient target domain samples.
The Unconstrained Ear Recognition Challenge 2019 - ArXiv Version With Appendix
The goal of the challenge is to assess the performance of existing ear recognition techniques on a challenging large-scale ear dataset and to analyze performance of the technology from various viewpoints, such as generalization abilities to unseen data characteristics, sensitivity to rotations, occlusions and image resolution and performance bias on sub-groups of subjects, selected based on demographic criteria, i. e. gender and ethnicity.
Object Relation Detection Based on One-shot Learning
Detecting the relations among objects, such as "cat on sofa" and "person ride horse", is a crucial task in image understanding, and beneficial to bridging the semantic gap between images and natural language.
