What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of study are Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Activity recognition.
His study involves Feature extraction, Robustness, Feature, Image resolution and Hidden Markov model, a branch of Artificial intelligence.
His work deals with themes such as Real-time computing and State, which intersect with Computer vision.
His work on Training set as part of his general Pattern recognition study is frequently connected to Shape dynamics, thereby bridging the divide between different branches of science.
His study in the field of Stability also crosses realms of Class.
The Activity recognition study combines topics in areas such as Data mining and Image warping.
His most cited work include:
- Identification of humans using gait (481 citations)
- A large-scale benchmark dataset for event recognition in surveillance video (463 citations)
- Learning Temporal Regularity in Video Sequences (315 citations)
What are the main themes of his work throughout his whole career to date?
Amit K. Roy-Chowdhury mainly investigates Artificial intelligence, Computer vision, Machine learning, Pattern recognition and Feature extraction.
His studies in Activity recognition, Feature, Tracking, Representation and Benchmark are all subfields of Artificial intelligence research.
His study on Computer vision is mostly dedicated to connecting different topics, such as Robustness.
His studies in Machine learning integrate themes in fields like Embedding and Training set.
Amit K. Roy-Chowdhury works mostly in the field of Embedding, limiting it down to concerns involving Automatic summarization and, occasionally, Data mining.
His research brings together the fields of Pixel and Pattern recognition.
He most often published in these fields:
- Artificial intelligence (81.39%)
- Computer vision (41.61%)
- Machine learning (24.09%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (81.39%)
- Machine learning (24.09%)
- Pattern recognition (18.98%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Machine learning, Pattern recognition, Discriminative model and Benchmark are his primary areas of study.
His Artificial intelligence study combines topics in areas such as Computer vision and Natural language processing.
The study incorporates disciplines such as Frame, Architecture and Task in addition to Computer vision.
Many of his research projects under Machine learning are closely connected to Process and Active learning with Process and Active learning, tying the diverse disciplines of science together.
His Pattern recognition research includes themes of Pixel, Co-occurrence, Boosting, Resampling and Image forgery.
In his study, which falls under the umbrella issue of Discriminative model, Feature extraction, Noise reduction, Transformation geometry and Contextual image classification is strongly linked to Visualization.
Between 2017 and 2021, his most popular works were:
- W-TALC: Weakly-Supervised Temporal Activity Localization and Classification (103 citations)
- Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieval (82 citations)
- Hybrid LSTM and Encoder–Decoder Architecture for Detection of Image Forgeries (49 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Amit K. Roy-Chowdhury mainly investigates Artificial intelligence, Machine learning, Pattern recognition, Embedding and Benchmark.
Amit K. Roy-Chowdhury interconnects Computer vision and Natural language processing in the investigation of issues within Artificial intelligence.
His Machine learning study integrates concerns from other disciplines, such as Exploit and Emotion recognition.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Feature, Pixel, Co-occurrence, Resampling and Image forgery.
His work investigates the relationship between Embedding and topics such as Text retrieval that intersect with problems in Leverage and Supervised learning.
The various areas that Amit K. Roy-Chowdhury examines in his Benchmark study include Artificial neural network and Representation.
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