Search Results for author:
Found 24 papers, 14 papers with code
CLIP-EBC: CLIP Can Count Accurately through Enhanced Blockwise Classification
The CLIP (Contrastive Language-Image Pretraining) model has exhibited outstanding performance in recognition problems, such as zero-shot image classification and object detection.
Ranked #1 on
Crowd Counting
on UCF-QNRF
Explainable Depression Detection via Head Motion Patterns
While depression has been studied via multimodal non-verbal behavioural cues, head motion behaviour has not received much attention as a biomarker.
Robust Multiview Multimodal Driver Monitoring System Using Masked Multi-Head Self-Attention
Driver Monitoring Systems (DMSs) are crucial for safe hand-over actions in Level-2+ self-driving vehicles.
Heterogeneous Graph Learning for Acoustic Event Classification
Heterogeneous graphs provide a compact, efficient, and scalable way to model data involving multiple disparate modalities.
Explainable Human-centered Traits from Head Motion and Facial Expression Dynamics
We explore the efficacy of multimodal behavioral cues for explainable prediction of personality and interview-specific traits.
Real-Time Driver Monitoring Systems through Modality and View Analysis
Driver distractions are known to be the dominant cause of road accidents.
Visually-aware Acoustic Event Detection using Heterogeneous Graphs
In contrast, we employ heterogeneous graphs to explicitly capture the spatial and temporal relationships between the modalities and represent detailed information about the underlying signal.
Learning Long-Term Spatial-Temporal Graphs for Active Speaker Detection
Active speaker detection (ASD) in videos with multiple speakers is a challenging task as it requires learning effective audiovisual features and spatial-temporal correlations over long temporal windows.
Ranked #1 on
Node Classification
on AVA
FusionCount: Efficient Crowd Counting via Multiscale Feature Fusion
Then, to account for perspective distortion, the highest-level feature map is fed to extra components to extract multiscale features, which are the input to the decoder to generate crowd densities.
Ranked #8 on
Crowd Counting
on ShanghaiTech B
Self-supervised Graphs for Audio Representation Learning with Limited Labeled Data
Large scale databases with high-quality manual annotations are scarce in audio domain.
Head Matters: Explainable Human-centered Trait Prediction from Head Motion Dynamics
We demonstrate the utility of elementary head-motion units termed kinemes for behavioral analytics to predict personality and interview traits.
Learning Spatial-Temporal Graphs for Active Speaker Detection
We address the problem of active speaker detection through a new framework, called SPELL, that learns long-range multimodal graphs to encode the inter-modal relationship between audio and visual data.
Multi-Camera Trajectory Forecasting with Trajectory Tensors
We introduce the problem of multi-camera trajectory forecasting (MCTF), which involves predicting the trajectory of a moving object across a network of cameras.
In Defense of Scene Graphs for Image Captioning
Several studies have noted that the naive use of scene graphs from a black-box scene graph generator harms image captioning performance and that scene graph-based captioning models have to incur the overhead of explicit use of image features to generate decent captions.
Dynamic Character Graph via Online Face Clustering for Movie Analysis
An effective approach to automated movie content analysis involves building a network (graph) of its characters.
Ensemble Network for Ranking Images Based on Visual Appeal
The ranking is expected to correspond with human perception of overall appeal of the images.
Multi-Camera Trajectory Forecasting: Pedestrian Trajectory Prediction in a Network of Cameras
To facilitate research in this new area, we release the Warwick-NTU Multi-camera Forecasting Database (WNMF), a unique dataset of multi-camera pedestrian trajectories from a network of 15 synchronized cameras.
Coordinated Joint Multimodal Embeddings for Generalized Audio-Visual Zeroshot Classification and Retrieval of Videos
We present an audio-visual multimodal approach for the task of zeroshot learning (ZSL) for classification and retrieval of videos.
Ranked #5 on
GZSL Video Classification
on VGGSound-GZSL(main)
Multiple Object Forecasting: Predicting Future Object Locations in Diverse Environments
In contrast to existing works on object trajectory forecasting which primarily consider the problem from a birds-eye perspective, we formulate the problem from an object-level perspective and call for the prediction of full object bounding boxes, rather than trajectories alone.
Ranked #1 on
Multiple Object Forecasting
on Citywalks
Learning Affective Correspondence between Music and Image
We introduce the problem of learning affective correspondence between audio (music) and visual data (images).
AN ONLINE ALGORITHM FOR CONSTRAINED FACE CLUSTERING IN VIDEOS
We address the problem of face clustering in long, real world videos. This is a challenging task because faces in such videos exhibit wid evariability in scale, pose, illumination, expressions, and may also be partially occluded.
Learning Spontaneity to Improve Emotion Recognition In Speech
We investigate the effect and usefulness of spontaneity (i. e. whether a given speech is spontaneous or not) in speech in the context of emotion recognition.
Sparse Representation-based Image Quality Assessment
This sparse strategy is employed because it is known to generate basis vectors that are qualitatively similar to the receptive field of the simple cells present in the mammalian primary visual cortex.
Image Similarity Using Sparse Representation and Compression Distance
This paper proposes a sparse representation-based approach to encode the information content of an image using information from the other image, and uses the compactness (sparsity) of the representation as a measure of its compressibility (how much can the image be compressed) with respect to the other image.
