In this paper, we introduce SearchDet, a training-free long-tail object detection framework that significantly enhances open-vocabulary object detection performance. SearchDet retrieves a set of positive and negative images of an object to ground, embeds these images, and computes an input image–weighted query which is used to detect the desired concept in the image. Our proposed method is simple and training-free, yet achieves over 16.81% mAP improvement on ODinW and 59.85% mAP improvement on LVIS compared to state-of-the-art models such as GroundingDINO. We further show that our approach of basing object detection on a set of Web-retrieved exemplars is stable with respect to variations in the exemplars, suggesting a path towards eliminating costly data annotation and training procedures.1

We present MIRACLE, a system for online, interpretable visual concept and video action recognition. Through a chat interface, users query the recognition system with an uploaded image or video. For images, MIRACLE returns concept predictions from its structured knowledge base, justifying its predictions with heatmaps and natural language-based attribute detections. For videos, MIRACLE predicts an action and justifies its prediction with time varying entity-entity relations. With its ability to learn new concepts in an online, few-shot manner and its support of dynamic changes to its knowledge base, MIRACLE represents a step forward in interpretable multimodal learning systems.

Chatbots are often hindered by the latency associated with integrating real-time web search results, compromising user experience. To overcome this, we present CharmBana, an innovative social chatbot that introduces the use of progressive response generation to effortlessly blend search results into the bot's responses, while ensuring low response latency. The use of progressive responses is especially beneficial for voice-based chatbots, where the preliminary response buys time for a detailed follow-up, ensuring a smooth user interaction. As a result, our method not only cuts down user waiting times by 50% but also generates more relevant, precise, and engaging search inquiries. When tested in the Alexa Prize Socialbot Grand Challenge 5, our chatbot employing progressive responses consistently received higher user ratings.