Robust Relevance Feedback for Interactive Known-Item Video Search

Known-item search (KIS) involves only a single search target, making relevance feedback-typically a powerful technique for efficiently identifying multiple positive examples to infer user intent-inapplicable. PicHunter addresses this issue by asking users to select the top-k most similar examples to the unique search target from a displayed set. Under ideal conditions, when the user's perception aligns closely with the machine's perception of similarity, consistent and precise judgments can elevate the target to the top position within a few iterations. However, in practical scenarios, expecting users to provide consistent judgments is often unrealistic, especially when the underlying embedding features used for similarity measurements lack interpretability. To enhance robustness, we first introduce a pairwise relative judgment feedback that improves the stability of top-k selections by mitigating the impact of misaligned feedback. Then, we decompose user perception into multiple sub-perceptions, each represented as an independent embedding space. This approach assumes that users may not consistently align with a single representation but are more likely to align with one or several among multiple representations. We develop a predictive user model that estimates the combination of sub-perceptions based on each user feedback instance. The predictive user model is then trained to filter out the misaligned sub-perceptions. Experimental evaluations on the large-scale open-domain dataset V3C indicate that the proposed model can optimize over 60% search targets to the top rank when their initial ranks at the search depth between 10 and 50. Even for targets initially ranked between 1,000 and 5,000, the model achieves a success rate exceeding 40% in optimizing ranks to the top, demonstrating the enhanced robustness of relevance feedback in KIS despite inconsistent feedback.

Interactive Video Corpus Moment Retrieval using Reinforcement Learning

Known-item video search is effective with human-in-the-loop to interactively investigate the search result and refine the initial query. Nevertheless, when the first few pages of results are swamped with visually similar items, or the search target is hidden deep in the ranked list, finding the know-item target usually requires a long duration of browsing and result inspection. This paper tackles the problem by reinforcement learning, aiming to reach a search target within a few rounds of interaction by long-term learning from user feedbacks. Specifically, the system interactively plans for navigation path based on feedback and recommends a potential target that maximizes the long-term reward for user comment. We conduct experiments for the challenging task of video corpus moment retrieval (VCMR) to localize moments from a large video corpus. The experimental results on TVR and DiDeMo datasets verify that our proposed work is effective in retrieving the moments that are hidden deep inside the ranked lists of CONQUER and HERO, which are the state-of-the-art auto-search engines for VCMR.