Event
Ph.D. Dissertation Defense: Chinmaya Devaraj
Tuesday, September 17, 2024
3:00 p.m.
Iribe 4105
Maria Hoo
301 405 3681
mch@umd.edu
ANNOUNCEMENT: Ph.D. Dissertation Defense
Name: Chinmaya Devaraj
Committee:
Prof. Yiannis Aloimonos (Chair):
Dr Cornelia Fermuller
Prof. Behtash Babadi
Prof Dinesh Manocha
Prof Nikhil Chopra (Dean's representative)
Date/time: Tuesday, September 17, 2024 at 3-5 p.m.
Location: Iribe 4105
Title: FROM PARTS TO WHOLE IN ACTION AND OBJECT UNDERSTANDING
Abstract:
The traditional paradigm of supervised learning in action or object recognition often relies on a top-down approach, ignoring explicit modeling of what activity or objects consist of. Recent approaches in generative AI research have shown us the ability to generate images and videos using text, indirectly indicating that we have control over the constituents of images and videos. In this dissertation, we explore ways to use the constituents of actions to develop methods to improve understanding of action. We devise different approaches to utilize the parts of actions, namely object motion, object state changes, and motion descriptions obtained by LLMs in various tasks like in the next active object segmentation, zero-shot action recognition, or video-text retrieval. We show promising benefits in action anticipation, zero-shot action recognition, and text-video retrieval tasks, demonstrating the practical applications of our methods.
In the first part of the dissertation, we explore the idea of using the constituents of actions in GCNs for zero-shot human-object action recognition. The main idea is that semantically similar actions (of similar constituents) are closer in feature space. Thus, in our graph, we encode the edges connecting those actions with higher similarity. We introduce a method to visually ground the external knowledge graph using the concept of shared similarity between similar actions. We evaluate the method on the EPIC Kitchens dataset and the Charades dataset showing impressive results over baseline methods. We further show that visually grounding the knowledge graph enhances the performance of GCNs when an adversarial attack corrupts the input graph.
In the second part of the thesis, we extend our ideas on human-object interactions in first-person videos. Human actions involving hand manipulations are structured according to the making and breaking of hand-object contact, and human visual understanding of action relies on anticipation of contact, as demonstrated by pioneering work in cognitive science. Taking inspiration from this, we introduce representations and models centered on contact, which we then use in action prediction and anticipation. We train the Anticipation Module, a module producing Contact Anticipation Maps and Next Active Object Segmentations - novel low-level representations providing temporal and spatial characteristics of anticipated near future action. On top of the Anticipation Module, we apply Egocentric Object Manipulation Graphs
(Ego-OMG), a framework for action anticipation and prediction. Using the Anticipation Module to aid Ego-OMG produces state-of-the-art results, achieving first and second places on the unseen and seen test sets of the EPIC Kitchens Action Anticipation Challenge and achieving state-of-the-art results on action anticipation and action prediction over EPIC Kitchens.
In third part of dissertation, we extend our ideas on human-object interactions in first person videos. Human actions involving hand manipulations are structured according to the making and breaking of hand-object contact, and human visual understanding of action is reliant on anticipation of contact as is demonstrated by pioneering work in cognitive science. Taking inspiration from this, we introduce representations and models centered on contact, which we then use in action prediction and anticipation. We annotate a subset of the EPIC Kitchens dataset to include time-to-contact between hands and objects, as well as segmentations of hands and objects. Using these annotations we train the Anticipation Module, a module producing Contact Anticipation Maps and Next Active Object Segmentations - novel low-level representations providing temporal and spatial characteristics of anticipated near future action. On top of the Anticipation Module we apply Egocentric Object Manipulation Graphs
(Ego-OMG), a framework for action anticipation and prediction. Ego-OMG models longer term temporal semantic relations through the use of a graph modeling transitions between contact delineated action states. Use of the Anticipation Module within Ego-OMGproduces state-of-the-art results, achieving 1st and 2 place on the unseen and seen test sets, respectively, of the EPIC Kitchens Action Anticipation Challenge, and achieving state-of-the-art results on the tasks of action anticipation and action prediction over EPIC Kitchens. We perform ablation studies over characteristics of the Anticipation Module to evaluate their utility.
In the same line of thinking of constituents of action, we next focus on investigating how motion understanding can be modeled in current video-text models. We introduce motion descriptions generated by GPT4 on three action datasets that capture fine-grained motion descriptions of activities. We evaluated several video-text models on the task of retrieval of motion descriptions and found them to need to catch up to the human expert performance. We introduce a method of improving motion understanding in video-text models by utilizing motion descriptions. This method is demonstrated on two action datasets for the motion description retrieval task. The results draw attention to the need for quality captions involving fine-grained motion information in existing datasets and demonstrate the effectiveness of the proposed pipeline in understanding fine-grained motion during video-text retrieval.
